Explorez tous les épisodes du podcast AWS for Software Companies Podcast

CyberArk's technology leader discusses their strategy for securing against AI threats, protecting agentic AI systems, and their vision for the future in an increasingly AI-driven cybersecurity landscape.

Topics Include:

• CyberArk celebrates recent exciting news while discussing their incredible cybersecurity journey
• Founded in 1999, CyberArk pioneered privilege access management and expanded into comprehensive identity security
• Company executed textbook SaaS transformation from perpetual licensing to subscription-based cloud model
• Leadership set clear customer expectations, framing SaaS shift as faster innovation delivery
• Addressed customer concerns about cost predictability, security compliance, and data residency requirements
• Technical team implemented lift-and-shift architecture with AWS RDS and multi-tenant improvements
• Corporate initiative tracked weekly metrics and milestones throughout full development lifecycle process
• Customer Success evolved from transactional support to strategic partnership embedded in security journeys
• AWS partnership fundamental to cloud journey with 25+ integrations and Marketplace collaboration
• AI strategy focuses on three pillars: using AI, securing against AI threats
• Future 12-24 months: continue securing all identities while expanding AI capabilities and solutions
• AWS partnership expanding in 2025 leveraging machine identity leadership and GenAI advances

Participants:

• Peretz Regev – Chief Product & Technology Officer, CyberArk
• Boaz Ziniman – Principal Developer Advocate - EMEA, Amazon Web Services

Further Links:

·        CyberArk: Website – LinkedIn – AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Three leading ISV executives from Coveo, DTEX Systems and Honeycomb, reveal how companies with proprietary datasets are gaining unbeatable competitive advantages in the AI era and share real-world strategies how you have similar outcomes.

Topics Include:

• Panel introduces three ISV leaders discussing data platform transformation for AI
• DTEX focuses on insider threats, Coveo on enterprise search, Honeycomb on observability
• Companies with proprietary datasets gain strongest competitive advantage in AI transformation
• Data gravity concept: LLMs learning from unique datasets create defensible business positions
• Coveo maintains unified enterprise index with real-time content and access rights sync
• Honeycomb enables subsecond queries for analyzing logs, traces, and metrics at scale
• Multi-tenant architectures balance shared infrastructure benefits with single-tenant data separation
• Coveo deployed 140,000 times last year using mostly multi-tenant, some single-tenant components
• DTEX scaled from thousands to hundreds of thousands endpoints after architectural transformation
• Capital One partnership taught DTEX how to break monolithic architecture into services
• Apache Iceberg and open table formats enable interoperability without data duplication
• Honeycomb built custom format following similar patterns with hot/cold storage tiers
• Business data catalogs become critical for AI agents understanding dataset context
• MCP servers allow AI systems to leverage structured cybersecurity datasets effectively
• DTEX used Cursor with their data to identify North Korean threat actors
• Real-time AI data needs balanced with costs using right models for jobs
• Caching strategies and precise context reduce expensive LLM inference calls unnecessarily
• Search remains essential for enterprise AI to prevent hallucination and access information
• ROI measurement focuses on cost reduction, analyst efficiency, and measurable business outcomes
• Key takeaway: invest in data structure early, context is king, AI is just software

Participants:

• Sebastien Paquet - Vice President of AI Strategy, Coveo
• Rajan Koo - CTO, DTEX Systems
• Patrick King - Head of Data, Honeycomb.io
• KP Bhat - Sr Solutions Architecture Leader- Analytics & AI, Amazon Web Services

Further Links:

• Coveo: Website – LinkedIn – AWS Marketplace
• DTEX Systems: Website – LinkedIn – AWS Marketplace
• Honeycomb.io: Website – LinkedIn – AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Okta's CTO Bhawna Singh discusses AI adoption, innovation and the four critical identity patterns needed to build the trust that accelerates AI implementation.

Topics Include:

• AI innovation races ahead while adoption lags due to trust and security concerns
• Research shows 82% plan AI deployment but 61% of customers demand trust first
• AI coding tools dramatically reduce development time, accelerating software delivery cycles
• AI interaction evolved from ChatGPT conversations to autonomous headless agents working independently
• Future envisions millions of agents making decisions and communicating without human oversight
• Complex data relationships emerge as agents access multiple dynamic sources simultaneously
• Trust fundamentally starts with identity - the foundation for all AI security
• Four critical identity patterns needed: authentication, API security, user confirmation, and authorization
• Authentication ensures legitimate agents while token vaults enable secure agent-to-agent communication
• Asynchronous user approval prevents rogue decisions like the recent database deletion incident
• Industry standards like MCP protocol establish minimum security guardrails for interoperability
• Trust accelerates AI adoption through security, accountability, and collaborative standard-building efforts

Participants:

• Bhawna Singh – CTO, Customer Identity, Okta

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

A panel discussion with AI industry leaders revealing how enterprises are scaling AI today, with predictions on coming breakthroughs for AI and the impact on Fortune 500 companies and beyond.

Topics Include:

• Three technical leaders discuss production challenges: security, interoperability, and scaling agentic systems
• Panelists represent Enkrypt (security), Anyscale (infrastructure), and CrewAI (agent orchestration platforms)
• Industry moving from flashy demos to dependable agents with real business outcomes
• Breakthrough examples include 70-page IRS form processing and multimodal workflow automation
• Multimodal data integration becoming crucial - incorporating video, audio, screenshots into decisions
• Less than 10% of future applications expected to be text-only
• Companies shifting from experimenting with individual models to deploying agent networks
• Need for governance frameworks as enterprises scale to hundreds of agents
• Growing software stack complexity requires specialized infrastructure between applications and GPUs
• Security teams need centralized visibility across fragmented agent deployments across enterprises
• Existing industry regulations apply to AI services - no special AI laws needed
• Interoperability standards debate: MCP gaining adoption while A2A seems premature solution
• MCP shows higher API reliability than OpenAI tool calling for implementations
• Multimodal systems more vulnerable to attacks but value proposition too high ignore
• Fortune 500 company automated price operations approval process using 630 brands data
• 87% of enterprise customers deploy agents in private VPCs or on-premises infrastructure
• Specialized AI systems needed to oversee other agents at machine speed scales
• Cost optimization through model specialization rather than always using most powerful models
• Future learning may happen through context/prompting rather than traditional weight fine-tuning
• Predictions include AI meeting moderators and agents working autonomously for hours

Participants:

• Robert Nishihara - Co-founder, Anyscale
• João Moura - CEO, CrewAI
• Sahil Agarwal - Co-Founder & CEO, Enkrypt AI
• Jillian D’Arcy - Sr. ISV Sales Leader, Amazon Web Services

Further Links:

• Anyscale – Website  | LinkedIn  | AWS Marketplace
• CrewAI - Website  | LinkedIn  | AWS Marketplace
• Enkrypt AI - Website  | LinkedIn  | AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

AI executives from Archer, Demandbase and Highspot and AWS reveal how they're tackling AI's biggest challenges—from securing data, managing regulatory changes and keeping humans in the loop.

Topics Include:

• Three AI leaders introduce their companies: Archer, Demandbase and Highspot's approaches to enterprise AI
• Demandbase's data strategy: Customer data stays isolated, shared data requires consent, public sources fuel training
• Geographic complexity: AI compliance varies dramatically between Germany, US, Canada, and California regulations
• HighSpot tackles sales bias: Granular questions replace generic assessments for more accurate rep evaluations
• SBI framework applied to AI: Specific behavioral observations create better, more actionable sales coaching
• AI transparency through citations: Timestamped evidence lets managers verify AI feedback and catch hallucinations
• Archer handles 20-30K monthly regulations: AI helps enterprises manage overwhelming compliance requirements at scale
• Two compliance types explained: Operational (common across companies) versus business-specific regulatory requirements
• EU AI Act adoption: US companies embracing European framework for responsible AI governance
• Human oversight becomes mandatory: Expert-in-the-loop reviews ensure AI decisions remain correctable and auditable
• The bigger AI risk: Companies face greater danger from AI inaction than AI adoption
• Agentic AI security challenges: Data layers must enforce permissions before AI access, not after
• AI agents need identity management: Same access controls apply whether human clicks or AI acts
• Human oversight in high stakes: Chief compliance officers demand transparency and correction capabilities
• Future challenge identified: 80% of enterprise data behind firewalls remains invisible to AI models

Participants:

• Kayvan Alikhani - Global Head of Engineering- Emerging Solutions, Archer Integrated Risk Management
• Umberto Milletti - Chief R&D Officer, Demandbase
• Oliver Sharp - Co-Founder & Chief AI Officer, Highspot
• Brian Shadpour - General Manager, Security, Amazon Web Services

Further Links:

• Archer Integrated Risk Management: Website – LinkedIn – AWS Marketplace
• Demandbase: Website – LinkedIn – AWS Marketplace
• Highspot: Website – LinkedIn – AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

In a fascinating discussion, Rob McGrorty, Product Leader of Agents at Amazon AGI Lab, reveals how rapidly AI agents are evolving with corporate adoption exploding as companies race to deploy production agents and the challenges and advantages they’re experiencing.

Topics Include:

• GenAI adoption outpaces all previous tech waves, growing faster than computers or internet
• Early adopters tackle complex tasks while newcomers still use basic text manipulation features
• AI models double their single-call task capabilities every seven months, exponentially increasing power
• Accelerating progress makes yesterday's magic mundane, unlocking mass creativity and customer demand
• Agents represent natural evolution: chatbots answered questions, now agents autonomously accomplish tasks
• Amazon's browser agent finds apartments, maps distances, ranks options using multiple transit modes
• Corporate adoption exploded: 33% piloting agents in 2024, 67% moving to production now
• Two main agent types today: API calling with tool use, browser automation
• Current applications mirror "RPA 2.0" - form filling, data extraction, website QA testing
• Future brings multi-agent systems, self-directing loops, and agent-to-agent negotiation scenarios
• Major challenges: data privacy, oversight protocols, error responsibility, and ecosystem sustainability
• Technical hurdles include real-time accuracy measurement, latency issues, and quality assurance frameworks

Participants:

• Rob McGrorty – Product Leader, Agents at Amazon AGI Lab

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Gagan Singh of Elastic discuses how agentic AI systems reduce analyst burnout by automatically triaging security alerts, resulting in measurable ROI for organizations

Topics Include:

• AI breaks security silos between teams, data, and tools in SOCs
• Attackers gain system access; SOC teams have only 40 minutes to detect/contain
• Alert overload causes analyst burnout; thousands of low-value alerts overwhelm teams daily
• AI inevitable for SOCs to process data, separate false positives from real threats
• Agentic systems understand environment, reason through problems, take action without hand-holding
• Attack discovery capability reduces hundreds of alerts to 3-4 prioritized threat discoveries
• AI provides ROI metrics: processed alerts, filtered noise, hours saved for organizations
• RAG (Retrieval Augmented Generation) prevents hallucination by adding enterprise context to LLMs
• AWS integration uses SageMaker, Bedrock, Anthropic models with Elasticsearch vector database capabilities
• End-to-end LLM observability tracks costs, tokens, invocations, errors, and performance bottlenecks
• Junior analysts detect nation-state attacks; teams shift from reactive to proactive security
• Future requires balancing costs, data richness, sovereignty, model choice, human-machine collaboration

Participants:

• Gagan Singh – Vice President Product Marketing, Elastic

Additional Links:

• Elastic – LinkedIn - Website – AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Pete Rubio reveals how Rapid7 transformed to an AI-first platform that automates security investigations and accelerates results from hours to seconds.

