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Why is the Cybersecurity AI Forward Deployed Engineer emerging as one of tech’s most critical roles, commanding compensation of up to $235,100? 

In this episode, we explore why agentic security has reached an urgent turning point, with 88% of organisations confirming or suspecting an AI agent security incident in the past 12 months. 

As enterprises rapidly grant autonomous agents live credentials and production database access, market demand for forward-deployed talent has surged by 1,165% year-over-year.

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Episode Breakdown:

• From Palantir to Client Environments: How the forward-deployed model evolved, and how defending client systems on-site differs fundamentally from standard in-house AI security.
• Beyond Basic Prompt Injection: A deep dive into the real agentic attack surface, including memory and RAG poisoning, tool-calling privilege escalation, workspace escapes, and protocol vulnerabilities across A2A, ACP, and UCP.
• The $235K Compensation Driver: What is behind top-of-market salaries at firms like Accenture, and why specialised agentic red teaming provides a 20% to 30% pay multiplier.
• The Steep Employer Hiring Bar: Breaking down the non-negotiable requirements, including 8+ years of engineering experience, deep cloud IAM knowledge, and daily fluency with agentic coding environments.
• The 6-Step Transition Roadmap: The structured pathway to entering the field—from hardening agent runtimes to publishing verified deployment artifacts and securing client-facing reps.

Whether you are an experienced security engineer or an architect seeking to secure live autonomous workflows, this breakdown offers an essential guide to the industry's newest frontline role

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we explore key insights from the State of Product Security Roles: 2026 Research Report. As AI-generated code and autonomous AI agents transform software production, product security is shifting from narrow application scanning to full lifecycle protection. 

We break down how top organizations are navigating severe talent shortages, structuring modern security teams, and securing the AI supply chain.

What You’ll Learn in This Episode:

• The 2026 Skills Shortage: Why 59% of security leaders report a critical or significant skills gap in cloud-native and AI/LLM security, and how the industry benchmark holds at ~1 dedicated security staff per 100 developers.
• Emerging AI Security Roles: How autonomous agents are creating specialized roles like AI Agent Security Engineer with 78% of CISOs already running dedicated AI agent security teams.
• Salary Benchmarks & High-Demand Roles: Breakdown of market compensation, from average US totals of $185,700 to Big Tech IC medians of $281,000–$335,000+, with AI/ML Security leading specialist base pay at $172,000.
• 6 Core Security Team Models: A comparative look at Centralized, Federated, Embedded, Hub-and-Spoke, Platform, and Hybrid organizational structures.
• Career Pathways: Transition guides for Software Engineers, SREs, Pentesters, and AppSec Analysts entering product security.
• The 2030 Job Market Forecast: How AI automation will move daily work away from manual scan triage toward reviewing AI-generated threat models and defining agent containment guardrails.
• Certifications & Training Bundles: Essential certification tracks; including CDP, CCSE, CCNSE, CAISP, and CAASE; and high-value bundle options for container, cloud-native, and agentic AI security
  
  

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Certified AI Security Professional (CAISP), CompTIA SecAI+, and ISACA AAISM represent the premier credentials competing for cybersecurity professionals in 2026 as demand for AI security roles surges. In this episode, we break down how these certifications stack up for engineers looking to defend real-world production stacks against novel AI vulnerabilities. 

With job postings requiring AI security skills doubling and red-teaming roles projected to grow by 35%, choosing the right certification pathway has never been more critical for your career trajectory.

CAISP, SecAI+, and Microsoft Azure AI Security highlight the fundamental shift between traditional theoretical assessments and modern practical lab exams.

Most conventional cybersecurity exams rely strictly on multiple-choice questions (MCQs) that evaluate recall rather than execution, leaving a major gap in testing whether an engineer can handle live system attacks. 

We discuss how practical lab environments bridge this gap by requiring candidates to execute prompt injections, exploit the OWASP LLM Top 10, poison training pipelines, and implement MITRE ATLAS defenses before patching compromised systems.

AAISM by ISACA and CAISP by Practical DevSecOps showcase opposing approaches to prerequisite gatekeeping and career mapping. While AAISM locks enrollment behind existing CISM or CISSP credentials, CAISP requires no prerequisite credential, opening doors directly for AppSec engineers, penetration testers, cloud professionals, and DevSecOps practitioners. We evaluate which certifications best support aspiring AI security engineers aiming for roles with average US salaries ranging from $152,773 to $187,975, contrasting technical hands-on roles with governance and audit pathways like AAISM or AIGP.