Topics Include:

• Pete Rubio introduces Rapid7's journey to becoming an AI-first cybersecurity platform
• Cybersecurity teams overwhelmed by growing attack surfaces and constant alert fatigue
• Customers needed faster response times, not just more alerts coming faster
• Legacy tools created silos requiring manual triage that doesn't scale effectively
• AI must turn raw security data into real-time decisions humans can trust
• Unified data platform correlates infrastructure, applications, identity, and business context together
• Agentic AI automates investigative work, reducing analyst tasks from hours to seconds
• Rapid7 evaluated multiple vendors, choosing AWS for performance, cost, and flexibility
• Nova models delivered unmatched performance for global scaling at controlled costs
• Bedrock provided secure model deployment with governance and data privacy boundaries
• AWS partnership enabled co-development and rapid iteration beyond typical vendor relationships
• Transparent AI shows customers how models reach conclusions before automated actions
• SOC analyst expertise continuously trains models with real-time security intelligence
• Governance frameworks and guardrails implemented from day one, not retrofitted later
• Future plans include customer AI integration and bring-your-own-model capabilities

Participants:

• Pete Rubio – Senior Vice President, Platform & Engineering, Rapid7

Additional Links:

• Rapid 7 – LinkedIn - Website – AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Panther CEO William Lowe explains how integrating Amazon Bedrock AI into their security platform delivered 50% faster alert resolution for enterprise customers while maintaining the trust and control that security practitioners demand.

Topics Include:

• Panther CEO explains how Amazon partnership accelerates security outcomes for customers
• Cloud-native security platform delivers 100% visibility across enterprise environments at scale
• Customers like Dropbox and Coinbase successfully replaced Splunk with Panther's solution
• Platform processes petabytes monthly with impressive 2.3-minute average threat detection time
• Critical gap identified: alert resolution still takes 8 hours despite fast detection
• Security teams overwhelmed by growing attack surfaces and severe talent burnout
• Constant context switching across tools creates inefficiency and organizational collaboration problems
• AI integration with Amazon Bedrock designed to accelerate security team decision-making
• Four trust principles: verifiable actions, secure design, human control, customer data ownership
• Results show 50% faster alert triage; future includes Slack integration and automation

Participants:

·        William H Lowe – CEO, Panther

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

AWS executives reveal how generative AI is fundamentally reshaping ISV business models, from pricing strategies to go-to-market approaches, and provide actionable insights for software companies navigating this transformation.

Topics Include:

• Alayna Broaderson and Andy Perkins introduce AWS Infrastructure Partnerships and ISV Sales
• Generative AI profoundly changing how ISVs build, deliver and market software products
• Two ISV categories emerging: established SaaS companies versus pure gen AI startups
• Legacy SaaS firms struggle with infrastructure modernization and potential revenue cannibalization
• Pure gen AI companies face scaling challenges, reliability issues and cost optimization
• Revenue models shifting from subscription-based to consumption-based pricing per token/prompt/task
• Future-proofing architecture critical as technology evolves rapidly like F-35 fighter jets
• Data becoming key differentiator, especially domain-specific datasets in healthcare and legal
• Balancing cost, accuracy, latency and customer experience creates complex optimization challenges
• Multiple specialized models replacing single solutions, with agentic AI accelerating this trend
• Human capital challenges include retraining engineering teams and finding expensive AI talent
• Security, compliance and explainability now mandatory - no more black box solutions
• Enterprise customers struggle with data organization and quantifying clear gen AI ROI
• ISV pricing models evolving with tiered structures and targeted vertical use cases
• Traditional SaaS playbooks failing in generative AI landscape due to ROI uncertainty
• POC-based go-to-market with free trials and case study selling proving most effective
• Pricing strategies include AI gates, credit systems and separate SKUs for services
• Customer trust requires proactive security messaging and auditable, transparent AI solutions
• Modular architecture enables evolution as new technologies emerge in fast-changing market
• AWS positioning as ultimate gen AI toolkit partner with ISV collaboration opportunities

Participants:

• Alayna Broaderson - Sr Manager, Infrastructure Technology Partnership, Amazon Web Services
• Andy Perkins - General Manager, US ISV, Amazon Web Services

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Hear how PagerDuty and Zoom built successful AI products using Amazon Q-Index to solve real customer problems like incident response and meeting intelligence, while sharing practical lessons from their early adoption journey.

Topics Include:

• David Gordon introduces AWS Q-Business partnerships with PagerDuty and Zoom
• Meet Everaldo Aguiar: PagerDuty's Applied AI leader with academia and enterprise background
• Paul Magnaghi from Zoom brings AI platform scaling experience from Seattle
• Q-Business launched over a year ago as managed generative AI service
• Platform enables agentic experiences: content discovery, analysis, and process automation
• Built on AWS Bedrock with enterprise guardrails and data source integration
• Partners wanted backend capabilities but preferred their own UI and models
• Q-Index provides vector database functionality for ISV partner integrations
• Everaldo explains PagerDuty's evolution from traditional ML to generative AI solutions
• Historical challenges: alert fatigue, noise reduction using machine learning approaches
• New gen AI opportunities: incident context, relevant data surfacing, automated postmortems
• Engineering teams faced learning curve with agents and high-latency user experiences
• Paul discusses Zoom's existing AI: virtual backgrounds and voice isolation technology
• AI Companion strategy focused on simplicity during complex generative AI adoption
• Problem identified: valuable meeting conversations disappear after Zoom calls end
• Customer feedback revealed need for enterprise data integration beyond basic summaries
• Goal: combine unstructured conversations with structured enterprise data seamlessly
• PagerDuty Advanced provides agentic AI for on-call engineers during incidents
• Q-Index integration accesses internal documentation: Confluence pages, runbooks, procedures
• Demo shows Slack integration pulling relevant incident response documentation automatically
• Access control lists ensure users see only data they're authorized to access
• Zoom's AI companion panel enables real-time meeting questions and summaries
• Example use cases: decision tracking, incident analysis, action item identification
• Advice for starting: standardize practices and create internal development templates
• Single data access point reduces legal and security evaluation overhead
• Center of excellence approach helps teams move quickly across product divisions
• Cut through generative AI buzzwords to focus on real user value
• Federated AWS Bedrock architecture provides model choice and flexibility meeting customers
• Customer trust alignment between Zoom conversations and AWS data handling
• Getting started: PagerDuty Advance available now, Zoom AI free with paid add-ons

Participants:

• Everaldo Aguiar – Senior Engineering Manager, Applied AI, PagerDuty
• Paul Magnaghi – Head of AI & ISV Go To Market, Zoom
• David Gordon - Global Business Development, Amazon Q for Business. Amazon Web Services

Further Links:

• PagerDuty Website, LinkedIn & AWS Marketplace
• Zoom Website, LinkedIn & AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Teleport Co-Founder and CEO Ev Kontsevoy discusses the security vs productivity trade-off that plagues growing companies and how Teleport's trusted computing model protects against the exponential growth of cybersecurity threats.

Topics Include:

• Teleport CEO explains how to make infrastructure "nearly unhackable" through trusted computing
• Traditional security vs productivity trade-off: high security kills team efficiency
• Companies buy every security solution but still get told they're at risk
• Why "crown jewels" thinking fails: computers should protect everything at scale
• Modern infrastructure has too many access paths to enumerate and secure
• Apple's PCC specification shows trusted computing working in real production environments
• AI revolutionizes both offensive and defensive cybersecurity capabilities for everyone
• 80% of companies can't guarantee they've removed all ex-employee access
• Identity fragmentation across systems creates anonymous relationships and security gaps
• Human error probability grows exponentially as companies scale in three dimensions
• Your laptop already demonstrates trusted computing: seamless access without constant logins
• Apple ecosystem shows device trust at scale through secure enclaves
• AI agents need trusted identities just like humans and machines
• AWS marketplace partnership accelerates deals and provides strategic account insights
• Hire someone who understands partnership dynamics before starting with AWS
• Generative AI will make identity attacks cheaper and faster than ever
• Security responsibility shifting from IT teams to platform engineering teams
• Teleport's "steady state invariant": infrastructure locked down except during authorized work
• Temporary access granted through tickets, then automatically revoked after completion
• Legacy systems and IoT devices require extending trust models beyond cloud-native

Participants:

• Ev Kontsevoy – Co-Founder and CEO, Teleport

Further Links:

• Teleport Website
• Teleport AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Raj Koo, CTO of DTEX Systems, discusses how their enterprise-grade generative AI platform detects and disarms insider threats and enables them to stay ahead of evolving risks.

Topics Include:

• Raj Koo, CTO of DTEX Systems, joins from Adelaide to discuss insider threat detection
• DTEX evolved from Adelaide startup to Bay Area headquarters, serving Fortune 500 companies
• Company specializes in understanding human behavior and intention behind insider threats
• Market shifting beyond cyber indicators to focus on behavioral analysis and detection
• Recent case: US citizen sold identity to North Korean DPRK IT workers
• Foreign entities used stolen credentials to infiltrate American companies undetected
• DTEX's behavioral detection systems helped identify this sophisticated identity theft operation
• Generative AI becomes double-edged sword - used by both threat actors and defenders
• Bad actors use AI for fake resumes and deepfake interviews
• DTEX uses traditional machine learning for risk modeling, GenAI for analyst interpretation
• Goal is empowering security analysts to work faster, not replacing human expertise
• AWS GenAI Innovation Center helped develop guardrails and usage boundaries for enterprise
• Challenge: enterprises must follow rules while hackers operate without ethical constraints
• DTEX gains advantage through proprietary datasets unavailable to public AI models
• AWS Bedrock partnership enables private, co-located language models for data security
• Private preview launched February 2024 with AWS Innovation Center acceleration support
• Software leaders should prioritize privacy-by-design from day one of GenAI adoption
• Future threat: information sharing shifts from files to AI-powered data queries
• Monitoring who asks what questions of AI systems becomes critical security concern
• DTEX contributes to OpenSearch development while building vector databases for analysis

Participants:

• Rajan Koo – Chief Technology Officer, DTEX Systems

Further Links:

• DTEX Systems Website
• DTEX Systems AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Enterprise AI leaders from C3 AI, Resolve AI, and Scale AI reveal how Fortune 100 companies are successfully scaling agentic AI from pilots to production and share secrets for successful AI transformation.

Topics Include:

• Panel introduces three AI leaders from Resolve AI, C3 AI, and Scale AI
• Resolve AI builds autonomous site reliability engineers for production incident response
• C3 AI provides full-stack platform for developing enterprise agentic AI workflows
• Scale AI helps Fortune 100 companies adopt agents with private data integration
• Moving from AI pilots to production requires custom solutions, not shrink-wrap software
• Success demands working directly with customers to understand their specific workflows
• All enterprise AI solutions need well-curated access to internal data and resources
• Software engineering has permanently shifted to agentic coding with no going back
• AI agents rapidly improving in reasoning, tool use, and contextual understanding
• Industry moving from simple co-pilots to agents solving complex multi-step problems
• Spiros coins new concept: evolving from "systems of record" to "systems of knowledge"
• Democratized development platforms let enterprises declare their own agent workflows
• Semantic business layers enable agents to understand domain-specific enterprise operations
• Trust and observability remain major barriers to enterprise agent adoption
• Oversight layers essential for agents making longer-horizon autonomous business decisions
• Performance tracking and calibration systems needed like MLOps for reasoning chains
• CEO-level top-down support required for successful AI transformation initiatives
• Traditional per-seat SaaS pricing models completely broken for agentic AI solutions
• Industry shifting toward outcome-based and work-completion pricing models instead
• Real examples shared: agent collaboration in production engineering and sales automation

Participants:

• Nikhil Krishnan – SVP & Chief Technology Officer, Data Science, C3 AI
• Spiros Xanthos – Founder and CEO, Resolve AI
• Vijay Karunamurthy – Head of Engineering, Product and Design / Field Chief Technology Officer, Scale AI
• Andy Perkins – GM, US ISV Sales – Data, Analytics, GenAI, Amazon Web Services

Further Links:

• C3 – Website – AWS Marketplace
• Resolve AI – Website – AWS Marketplace
• Scale AI – Website – AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Industry leaders from Automation Anywhere and AWS discuss how modern customer data collection has evolved, and practical strategies for implementing enterprise automation at scale.

Topics Include:

• Automation Anywhere and AWS experts discuss modern enterprise automation strategies
• Traditional profiting strategies may not work with today's changing business models
• Customer data collection methods have evolved across multiple platforms significantly
• Modern verification processes include automated validation systems and streamlined timelines
• Background check automation is increasingly handled by AI-powered models and systems
• Stanford's "Wonder Bread" research paper introduced revolutionary enterprise process observation technology
• Wonder Bread demonstrated AI systems watching and automatically learning hospital workflows
• The technology can author workflows by observing real enterprise processes
• Enterprise Process Management built around observed behaviors shows promising results
• Verification challenges exist since Wonder Bread research isn't widely publicized yet
• Process observation technology could transform how enterprises handle workflow creation
• Salesforce Wizard Interface dominates many current automation implementations in enterprises
• Salesforce Agent Codes offer alternative approaches to traditional automation methods
• AWS platform selection involves careful consideration of enterprise integration needs
• Demo implementations showcase real-world timeline expectations and deployment maturity levels
• Current automation solutions have reached significant scale across various industries
• Workflow automation differs fundamentally from true agentic intelligence systems capabilities
• Agentic AI demonstrates autonomous decision-making beyond simple rule-based automation processes
• Understanding this distinction helps organizations choose appropriate technology approaches effectively
• Session concludes with clarity on modern automation landscape and implementation strategies

Participants:

• Pratyush Garikapati – Director of Products, Automation Anywhere
• Sreenath Gotur – Snr Generative AI Specialist, Amazon Web Services

Further Links:

• Automation Anywhere website
• Automation Anywhere – AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

AWS's Mark Relph draws fascinating parallels between today's AI revolution and the 1900s agricultural mechanization that delivered 2,000% productivity gains, while exploring how agentic AI will fundamentally reshape every aspect of software business models.

Topics Include:

• Mark Relph directs AWS's data and AI partner go-to-market strategy team
• His role focuses on making ISV partners a force multiplier for customer success
• Previously ran go-to-market for Amazon Bedrock, AWS's fastest growing service ever
• Current AI adoption pace exceeds even the early cloud computing boom years
• Historical parallel: 1900s agricultural mechanization delivered 2,000% productivity gains and 95% resource reduction
• First commercial self-propelled farming equipment revolutionized entire economies and never looked back
• 500 machines formed the "Harvest Brigade" during WWII, harvesting from Texas to Canada
• Mark has spoken to 600+ AWS customers about GenAI over two years
• Organizations range from AI pioneers to those still "fending off pirates" internally
• GenAI has become a phenomenal assistant within organizations for content and automation
• AWS's AI stack has three layers: infrastructure, Bedrock, and applications
• Bottom layer provides complete control over training, inference, and custom applications
• Middle layer Bedrock serves as the "operating system" for generative AI applications
• Top layer offers ready-to-use AI through Q assistants and productivity tools
• AI systems are rapidly becoming more complex with multiple model chains
• Many current "agents" are just really, really long prompts (Mark's hot take)
• Task-specific models are emerging as one size won't fit all use cases
• Evolution moves from human-driven AI to agent-assisted to fully autonomous agents
• Agent readiness requires APIs that allow software to interact autonomously
• Traditional UIs become unnecessary when agents interface directly with systems
• Core competencies shift when AI handles the actual "doing" of tasks
• Sales and marketing must adapt to agents delivering outcomes autonomously
• Go-to-market strategies need complete rethinking for an agentic world
• The agentic age is upon us and AWS partners should shape the future

Participants:

• Mark Relph – Director – Data & AI Partner Go-To-Market, Amazon Web Services

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Industry leaders from Celonis and AWS explain why 2025 marks the inflection point for agentic AI and how early adopters are gaining significant competitive advantages in efficiency and innovation.

Topics Include:

• AWS's Cristen Hughes and Celonis's Jeff Naughton discuss AI agent transformation
• Andy Jassy declares AI agents will fundamentally change how we work
• Three key trends make AI agents practical: smarter models, longer tasks, cheaper costs
• AI now beats humans on complex benchmarks for the first time ever
• Claude 3.7 cracked graduate-level reasoning where humans previously dominated completely
• AI evolved from brief interactions to managing sustained multi-step complex workflows
• Processing costs plummeted 99.7% making enterprise-grade AI economically viable at scale
• We're transitioning from 2023's adaptation era to 2025's human-AI collaboration era
• By 2028, AI will suggest actions to humans rather than vice versa
• Agents are autonomous software that plan, act, and reason independently with minimal intervention
• Agent workflow: receive human request, create plan, execute actions, review, adjust, deliver
• Four agent components: brain (LLM), memory (context), actions (tools), persona (role definition)
• AWS offers three building approaches: ready-made solutions, managed platform, DIY development
• Key enterprise applications: software development acceleration, customer care automation, knowledge work optimization
• Manual processes like accounts payable offer huge transformation opportunities through intelligent automation
• Deep process analysis is critical before deploying agents for maximum effectiveness
• Celonis pioneered process mining to help enterprises understand their actual workflow realities
• Companies are collections of interacting processes that agents need proper context to navigate
• Process intelligence provides agents with placement guidance, data feeds, monitoring, and workflow direction
• Celonis-AWS partnership demonstrates order management agents that automatically handle at-risk situations

Participants:

• Jeff Naughton – SVP and Fellow, Celonis
• Cristen Hughes – Solutions Architecture Leader, ISV, North America, Amazon Web Services

Further Links:

• Celonis Website
• Celonis on AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Justin DiPietro, Co-Founder & Chief Strategy Officer of Glia, shares how they are leveraging AI to enhance the customer experience in the highly regulated world of financial institutions.

Topics Include:

• Glia provides voice, digital, and AI services for customer-facing and internal operations
• Built on "channel-less architecture" unlike traditional contact centers that added channels sequentially
• One interaction can move seamlessly between channels (voice, chat, SMS, social)
• AI applies across all channels simultaneously rather than per individual channel
• 700 customers, primarily banks and credit unions, 370 employees, headquartered in New York
• Targets 3,500 banks and credit unions across the United States market
• Focuses exclusively on financial services and other regulated industries
• AI for regulated industries requires different approach than non-regulated businesses
• Traditional contact centers had trade-off between cost and quality of service
• AI enables higher quality while simultaneously decreasing costs for contact centers
• Number one reason people call banks: "What's my balance?" (20% of calls)
• Financial services require 100% accuracy, not 99.999% due to trust requirements
• Uses AWS exclusively for security, reliability, and future-oriented technology access
• Real-time system requires triple-hot redundancy; seconds matter for live calls
• Works with Bedrock team; customers certify Bedrock rather than individual features
• Showed examples of competitors' AI giving illegal million-dollar loans at 0%
• "Responsible AI" separates probabilistic understanding from deterministic responses to customers
• Uses three model types: client models, network models, and protective models
• Traditional NLP had 50% accuracy; their LLM approach achieves 100% understanding
• Policy is "use Nova unless" they can't, primarily for speed benefits

Participants:

• Justin DiPietro – Co-Founder & Chief Strategy Officer, Glia

Further Links:

• Glia Website
• Glia AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Ed Bailey, Field CISO at Cribl, shares how Cribl and AWS are helping customers rethink their data strategy by making it easier to modernize, reduce complexity, and unlock long-term flexibility.

Topics Include:

• Ed Bailey introduces topic: bridging gap between security data requirements and budget
• Companies face mismatch: 10TB data needs vs 5TB licensing budget constraints
• Data volumes growing exponentially while budgets remain relatively flat year-over-year
• IT security data differs from BI: enormous volume, variety, complexity
• Many companies discover 600+ data sources during SIEM migration projects
• 50% of SIEM data remains un-accessed within 90 days of ingestion
• Complex data collection architectures break frequently and require excessive maintenance
• Teams spend 80% time collecting data, only 20% analyzing for value
• Data collection and storage are costs; analytics and insights provide business value
• Poor data quality creates operational chaos requiring dozens of browser tabs
• SOC analysts struggle with context switching across multiple disconnected systems
• Traditional vendor approach: "give us all data, we'll solve problems" is outdated
• Data modernization requires sharing information widely across organizational business units
• Data maturity model progression: patchwork → efficiency → optimization → innovation
• Data tiering strategy: route expensive SIEM data vs cheaper data lake storage
• SIEM costs ~$1/GB while data lakes cost ~$0.15-0.20/GB for storage
• Compliance retention data should go to object storage at penny fractions
• Decouple data retention from vendor tools to enable migration flexibility
• Cribl platform offers integrated solutions: Stream, Search, Lake, Edge components
• Customer success: Siemens reduced 5TB to 500GB while maintaining security effectiveness

Participants:

• Edward Bailey – Field CISO, Cribl

Further Links:

• Cribl Website
• Cribl on AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Sam Johnson, Chief Customer Officer of Jamf, discusses the implementation of AI built on Amazon Bedrock that is a gamechanger in helping Jamf’s 76,000+ customers scale their device management operations.

Topics Include:

• Sam Johnson introduces himself as Chief Customer Officer from Jamf company
• Jamf's 23-year mission: help organizations succeed with Apple device management
• Company manages 33+ million devices for 76,000+ customers worldwide from Minneapolis
• Jamf has used AI since 2018 for security threat detection
• Released first customer-facing generative AI Assistant just last year in 2024
• Presentation covers why, how they built it, use cases, and future plans
• Jamf serves horizontal market from small business to Fortune 500 companies
• Challenge: balance powerful platform capabilities with ease of use and adoption
• AI could help get best of both worlds - power and simplicity
• AI also increases security posture and scales user capabilities significantly
• Customers already using ChatGPT/Claude but wanted AI embedded in product
• Built into product to reduce "doorway effect" of switching digital environments
• Created small cross-functional team to survey land and build initial trail
• Rest of engineering organization came behind to build the production highway
• Team needed governance layer with input from security, legal, other departments
• Evaluated multiple providers but ultimately chose Amazon Bedrock for three reasons
• AWS team support, large community, and integration with existing infrastructure
• Uses Lambda, DynamoDB, CloudWatch to support the Bedrock AI implementation
• AI development required longer training/validation phase than typical product features
• Released "AI Assistant" with three skills: Reference, Explain, and Search capabilities

Participants:

• Sam Johnson – Chief Customer Officer, Jamf

Further Links:

• Jamf.com
• Jamf on AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Mark Stevens, SVP, Channels and Alliances, discusses how SecurityScorecard's strategic partnership with AWS enables them to scale their security solutions through cloud infrastructure, marketplace integration, and co-sell programs

Topics Include:

• SecurityScorecard founded 10 years ago to understand third-party vendor security posture
• Company has grown to 3,000 enterprise customers and 200+ partners globally
• Evolved from ratings to "supply chain detection and response" over last year
• Supply chain threats have doubled, creating extended attack surfaces for companies
• Many organizations don't know their vendor count or vulnerabilities within supply chains
• SecurityScorecard provides visibility into attack surfaces and management tools for control
• Generative AI is central to their ecosystem, leveraging AWS Bedrock extensively
• They scan the entire internet every two days at massive scale
• Have scored 12 million companies with security scorecards to date
• All workloads run on AWS cloud infrastructure as their primary platform
• AWS partnership provides necessary scale for managing hundreds of thousands of vendors
• Case study: Identified vendor misconfigurations that could shut down 1,000 locations
• Own massive 10-year data lake with tens of millions of companies
• New managed service combines AI automation with human analysts for support
• Large organizations cannot fully automate supply chain security management yet
• Quality threat intelligence data now valuable to SOC teams, not just risk
• Third-party risk management and SOC teams are slowly converging for better security
• AWS marketplace integration provides frictionless customer experience and larger deals
• Co-sell programs with AWS enterprise sales teams create effective flywheel motion
• Future expansion includes identity management, response actions, and internal signal management

Participants:

• Mark Stevens – SVP, Channels and Alliances, SecurityScorecard

Further Links:

• SecurityScorecard.io
• SecurityScorecard AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Chief Product Development Officer Mitchell Johnson discusses how Sonatype protects enterprise developers from malicious open source components while keeping them productive through AI.

Topics Include:

• Sonatype provides software supply chain solutions for enterprises using open source components
• They serve large enterprises, government agencies, and critical infrastructure providers globally
• Main challenge: keeping developers productive while maintaining secure software supply chains
• Cybercrime and supply chain attacks are massive, growing industries threatening developers
• AI adoption is happening faster than expected, profoundly changing development workflows
• Bad actors evolved from waiting for vulnerabilities to creating malicious components
• Malicious open source components specifically target developer and DevOps toolchains
• Sonatype's security research team uses AI/ML to analyze every open source component
• They can predict and block malicious components before entering customer environments
• AWS partnership helps Sonatype meet customers where they want to do business
• Partnership focuses on go-to-market alignment, not just technical integration
• AWS sales teams should be treated as extensions of your own sales organization
• Understanding AWS sales structure and incentives is crucial for successful partnerships
• AI development is following same pattern as open source adoption twenty years ago
• "Shadow AI" parallels the earlier "shadow IT" trend with open source software
• AI speeds up code generation but security review processes haven't kept pace
• Developers need a "Hippocratic Oath" - taking responsibility for AI-generated code output
• Within 24 months, professionals not skilled in AI will struggle to stay relevant
• Sonatype's culture encourages curiosity, experimentation, and accepts failure as part of innovation
• Their core mission: help developers focus on innovation, not security chores

Participants:

• Mitchell Johnson – Chief Product Development Officer, Sonatype

Further Links:

• Sonatype Website
• Sonatype on AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Founder and CTO Alex Rice discusses how HackerOne uses generative AI to automate security workflows and prioritizing accuracy over efficiency to achieve end-to-end outcomes.

Topics Include:

• HackerOne uses ethical hackers and AI to find vulnerabilities before criminals
• White hat hackers stress test systems to identify security weaknesses proactively
• Generative AI plays a huge role in HackerOne's security operations
• Security teams struggle with constant toil of finding and fixing vulnerabilities
• AI helps minimize toil through natural language interfaces and automation
• Both good and bad actors have access to generative AI tools
• Success requires measuring individual task inputs and outputs, not just aggregates
• Breaking down workflows into granular tasks reveals measurable AI improvements
• HackerOne deployed "Hive," their AI security agent to reduce customer toil
• Initial focus was on tasks where AI clearly outperformed humans
• Started with low-hanging fruit before tackling more complex strategic workflows
• Accuracy is the primary success metric, not just efficiency or speed
• Security requires precision; wrong fixes create bigger problems than inefficiency
• Customer acceptance and reduced time to remediation are north star metrics
• Humans remain the source of truth for validation and feedback loops
• Break down human jobs into granular AI tasks using systems thinking
• Build specific agents for individual tasks rather than entire job roles
• Keep humans accountable for end-to-end outcomes to maintain customer trust
• AWS Bedrock chosen for security, confidentiality, and data separation requirements
• Moving from efficiency improvements to entirely new AI-enabled capabilities

Participants:

• Alex Rice – Founder & CTO/CISO, HackerOne

Further Links:

• HackerOne Website
• HackerOne on AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Spencer Herrick, Principal AI Product Manager of Asana and Oliver Myers of AWS demonstrate how their integration allows Asana's AI workflows to access enterprise data from Amazon Q Business, enabling seamless cross-application automation and insights.

Topics Include:

• Oliver Myers leads Amazon Q Business go-to-market, Spencer Herrick manages Asana AI products.
• Session focuses on end user productivity challenges with generative AI technology implementations.
• End users face technology overload with doubled workplace application usage over five years.
• Data silos prevent getting maximum value from generative AI across fragmented enterprise systems.
• Workers spend 53% of time on "work about work" instead of strategic contributions.
• Ideal experience needs single pane of glass with cross-application insights and actions.
• Amazon Q Business launched as managed service with 40+ enterprise data connectors.
• Connectors maintain end-user permissions from source systems for enterprise security compliance.
• QIndex feature enables ISVs to access Q Business data via API calls.
• End users get answers enriched with multiple data sources without switching applications.
• Asana's work graph connects all tasks, projects, and portfolios to company goals.
• Phase 1 AI focused on narrow solutions like smart status updates.
• Phase 2 aimed for AI teammate capabilities requiring extensive contextual knowledge.
• AI Studio launched as no-code workflow automation builder within Asana platform.
• Q integration allows AI Studio to access cross-application context beyond Asana boundaries.
• SmartChat enhanced with Q can answer "what should I work on today?" holistically.
• Users returning from PTO can quickly understand goal risks across data sources.
• AI Studio workflows automate feature request processing across Asana, Drive, Slack, email.
• Partnership eliminates silos while maintaining enterprise security and permission controls.
• Integration creates connected ecosystem enabling true cross-application AI automation and insights.

Participants:

• Spencer Herrick - Principal AI Product Manager, Asana
• Oliver Myers - Worldwide Head of Business Development, Amazon Web Services

Further Links:

• Asana.com
• Asana on AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Chief Product Officer Dan Brown explains how Celonis creates digital twins of business processes to power AI agents that automate operational improvements.

Topics Include:

• Dan Brown introduces Celonis as the thought leader in process mining for over a decade.
• Celonis serves largest global companies across all industries seeking operational improvements.
• Companies have process diagrams but actual operations differ significantly from documentation.
• Celonis creates digital twins of business processes by analyzing system data flows.
• Process intelligence reveals how work actually happens versus how companies think it happens.
• Platform enables process normalization, improvement assessment, and automated corrective actions.
• Celonis vision: making processes work better for people, companies, and the planet.
• Process intelligence provides visibility into current operations and improvement strategies.
• Great AI requires great data, but most companies only have static views.
• Process intelligence graph shows real-time flow of orders, invoices, and opportunities.
• Agentic AI requires four capabilities: sensing, planning, executing, and governing operations.
• Process intelligence enables real-time detection of conformance problems and deviations.
• AWS partnership leverages Bedrock for agentic AI and infrastructure for data processing.
• Data ingestion, organization, and enrichment are core to process intelligence value.
• AI agents now handle process deviations with increasing autonomy and sophistication.
• Heavy equipment manufacturer uses agents to coordinate with third-party vendors automatically.
• Agents text and email vendors to confirm delivery dates, reducing manual work.
• Implementation challenges include data quality, conservative adoption, and governance concerns.
• Companies should start with achievable use cases and expand gradually across domains.
• Future involves enterprise-wide process visibility powering automated applications and continuous improvement.

Participants:

• Dan Brown – Chief Product Officer, Celonis

Further Links:

• Celonis Website
• Celonis on AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

AWS partners Automation Anywhere, Qlik, and Vectra.ai discuss revolutionizing customer experience through generative AI, sharing real-world implementations in automation, analytics, and cybersecurity applications.

Topics Include:

• AWS Technology Partnerships panel on agentic AI implementation
• Three AWS partners share real-world AI deployment experiences
• Automation Anywhere automates end-to-end business processes with agents
• Vectra.ai uses autonomous agents for cybersecurity threat detection
• Qlik applies generative AI across their data platform portfolio
• Customer service automation handles L1 support requests efficiently
• Utility company processes 144,000 complaints annually using agents
• Regulatory compliance improved through faster complaint resolution workflows
• Cybersecurity agents reduce threat detection time by 50-60%
• Triage, correlation, and prioritization handled by autonomous agents
• Signal fatigue reduced through intelligent alert filtering systems
• Natural language queries enable faster business decision making
• Sales AI agent provides competitive information during calls
• AWS Marketplace reduced 7,000 weekly tickets to zero
• 2023 was proof-of-concept year, 2024 focuses production deployment
• AWS Bedrock integration seamless with existing data repositories
• Model optionality crucial for different use case requirements
• Agility most important capability in generative AI journey
• Code abandonment becomes acceptable due to rapid innovation
• Maximum team size of 10 people maintains development agility
• Targeted solutions outperform broad platform capabilities in adoption
• Implementation expertise becomes bottleneck for customer scaling efforts
• Natural language interaction patterns completely shifted user behavior
• Keywords searches replaced by conversational query approaches
• Responsible AI committees review decisions and establish principles
• Security considerations balance speed with responsible deployment practices
• Bad actors adopt generative AI faster than defenders
• Explainability requirements slow feature rollout but ensure auditability
• Multi-modal deployments use different models for specific cases
• Future trends include AI-powered business process outsourcing

Participants:

• Peter White – SVP, Emerging Products, Automation Anywhere
• Ryan Welsh – Field CTO - Generative AI, Qlik
• John Skinner – Vice President Corporate/Business Development, Vectra.ai
• Chris Grusz – Managing Director for Technology Partnerships, AWS

Further Links:

• Automation Anywhere in AWS Marketplace
• Qlik in AWS Marketplace
• Vectra.ai in AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Zak Krider, Trellix's Director of Strategy and AI, shares how Trellix has successfully integrated generative AI into their security operations and democratized access to AI models across the organization.

Topics Include:

• Trellix formed from McAfee Enterprise and FireEye merger
• Provides full security stack visibility in single platform
• Serves SMBs to Fortune 500 and government customers
• Used machine learning for two decades with 30 models
• Recently pivoted to generative AI with Wwise platform
• AI finds critical events among thousands daily alerts
• Incorporates threat hunting knowledge into AI prompt structures
• AWS Bedrock central to AI strategy for model flexibility
• Formed small tiger team to investigate generative AI
• Anthropic Claude provided breakthrough "aha moments" for capabilities
• Adopted "fail fast, learn fast" innovation culture approach
• Enabled employee access to models through Bedrock API
• Conducted innovation jam sessions with VC-style pitches
• AI decoded Base64 without prompting, identified benign activity
• Junior analysts elevated to level two with AI
• Common misconception: models train on customer data falsely
• Early challenge: providing too much data overwhelmed models
• Smaller models hallucinated more with plausible-sounding responses
• Design partner programs help prioritize product development
• Democratize AI access beyond just technical teams
• Test multiple models for specific use cases
• Large models work better than small ones initially
• Prompt engineering crucial for effective model communication
• Model Context Protocol will gain traction next year
• Backend data security remains largely unsolved challenge
• Federal customers require on-premises, air-gapped AI solutions

Participants:

• Zak Krider – Director of AI and Innovation, Trellix

Further Links:

• Website: https://www.trellix.com
• Trellix on AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

NVIDIA’s Global Head of Partnerships & Cloud for Startups, Jen Hoskins, details their collaboration with AWS to support over 25,000 startups through their Inception program.

Topics Include:

• AI transformation happening across all industries and verticals
• NVIDIA evolved from GPU company to full-stack AI solutions
• Accelerated computing requires complete stack re-engineering from chip up
• Traditional CPU scaling has reached its fundamental performance limits
• NVIDIA-AWS partnership spans over 13 years of co-development
• DGX Cloud integrates seamlessly with AWS SageMaker and Bedrock
• Over 26 NVIDIA solutions available in AWS Marketplace
• NVIDIA AI Enterprise accelerates data science and deployment pipelines
• NIM microservices streamline AI model development like Docker containers
• Codeway gaming startup saved 48% on compute costs using NVIDIA
• Eternal improved marketing ROI by 30X with generative AI
• Quoto achieved 10X content length and 3X throughput improvement
• NOATech biotech scaled cancer research with small team efficiently
• NVIDIA Inception program supports over 25,000 startups globally
• Program covers 100+ countries across all verticals and stages
• Startups get AWS credits up to $100,000 through Activate
• Developer program offers free access to hundreds of SDKs
• Three program pillars: Innovate, Build, and Grow stages
• VC Alliance connects startups with over 1,000 investors
• Venture Capital Connect directly links startups to funding opportunities

Participants:

• Jen Hoskins – Startups, Global Head of Cloud, Partnerships & Go to Market, NVIDIA

Further Links:

• Website: https://www.nvidia.com
• NVIDIA Inception Program
• NVIDIA on AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Matt “Kix” Kixmoeller, Chief Marketing Officer of Glean, shares how Glean partners with AWS to deploy secure, scalable AI solutions that help companies move from basic productivity tools to transformative business intelligence.

Topics Include:

• Introduction to Glean
• Glean targets Global 2000 companies for AI transformation
• Enterprise AI needs company context: data, people, processes
• Bottom-up approach: deploy to all employees first
• Focus on business results, not just productivity gains
• Glean Assistant provides daily AI tool for employees
• Glean Agents platform enables natural language agent building
• Open APIs export context to enterprise systems
• Started as enterprise search, evolved to knowledge graphs
• Knowledge graphs map content, people, projects, and processes
• Individual knowledge graphs created for each person
• Glean WorkAI platform includes search, protect, agents
• Glean Protect ensures data security and AI governance
• Platform integrates with existing enterprise tools natively
• MCP enables connection to various AI systems
• Strong growth: $100M ARR, $700M+ funding raised
• AWS partnership provides models, security, and deployment

Participants:

• Matt “Kix” Kixmoeller – Chief Marketing Officer, Glean

Further Links:

• Website: https://www.glean.com/
• Glean on AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Kui Jia, Sumo Logic's Vice President of Engineering and Head of AI, shares how their AWS-powered AI agents transform chaotic security investigations into streamlined workflows.

Topics Include:

• Kui Jia leads AI Engineering at Sumo Logic
• SREs and SOC analysts work under chaotic, high-pressure conditions
• Teams constantly switch between different vendor tools and platforms
• Investigation requires quick hypothesis formation and complex query writing
• Sumo Logic processes petabytes of data daily across enterprises
• Company serves 2,000+ enterprise customers for 15 years
• Platform focuses on observability and cybersecurity use cases
• Investigation journey: discover, diagnose, decide, act, learn phases
• Data flows from ingestion through analytics to human insights
• Traditional workflow relies heavily on tribal domain knowledge
• Senior engineers create queries that juniors struggle to understand
• War room situations demand immediate answers, not learning curves
• Context switching between tools wastes time and creates friction
• Multiple AI generations deployed: ML anomaly detection to GenAI
• Agentic AI enables reasoning, planning, tools, and evaluation capabilities
• Mo Copilot launched at AWS re:Invent as AI agent suite
• Natural language converts high-level questions into Sumo queries
• System provides intelligent autocomplete and multi-turn conversations
• Insight agents summarize logs and security signals automatically
• Knowledge integration combines foundation models with proprietary metadata
• AI generates playbooks and remediation scripts for automated actions
• Three-tier architecture: Infrastructure, AI Tooling, and Application layers
• Built on AWS Bedrock with Nova models for performance
• Focus on reusable infrastructure and AI tooling components
• Data differentiation more important than AI model selection
• Golden datasets and contextualized metadata are development challenges
• Guardrails and evaluation frameworks critical for enterprise deployment
• AI observability enables debugging and performance monitoring
• Enterprise agents achievable within one year development timeline
• Future vision: multiple AI agents collaborating with human investigators

Participants:

• Kui Jia – Vice President of AI Engineering, Head of AI, Sumo Logic

Further Links:

• Website: https://www.sumologic.com/
• Sumo Logic in the AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Wilson Patton, Solutions Architect for Trellix, demonstrates how their four-pillar Gen-AI framework transforms incident alerts into actionable intelligence.

Topics Include:

• Wilson Patton: Trellix Solutions Architect, 20 years government experience
• Witnessed evolution from basic firewalls to zero trust architectures
• Trellix combines McAfee and FireEye heritage and capabilities
• AI integration isn't new - machine learning embedded for years
• Partnership with AWS Bedrock accelerates Gen-AI development capabilities
• 2014: Developed Impossible Travel Analytic for anomaly detection
• 2016: Launched Guided Investigations framework for SOC analysts
• 2023: Introduced AI Guided Investigations with contextual understanding
• 64% of public sector exploring AI adoption actively
• Only 21% have requisite data ready for training
• Gen-AI won't magically clean up messy, siloed data
• 74% of executives doubt AI information accuracy currently
• Monday morning alert queue: 76 high, 318 medium alerts
• Adversaries steal credentials 90 days before major incidents
• Critical breadcrumbs hidden in low-priority informational alerts
• 1000+ data-driven investigative questions developed over eight years
• Skilled analysts take too long reading all answers
• Automate analysis, distill thousands down to ten critical alerts
• Four foundational pillars for effective, trustworthy Gen-AI implementation
• Cybersecurity expertise essential - Gen-AI is just a tool
• Frameworks ensure reliability and consistent prompting for production
• Multiple LLM models tested through AWS Bedrock platform
• Quality diverse datasets required for accurate question answering
• Good prompts combine evidence, context, and comprehensive information
• Testing shows order of magnitude price differences between models
• Nova Micro provides cost-effective results for many scenarios
• Prompt engineering superior to fine-tuning for avoiding bias
• Agentic AI performs multi-step investigations with live data
• Strategic model choice based on specific requirements and costs
• Transparent audit trails mandatory for government compliance requirements

Participants:

• Wilson Patton – Solutions Architect, Trellix

Further Links:

• Website: https://www.trellix.com
• Trellix in the AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Jeff Chow, Chief Product and Technology Officer at Miro, explores how harnessing AI — in addition to reshaping teams and workflows — accelerates the product development lifecycle. He also shares insight into how Miro is embracing new technology and ways of working to transform its Innovation Workspace.

Topics Include:

Platform & Partnership

• Miro serves 250,000+ customers with 90+ million knowledge workers using their Innovation Workspace
• Platform supports discovery, definition, and delivery phases of innovation process
• Real-time multiplayer canvas enables team co-creation across multiple formats, including seamless transitions between structured and unstructured work.
• Three-tier AWS partnership: infrastructure backbone, AI services (Bedrock/Q), and joint customer solutions

Innovation Challenges & Friction

• Product development lifecycle bottlenecks: separate tools per function create process delays and collaborative friction
• Pain points include stalled product kickoffs, lengthy design ideation cycles, and process delays from engineering architecture discussions.
• Leadership struggles with project visibility and strategic alignment across initiatives

AI Transformation

• AI fundamentally shifts workflows with universal knowledge access at fingertips
• Craft democratization blurs traditional role boundaries (PMs prototyping, developers designing)
• Agentic workflows and agents collapse traditional development stack layers
• AI shortcuts enable one-button synthesis of workshops into product briefs
• Product development lifecycle compression from 20 steps to 5 key phases
• Bedrock and Q services create significant business acceleration

Organizational Design

• Common organizational rhythms and rituals create shared working language
• Driving maximum impact by aligning on big initiatives vs. distributed priorities
• Collaborating across all functions — product, engineering, design — and at all organizational levels
• Bottom-up innovation requiring clear problem communication throughout organization
• Inclusive environments welcoming ideas from junior and introverted team members
• Working backwards planning and PR FAQs adopted from Amazon methodologies

Creating the next big thing with Miro

• Large enterprises use Miro for strategic planning, OKR planning, capacity planning, roadmapping
• Visual proof-of-concepts and live demos make abstract concepts tangible
• Same-day product brief delivery improves team collaboration and ownership
• Voice of customer integration: automated synthesis of feedback into feature development
• Miro uses Miro internally to build next-generation features
• Enhanced employee engagement alongside improved business outcomes
• Customers consistently achieve 2-3x time-to-market improvements

Participants:

• Jeff Chow – Chief Product and Technology Officer, Miro
• Johan Broman – EMEA ISV Head of Solutions Architecture, AWS

Further Links:

• Website: https://miro.com/page/product-leaders/
• Miro in the AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Andrea Malagodi, CTO of Sonar, discusses how the company successfully transitioned from on-premise to SaaS, leveraging AWS partnership and maintaining focus on developer-centric code quality and security solutions.

Topics Include:

• Andrea Malagodi is CTO of Sonar, guest on podcast
• Sonar founded 16+ years ago by three software engineers
• Founders wanted to help developers understand code quality issues
• Focus on giving developers precise, actionable insights for improvement
• Products include SonarQube Server, Cloud, and IDE versions
• Recent acquisitions: ACR, Tidelift, and Structure 101 companies
• SaaS journey began seven years ago with SonarQube Cloud
• Initially targeted individual developers, then expanded to enterprises
• Now multi-region with comprehensive enterprise features available
• Seven million developers rely on Sonar's solutions globally
• 400,000 organizations and 28,000 enterprise customers use Sonar
• Started SaaS to test market demand, not assumptions
• Engaged customers early to understand migration requirements needed
• Recommends alpha versions with design customers for feedback
• Free tier for open-source code enables quick trial
• Enterprise certifications (ISO 27001, SOC 2) build trust
• AWS partnership includes enterprise support and technical resources
• Used CDK for infrastructure-as-code, experienced early adoption challenges
• Multi-region strategy should be considered from the beginning
• AWS Learning partnership certified all engineers in cloud
• Cloud enables faster development cycles than traditional infrastructure
• Recommends avoiding architectural one-way doors during transition
• Consider data residency requirements for global customer base
• AI-generated code creates productivity gains but needs validation
• Sonar provides deterministic rules for AI-generated code review
• Working on MCP protocol and AI code quality solutions
• Security approach is "start left" not "shift left"
• Advanced Security offering includes dependency scanning and vulnerabilities
• Available on sonarsource.com and AWS Marketplace
• Free tier offers 50,000 lines of code analysis

Participants:

• Andrea Malagodi – Chief Technical Officer, Sonar

Further Links:

• Website: www.sonarsource.com
• Sonar in the AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Kanaiya Vasani, Chief Product Officer, explains how ExtraHop leverages AWS services and generative AI to help enterprise customers address the growing security challenges of uncontrolled AI adoption.

Topics Include:

• ExtraHop reinventing network detection and response category
• Platform addresses security, performance, compliance, forensic use cases
• Behavioral analysis identifies potential security threats in infrastructure
• Network observability and attack surface discovery capabilities included
• Application and network performance assurance built-in features
• Traditional IDS capability with rules and IOCs detection
• Packet forensics for investigating threats and wire evidence
• Cloud-native implementations and compromised credential investigation support
• ExtraHop partnership with AWS spans 35-40 different services
• AWS handles infrastructure while ExtraHop focuses core competencies
• ExtraHop early adopter of generative AI in NDR
• Natural language interface enables rapid data access queries
• English questions replace complex query languages for users
• Agentic AI experiments focus on SOC automation workflows
• L1 and L2 analyst workflow automation improves productivity
• Shadow AI creates major risk concern for customers
• Uncontrolled chatbot usage risks accidental data leakage
• Governance structures needed around enterprise gen AI usage
• Visibility required into LLM usage across infrastructure endpoints
• AI innovation pace challenges security industry keeping up
• Models evolved from billion to trillion parameters rapidly
• Traditional security tools focus policies, miss real-time activity
• "Wire doesn't lie" - network traffic reveals actual behavior
• ExtraHop maps baseline behavior patterns across infrastructure endpoints
• Anomalous behavioral patterns flagged through network traffic analysis
• MCP servers enable LLM access through standardized protocols
• Stolen tokens allow adversaries unauthorized MCP server access
• Machine learning identifies anomalous traffic patterns L2-L7 protocols
• Gen AI automates incident triage, investigation, response workflows
• Best practices include clear policies, governance, monitoring, education

Participants:

• Kanaiya Vasani – Chief Product Officer, ExtraHop Networks

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Cribl’s Field CISO Ed Bailey discusses how customers can manage the quality and quantity of data by providing intelligent controls between data sources and destinations.

Topics Include:

• Cribl company name origin
• Company helps organizations screen data to find valuable insights
• Ed Bailey was Cribl's first customer back in 2018
• Data growth of 25% yearly created seven-figure cost increases
• CEOs and CIOs complained about explosive data storage costs
• Users demanded more data while budgets remained constrained
• Bailey discovered Cribl through a random Facebook advertisement
• Cribl Stream sits between data sources and destinations
• No new agents required, uses existing infrastructure connections
• Reduced data growth from 28% to 8% within year
• Development cycles shortened from six weeks to two weeks
• Bailey managed global security and telemetry data systems
• Operated large Splunk instance across forty different countries
• Team spent time collecting data instead of extracting value
• Cribl provided consistent data control plane for operations
• Smart engineers could focus on machine learning solutions
• Migrated from terrible SIEM to better security platform
• Data strategy should focus on business requirements first
• Not all data has the same business value
• Tier one: Critical data goes to expensive platforms
• Tier two: Important data stored in cheaper lakes
• Tier three: Compliance data in low-cost object storage
• SIEM costs around one dollar per gigabyte stored
• Data lakes cost twelve to eighteen cents per gigabyte
• Object storage costs fractions of pennies per gigabyte
• AWS partnership provides scalable infrastructure for rapid growth
• EC2, EKS, and S3 are heavily utilized services
• Cribl Search finds data directly in object storage
• Avoids costly data movement for search and analysis

Participants:

• Edward Bailey – Field CISO, Cribl

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Saviynt Co-Founder Amit Saha discusses how their AWS partnership has enabled the identity security company to deliver comprehensive identity protection while minimizing organizational friction.

Topics Include:

• Saviynt is leading identity security provider in market
• Secures human, non-human, workforce, and privileged access identities
• Eliminates friction while automating organizational access management processes
• Biggest challenge: reducing friction in new access processes
• Second challenge: visibility into accumulated technical debt problems
• Lost business context makes access permissions difficult to unwind
• Saviynt provides quick visibility to prioritize identity risks
• Shadow IT creates ungoverned workloads and cloud applications
• Need integration with asset management and cloud providers
• Must derive intelligence from multiple disconnected information sources
• AWS partnership provides access to prolific customer base
• AWS security owners are same buyers for Saviynt
• Eleven-year AWS relationship with early security competency
• ISV Accelerate program connects with sellers and architects
• Rising Star program helps stand out in crowded marketplace
• Find mutual customers for successful AWS partnership stories
• GenAI in bad actors' hands compromises customer security
• Product engineering uses GenAI tools for better quality
• Agentic AI creates new paradigm between human/non-human identities
• Agentic AI requires dynamic, fluid access management approaches
• AI agents can generate their own bots needing access
• Zero trust principles needed at broader scale for AI
• Next twelve months: getting ahead of GenAI curve
• New AWS services launch daily in GenAI space
• Contributing to new standards like MCP and A2A protocols
• AWS Marketplace simplifies procurement and buyer discovery processes
• EDP program and migration incentives benefit ISV transactions
• AWS developer-friendly startup programs accelerate time to market
• Cloud-native approach enables predictable scaling and AWS integration
• AWS-Saviynt partnership aims for once-in-generation security impact

Participants:

• Amit Saha – Co-Founder and Chief Growth Officer, Saviynt

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Chief Architect Russell Leighton discusses how Panther's cloud platform revolutionizes security operations by treating detections as Python code and AI enabled alert vetting turning responses from hours into minutes. 

Topics Include:

• Panther is a cloud security monitoring tool (cloud SIEM)
• Works at massive scale, more cost-effective than legacy systems
• Key differentiator: "detections as code" written in Python
• Brings software engineering best practices to security operations
• Enables unit testing and version control for security detections
• Recently adopted generative AI to improve security workflows
• SOC burnout is renowned due to tedious ticket processing
• AI has intelligence of security engineer, works much faster
• Example: Alert shows "Russ Leighton removed branch protection"
• Old way: Manual log analysis, checking user profiles manually
• Takes hours of squinting at detailed log data
• New AI way: Automatic vetting happens in minutes
• AI checks user profile in Okta or IDP
• Determines engineer status, assesses typical behavior patterns
• Provides risk assessment based on historical alert data
• Low risk for engineers, high risk for unusual users
• Example: HR person accessing production code is escalated
• Customer quote: Takes vetting "from hours to seconds"
• Panther customers get dedicated AWS accounts for security
• Company can't see customer data, only self-reported metrics
• AI provides summaries, risk assessments, timelines, visualizations
• Also suggests remediations like human security engineer would
• Initial concerns about putting AI in production environment
• Customer feedback exceeded expectations with feature requests
• AWS Bedrock integration addresses customer security concerns
• Uses Anthropic Claude as base LLM through Bedrock
• Customers can enable additional Bedrock guardrails independently
• AI transparency prevents hallucination concerns through explanations
• Claude's extended thinking mode shows reasoning process
• AI visualizes thinking with flowcharts explaining decision process

Participants:

• Russell Leighton – Chief Architect, Panther

Further Links:

• Website: Panther.com
• AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Security leaders from Anthropic and AWS discuss how agentic AI is transforming cybersecurity functions to autonomously handle everything from code reviews to SOC operations.

Topics Include:

• Agentic AI differs from traditional AI through autonomy and agency
• Traditional AI handles single workflow nodes, agents collapse multiple steps
• Higher model intelligence enables understanding of broader business contexts
• Agents make intelligent decisions across complex multi-step workflows processes
• Enterprise security operations are seeing workflow consolidation through GenAI
• Organizations embedding GenAI directly into customer-facing production applications
• Software-as-a-service transitioning to service-as-software through AI agents
• Securing AI requires guardrails to prevent hallucinations in applications
• New vulnerabilities appear at interaction points between system components
• Attackers target RAG systems and identity/authorization layers instead
• LLMs hallucinate non-existent packages, attackers create malicious honeypots
• Governance frameworks must be machine-readable for autonomous agent reasoning
• Amazon investing in automated reasoning to prove software correctness
• Anthropic uses Claude to write over 50% of code
• Automated code review systems integrated into CI/CD pipelines
• Security design reviews use MITRE ATT&CK framework automation
• Low-risk assessments enable developers to self-approve security reviews
• 40% reduction in application security team review workload
• Anthropic eliminated SOC, replaced entirely with Claude-based automation
• IT support roles transitioning to engineering as automation replaces frontline
• Compliance questionnaires fully automated using agentic AI workflows
• ISO 42001 framework manages AI deployment risks alongside security
• Executive risk councils evaluate AI risks using traditional enterprise processes
• AWS embeds GenAI into testing, detection, and user experience
• Finding summarization helps L1 analysts understand complex AWS environments
• Amazon encourages teams to "live in the future" with AI
• Interview candidates expected to demonstrate Claude usage during interviews
• Security remains biggest barrier to enterprise AI adoption beyond POCs
• Virtual employees predicted to arrive within next 12 months
• Model Context Protocol (MCP) creates new supply chain security risks

Participants:

• Jason Clinton – Chief Information Security Officer, Anthropic
• Gee Rittenhouse – Vice President, Security Services, AWS
• Hart Rossman – Vice President, Global Services Security, AWS
• Brian Shadpour – GM of Security and B2B Software Sales, AWS

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Spryker’s Chief Product Officer, Elena Leonova, discusses the Spryker Business Intelligence platform and how working with AWS as a strategic advisor unlocked deeper opportunities for transformative growth.

Topics Include:

• Elena Leonova introduces Spryker as digital commerce platform
• Spryker focuses on sophisticated B2B commerce transactions
• Traditional industries: manufacturing, industrial goods, med tech
• Customers sell complex equipment like MRI machines, tractors
• Products are custom-built to order through procurement processes
• Extensive negotiation and aftermarket servicing are required
• Competitors focus on fashion, food - not complex equipment
• Spryker exclusively hosted on AWS cloud infrastructure
• AWS partnership enables new capabilities and customer innovation
• Business intelligence tools and AI capabilities now available
• Ricoh example: global manufacturer of industrial-grade printers
• Ricoh sells through dealers and distributors worldwide
• S-Diverse: new automotive software marketplace partnership platform
• Connects automotive manufacturers with embedded software producers
• Spryker Business Intelligence powered by Amazon QuickSight launched
• Commerce becoming more intelligent than traditional repeat purchases
• Complex equipment buyers don't purchase MRI machines weekly
• Platform provides insights into customer portal navigation patterns
• Combines commerce data with search, CRM, competitive intelligence
• Helps merchants identify revenue optimization signals from noise
• Business intelligence integrated directly within Spryker platform
• Customers should evaluate platform's future scalability and flexibility
• Revenue optimization requires understanding what metrics to improve
• Easy-to-use data analysis prevents information overload problems
• QuickSight's GenAI capabilities enable faster executive decision-making
• AWS partnership provided cost optimization and innovation confidence
• Elena initially viewed AWS as just hosting provider
• Building shared vision with AWS unlocked deeper collaboration
• AWS became trusted advisor for strategy and partnerships
• Generative AI enables multi-persona communication across customer types

Participants:

• Elena Leonova – Chief Product Officer, Spryker

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

ActiveFence CEO Noam Schwartz discusses how his company evolved from protecting platforms against user-generated harmful content to helping companies deploy public-facing AI safely at scale.

Topics Include:

• Noam Schwartz introduces himself as ActiveFence CEO
• Former intelligence officer specializing in open source intelligence
• Mission: protect online experiences for everyone everywhere
• Online platforms constantly hammered by various attacks
• Attacks include cybersecurity, abuse, hate speech, spam
• Companies playing endless whack-a-mole game with violations
• Need scalable solution that works across languages/formats
• Developed enterprise-grade technology for sophisticated companies
• Amazon became customer and great partner early on
• Generative AI introduction changed the game completely
• LLMs non-deterministic unlike traditional programmed chatbots
• Same input produces different outputs each time
• AI deployed in customer support, healthcare, airlines
• New risks when models speak on company's behalf
• One bad output creates legal and reputational damage
• Companies need to deploy public-facing AI safely
• Transition affects healthcare, finance, gaming, government sectors
• Building on years of user-generated content expertise
• No specific ChatGPT moment triggered their AI pivot
• ActiveFence was AI company since day one
• Model companies like Amazon, Nvidia asked for help
• Realized their expertise perfectly suited for AI safety
• Staying on top of AI developments is impossible
• Focus on customer adoption, not every new release
• Main enterprise challenge is trusting AI technology
• Unrealistic expectations for 100% accuracy from AI
• Most companies will license existing models, not build
• Security solutions remain independent like traditional cybersecurity

Participants:

• Noam Schwartz – CEO and Co-Founder, ActiveFence
• Ofer Oringher – Software and Technology Account Manager, AWS

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Jeff Moncrief discusses Sonrai Security's Cloud Permissions Firewall, and the best practices for using AI-powered summaries and orchestration to ensure security at all points.

Topics Include:

• Jeff Moncrief introduces Sonrai Security and Cloud Permissions Firewall
• Focus on achieving least privilege access in AWS quickly
• Lightweight orchestration layer secures IAM from inside out
• Eliminates need to write hundreds of individual policies
• Customers struggle with identity risk in CNAP/CSPM tools
• Generative AI adoption driving top security use cases
• Bedrock and AI agents mentioned daily by customers
• Product managers should consider underlying platform security risks
• AI models have control over infrastructure they run on
• Identity is fundamental infrastructure enabling AWS AI models
• Sonrai uses Bedrock capability inside Cloud Permissions Firewall
• Just-in-time access provides temporary, time-boxed AWS access
• Bedrock generates session summaries from audit logs automatically
• Plain English insights show what happened during sessions
• Session summaries improve audit compliance and incident response
• Customer with 1000 accounts manually deployed service controls
• Friday afternoon deployment caused very bad weekend disaster
• Policy inheritance issues broke child accounts and OUs
• Planning and orchestration essential for scaling AI security
• Sonrai platform built 100% cloud-native on AWS
• Coordinates service control policies and resource control policies
• Just-in-time access relies on IAM Identity Center
• Participates in ISV Accelerate and AWS Marketplace
• Security best practices start with identity as foundation
• "Hackers don't hack, they just log in" philosophy
• Eliminate standing privileges with just-in-time access patterns
• Restrict AI services by user, location, and account
• Review over-permissioned or inactive third-party vendor access
• Actionable insights through useful logging and AI summarization
• Future focus on protecting new services and permissions

Participants:

• Jeff Moncrief – Field CTO & Director of Sales Engineering, Sonrai Security

Links:

• Website – Sonraisecurity.com
• AWS Marketplace – Sonrai Security

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Akanksha Bilani of Intel shares how businesses can successfully adopt generative AI with significant performance gains while saving on costs.

Topics Include:

• Akanksha runs go-to-market team for Amazon at Intel
• Personal and business devices transformed how we communicate
• Forrester predicts 500 billion connected devices by 2026
• 5,000 billion sensors will be smartly connected online
• 40% of machines will communicate machine-to-machine
• We're living in a world of data deluge
• AI and Gen AI help make data effective
• Goal is making businesses more profitable and effective
• Various industries need Gen AI and data transformation
• Intel advises companies as partners with AWS
• Three factors determine which Gen AI use cases adopt
• Factor one: availability and ease of use cases
• How unique and important are they for business?
• Does it have enough data for right analytics?
• Factor two: purchasing power for Gen AI adoption
• 70% of companies target Gen AI but lack clarity
• Leaders must ensure capability and purchasing power exist
• Factor three: necessary skill sets for implementation
• Need access to right partnerships if lacking skills
• Intel and AWS partnered for 18 years since inception
• Intel provides latest silicon customized for Amazon services
• Engineer-to-engineer collaboration on each processor generation
• 92% of EC2 runs on Intel processors
• Intel powers compute capability for EC2-based services
• Intel ensures access to skillsets making cloud alive
• AWS services include Bedrock, SageMaker, DLAMIs, Kinesis
• Performance is the top three priorities for success
• Not every use case requires expensive GPU accelerators
• CPUs can power AI inference and training effectively
• Every GPU has a CPU head node component

Participants:

• Akanksha Bilani – Global Sales Director, Intel

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Victoria Chin of Asana and Michael Horn of AWS demonstrate how Amazon Q integrates with Asana to enable AI-powered workflows while dramatically reducing manual work and improving cross-functional collaboration.

Topics Include:

• Victoria Chin introduces herself as Asana's CPO Chief of Staff
• Michael Horn from AWS discusses customer feedback on generative AI
• AI agents limited by quality of data pulled into them
• Amazon Q Business created to analyze information and take action
• Hundreds of customers using Q Business across various industries daily
• AWS hosts most business applications, ideal for AI journey
• Amazon Q has most built-in, managed, secure data connectors available
• Q Index creates comprehensive, accessible index of all company data
• Security permissions automatically pulled in, no manual configuration needed
• Supports both structured and unstructured data from multiple sources
• Victoria returns to discuss Asana's integration with Q Index
• Billions invested in integrations, but usage still lags behind
• Teams switch between apps 1000 times daily, missing connections
• Root problem: no reliable way to track who/what/when/why
• Content platforms store work but don't manage or coordinate
• Asana bridges content and communication for effective teamwork scaling
• AI disrupting software, but questions remain about real value
• Software must provide structured framework to guide LLMs effectively
• AI needs data AND structure to separate signal from noise
• Asana Work Graph maps how work actually gets done organizationally
• Work Graph visualized as interconnected data, not rows and columns
• Most strategic work is cross-functional, requiring multiple teams collaborating
• Traditional integrations require manual setup and knowing when to use
• Q Index gives Asana access to 40+ different data connectors
• Users can ask questions, get answers with cross-application context
• AI Studio enables no-code building of workflows with AI agents
• Product launch example shows intake, planning, execution, and reporting stages
• AI can surface relevant documents, research, and updates automatically
• Chat is tip of iceberg; real power comes from embedded workflows
• Integration evolves from feature-level to AI-powered product-level connections

Participants:

• Victoria J. Chin – Chief of Staff / Product Strategy, AI, Asana
• Michael Horn – Principal Head of Business Development – Artificial Intelligence & Machine Learning, AWS

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

AWS leaders commemorate the podcast's 100th episode while looking ahead to expanded coverage of technology partners and continued focus on generative AI, modern data strategies, agentic AI solutions and more!

Topics Include:

• Episode 100 celebrates milestone of AWS software companies podcast
• Weekly podcast shares ISV stories, best practices, guidance
• Today features AWS leader thoughts on ISV community
• Arym Diamond heads North America data and AI sales
• Specialist team helps win deals, create happy customers
• ISV customers do cutting-edge work on AWS platform
• ISVs create force multiplier effect for entire company
• Building community through podcast video and audio content
• Kristen Backeberg leads global ISV partner marketing at AWS
• Podcast featured 157 ISV leaders from 121 companies
• Reached over 30,000 listeners across 90+ countries worldwide
• ISV partners drive cloud innovation across all industries
• AWS supports growth from startups to enterprise leaders
• APN network designed to help partners succeed, scale
• Olawale Oladehin directs ISV solutions architecture in North America
• Podcast shares customer insights, journeys, and innovations
• AWS technology continues evolving to meet customer needs
• Carol Potts leads North America ISV sales at AWS
• Podcast started less than two years ago
• First episode titled "Data the Engine for Growth"
• Customer obsession drives everything AWS does for ISVs
• Deep collaboration focused on joint ISV success partnerships
• Vishal Sanghvi heads ISV marketing for North America
• ISVs face pressure delivering products at generative AI pace
• Modern data strategy foundational for ISV product success
• Favorite episodes include Snowflake, Wiz, Coupang discussions
• AWS offers programs for every ISV persona type
• Future episodes focus on generative AI, cybersecurity, data
• Agentic AI becoming important for production phase evolution
• Podcast expanding scope to include technology partners

Participants:

• Kristen Backeberg – Director, Global ISV, Solutions Enterprise and Alliance Partner Marketing, Amazon Web Services
• Arym Diamond – Director, US ISV Specialists, Amazon Web Services
• Olawale Oladehin – Director, ISV, Solutions Architecture, North America, Amazon Web Services
• Carol Potts – GM, ISV Sales Segment, North America, Amazon Web Services
• Vishal Sanghvi - Head of ISV Field Marketing, North America, Amazon Web Services

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Executive leaders from UneeQ and Zeta Global discuss the revolutionary impact of AI technologies that enable enhanced customer experiences and improved sales performances.

Topics Include:

• Dave Cristini introduces panel on AI in advertising and marketing.
• Panel explores personalized experiences at scale with privacy focus.
• UneeQ creates AI-powered digital humans for brand interactions.
• Zeta Global uses AI to optimize customer messaging.
• LLMs combined with traditional ML empowers marketers to create models.
• Marketers can now build models without needing data scientists.
• AI agents integrated into systems can take action, not just respond.
• Agent chaining orchestrates sophisticated marketing actions automatically.
• AWS Bedrock provides tools to shape AI marketing future.
• Hyper-personalization becoming more achievable through AI automation.
• Ethics requires authenticity in brand AI representation.
• Transparency about data usage builds customer trust.
• Win-win approach: AI should augment teams, not just reduce costs.
• Integration difficulties remain a major challenge for AI implementation.
• AI agents have limited context windows and memory.
• Solution: Create specialized agents with persistent viewpoints.
• Companies need strong integration capabilities before implementing AI.
• Privacy regulations impact AI use in global marketing.
• Highly regulated industries require careful AI implementation strategies.
• Generative AI creates compliance challenges with unpredictable outputs.
• Digital humans eliminate judgment, revealing new customer insights.
• Banking clients discovered customers didn't understand financial terminology.
• Zeta improved onboarding with AI agents for data mapping.
• AI data mapping increased NPS scores and accelerated monetization.
• CMOs and CIOs increasingly collaborating on AI initiatives.
• Tension exists between marketing (quick wins) and IT (security).
• Strategic alignment with approved infrastructure enables scaling AI solutions.
• CEOs have critical role in aligning AI goals across departments.
• Internal AI use case: practicing sales with digital humans.
• Sales teams achieved 500% higher sales through AI role-playing.

Participants:

• Danny Tomsett – Chief Executive Officer, UneeQ
• Roman Gun – Vice President, Product, Zeta Global
• David Cristini – Director, ISV Sales, North America – Business Applications, AWS

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Ash Pembroke, Portfolio CTO of Caylent, discusses the critical balance of data accuracy in the era of Gen AI for the benefit of boosting innovation.

Topics Include:

• Ash Pembroke, Portfolio CTO of Caylent, self-identifies as a "recovering data scientist."
• Caylent is an AWS native services company.
• Data quality remains an issue despite Gen AI.
• Contrasts legalism versus mysticism in data quality.
• Legalism: accurate data when applications need it.
• Mysticism: insights that help decision-making.
• Traditional data foundations approach is being challenged weekly.
• Gen AI developments force rethinking of solution architectures.
• Teams share solutions through giant Slack threads.
• Example: Vector databases questioned after model context protocol.
• Still do traditional data assessments, but stay flexible.
• Integration and data processing constantly get abstracted.
• Data strategy equals architecture strategy equals business strategy.
• Traditional approach: standardize data across engineering teams.
• New approach: allow business users to innovate.
• Bring valuable techniques back to the organization.
• Case study: North Sea wind turbine alerts.
• Initially seen as data quality issue, revealed new predictive failure signal.
• Gen AI enables local experimentation by business users.
• Blurring lines between enterprise enablement and software building.
• BrainBox AI case study: energy optimization across buildings.
• Architecture decisions impact ability to scale products.
• Work with business edges rather than looking for patterns.
• Gen AI can process information from these working groups.
• Think about data as a product, not asset.
• Redimensionalize dependencies across your organization.
• Now's a good time to attack data quality.
• New tools help visualize complexity across organizations.

Participants:

·        Ash Pembroke – Portfolio CTO, Caylent

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

New Relic's Head of AI and ML Innovation, Camden Swita discusses their four-cornered AI strategy and envisions a future of "agentic orchestration" with specialized agents.

Topics Include:

• Introduction of Camden Swita, Head of AI at New Relic.
• New Relic invented the observability space for monitoring applications.
• Started with Java workloads monitoring and APM.
• Evolved into full-stack observability with infrastructure and browser monitoring.
• Uses advanced query language (NRQL) with time series database.
• AI strategy focuses on AI ops for automation.
• First cornerstone: Intelligent detection capabilities with machine learning.
• Second cornerstone: Incident response with generative AI assistance.
• Third cornerstone: Problem management with root cause analysis.
• Fourth cornerstone: Knowledge management to improve future detection.
• Initially overwhelmed by "ocean of possibilities" with LLMs.
• Needed narrow scope and guardrails for measurable progress.
• Natural language to NRQL translation proved immensely complex.
• Selecting from thousands of possible events caused accuracy issues.
• Shifted from "one tool" approach to many specialized tools.
• Created routing layer to select right tool for each job.
• Evaluation of NRQL is challenging even when syntactically correct.
• Implemented multi-stage validation with user confirmation step.
• AWS partnership involves fine-tuning models for NRQL translation.
• Using Bedrock to select appropriate models for different tasks.
• Initially advised prototyping on biggest, best available models.
• Now recommends considering specialized, targeted models from start.
• Agent development platforms have improved significantly since beginning.
• Future focus: "Agentic orchestration" with specialized agents.
• Envisions agents communicating through APIs without human prompts.
• Integration with AWS tools like Amazon Q.
• Industry possibly plateauing in large language model improvements.
• Increasing focus on inference-time compute in newer models.
• Context and quality prompts remain crucial despite model advances.
• Potential pros and cons to inference-time compute approach.

Participants:

• Camden Swita – Head of AI & ML Innovation, Product Management, New Relic

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Jon Yoo, CEO of Suger, shares how his company automates the complex & challenging workflows of selling software through cloud marketplaces like AWS.

Topics Include:

• Jon Yoo is co-founder/CEO of Suger.
• Suger automates B2B marketplace workflows.
• Handles listing, contracts, offers, billing for marketplaces like AWS.
• Co-founder previously led Confluent's marketplace enablement product.
• Confluent had 40-50% revenue through cloud marketplaces.
• Required 10-20 engineers working solely on marketplace integration.
• Engineers prefer core product work over marketplace integration.
• Product/engineering leaders struggle with marketplace deployment requirements.
• Marketplace customers adopt without marketing, creating unexpected management needs.
• Version control is challenging for marketplace-deployed products.
• License management through marketplace creates engineering challenges.
• Suger helps sell, resell, co-sell through AWS Marketplace.
• Marketplace integration isn't one-time; requires ongoing maintenance.
• Business users constantly request marketplace automation features.
• Suger works with Snowflake, Intel, and AI startups.
• Data security concerns drive self-hosted AI deployments.
• AI products increasingly deploy via AMI/container solutions.
• AI products use usage-based pricing, not seat-based.
• Usage-based pricing creates complex billing challenges.
• AI products are tested at unprecedented rates.
• Two deployment options: vendor cloud or customer cloud.
• SaaS requires reporting usage to marketplace APIs.
• Customer-hosted deployment simplifies some billing aspects.
• Marketplaces need integration with ERP systems.
• Version control particularly challenging for AI products.
• Companies need automated updates for marketplace-deployed products.
• License management includes scaling up/down and expiration handling.
• Suger aims to integrate with GitHub for automatic updates.

Participants:

·        Jon Yoo – CEO and Co-founder, Suger

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

SentinelOne's Ric Smith shares how Purple AI, built on Amazon Bedrock, helps security teams handle increasing threat volumes while facing budget constraints and talent shortages.

Topics Include:

• Introduction of Ric Smith, President of Product Technology and Operations
• SentinelOne overview: cybersecurity company focused on endpoint and data security
• Customer range: small businesses to Fortune 10 companies
• Products protect endpoints, cloud environments, and provide enterprise observability
• Ric oversees 65% of company operations
• Purple AI launched on AWS Bedrock
• Purple AI helps security teams become more efficient and productive
• Security teams face budget constraints and talent shortages
• Purple AI helps teams manage increasing alert volumes
• Top security challenge: increased malware variants through AI
• AI enables more convincing spear-phishing attempts
• Identity breaches through social engineering are increasing
• Voice deepfakes used to bypass security protocols
• Future threats: autonomous AI agents conducting orchestrated attacks
• SentinelOne helps with productivity and advanced detection capabilities
• SentinelOne primarily deployed on AWS infrastructure
• Using SageMaker and Bedrock for AI capabilities
• Best practice: find partners for AI training and deployment
• Customer insight: Purple AI made teams more confident and creative
• AI frees security teams from constant anxiety
• SentinelOne's hyper-automation handles cascading remediation tasks
• Multiple operational modes: fully automated or human-in-the-loop
• Agent-to-agent interactions expected within 24 months
• Common misconception: generative AI is infallible
• AI helps with "blank slate problem" providing starting frameworks
• AI content still requires human personalization and review
• AWS partnership provides cost efficiency and governance benefits

Participants:

·        Ric Smith – President – Product, Technology and Operations, SentinelOne

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Sam Gantner, Chief Product Officer of Nexthink, reveals how DEX is moving IT from reactive firefighting to proactive problem prevention and transforming enterprise productivity.

Topics Include:

• DEX stands for Digital Employee Experience
• DEX eliminates IT issues preventing employee productivity
• Shifts IT from reactive to proactive problem-solving
• Employees often serve as IT problem alerting systems
• Best IT is transparent to employees
• DEX solves device sluggishness and slow application issues
• Network problems consistently appear across organizations
• IT teams often lack visibility into employee experiences
• Many organizations waste money on unused software licenses
• DEX Score measures comprehensive employee IT experience
• Surveys capture subjective aspects of technology experience
• Reduction of actual problems differs from ticket reduction
• Nexthink uses lightweight agents on employee devices
• Browser monitoring essential as browsers become application platforms
• Employee engagement metrics capture real-time feedback
• Nexthink rebuilt as cloud-native platform using AWS services
• Company deploys across 10+ global AWS regions
• 30% of engineering resources dedicated to AI development
• One customer eliminated 50% of IT tickets
• Another recovered 37,000 productivity hours worth $3M annually
• A third saved $1.3M by identifying unused licenses
• AI implementation requires dedicated employee training
• Good AI now better than perfect AI never
• Technology adoption is the next DEX frontier
• Digital dexterity becoming critical for maximizing IT investments

Participants:

• Samuele Gantner – Chief Product Officer, Nexthink

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/