Practical DevSecOps CAISP ($1,099), CompTIA SecAI+ ($359), and ISACA AAISM ($459–$599) illustrate stark differences in lifetime value versus recurring annual fee structures. CAISP offers a single payment structure with lifetime validity and no recertification fees while awarding 36 CPE points. Conversely, SecAI+ expires after three years, and AAISM requires ongoing annual maintenance fees alongside yearly CPE submissions. 

Tune in to discover which certification offers the strongest return on investment and practical skill verification for 2026

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Ready to secure your career in 2027? Enroll in the Certified AI Security Professional (CAISP) course today. This vendor-neutral, fully lab-based certification from Practical DevSecOps is trusted by global organizations like IBM, Accenture, and PwC. It offers over 30 browser-based labs and a rigorous six-hour practical exam to prove you can actively secure real AI architectures.

In this episode of the podcast, we dive into the exact skills you need to stay ahead of the curve as AI-driven systems take over production environments. We discuss:

The Salary Premium: Why AI security skills command exceptionally high compensation, with the average US AI security engineer earning $152,773 per year; well above traditional security analyst medians.

Active Defence over Passive Learning: Why passive learning fails and why practising real attacks and defences in hands-on labs is the only way to build verifiable, job-ready skills.

Prompt Injection Mitigation: Practical steps to secure your applications against prompt injection, the undisputed number-one vulnerability on the OWASP Top 10 for LLM Applications.

Internal Career Promotion: How to fast-track your career growth by taking on AI projects in your current organisation and becoming the team’s indispensable, go-to AI security expert.

Governing Shadow AI: How to track down unsanctioned LLM use and establish robust governance using established industry frameworks like the NIST AI Risk Management Framework and ISO/IEC 42001

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In this episode, we dive deep into the field of AI security to compare the top three AI red teaming certifications of 2026: Practical DevSecOps' Certified AI Security Professional (CAISP), OffSec's AI Red Teamer (OSAI/AI-300), and SANS's SEC536 (Adversarial AI). 

Whether you are an experienced pentester or an AppSec engineer looking to pivot into securing LLMs, we break down exactly what you get for your money, contrast the intensity of their practical labs against their exam formats, and analyze which credential offers the strongest career and salary growth potential.

What We Cover in This Episode:

The Core Contenders Explained: We detail the unique philosophy of each program. Learn why CAISP ($1,099) is built for those needing day-one defensive and offensive AI skills, why OSAI ($1,749+) requires a solid foundation in offensive fundamentals, and where SEC536 ($2,629–$3,505) sits as a high-intensity, instructor-led SANS experience.

Hands-On Lab Reps vs. Exam Formats: We contrast the actual practical training time against the pressure of the testing center:

CAISP: Features 30+ guided browser-based labs. The exam consists of 5 practical challenges completed over 6 hours, with a 24-hour window to write and submit a professional report.

OSAI: Employs OffSec’s rigorous "Try Harder" method with modular labs, leading into a grueling, fully proctored 24-hour practical exam designed to simulate a real-world enterprise compromise.

SEC536: Provides 10 highly current labs (covering cutting-edge techniques like Model Context Protocol tool abuse) but has no certification exam attached yet due to its beta status.

Salary Growth & Career Trajectory: We discuss the massive financial upside of entering the AI security space. With US AI/LLM Red Teamers commanding $160,000 to $280,000 and AI Security Engineers bringing in up to $340,000, we analyze which cert offers the lowest barrier to entry to help you bypass the standard AppSec salary ceiling ($120,000 to $230,000)

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In this episode, we break down the 2026 application security space to help you decide which training path actually moves the needle for your career. 

The central theme of our discussion is a critical choice every professional faces: are you looking to build hands-on skills you can use on Monday morning, or are you looking for a credential to bolster your resume?

We explore the certifications that hiring managers and AppSec leads are prioritizing this year, with a heavy focus on practical, lab-based learning over traditional multiple-choice exams.

Key topics covered in this episode include:

Mastering Threat Modeling: We look at the Certified Threat Modeling Professional (CTMP) and the importance of moving beyond theory to master frameworks like STRIDE and PASTA in live environments.

Our goal is to help you prioritize skills over certificates, ensuring that the time and money you invest in your education results in the ability to find and fix real-world vulnerabilities

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As organizations rapidly adopt the Model Context Protocol (MCP) to connect AI agents to production data and internal tools, a massive security gap has emerged.

 In this episode, we explore why MCP security has become one of the fastest-rising hiring signals in the cybersecurity industry and how mastering these skills can dramatically increase your salary potential.

Featured Resource: Learn more about the Certified MCP Security Expert (CMCPSE) course by Practical DevSecOps, focusing on attacking, assessing, and hardening MCP servers through browser-based labs.

In this episode, we cover:

The MCP Security Gap: Most teams have adopted MCP without knowing how to defend it. We discuss why this creates a unique "early mover" advantage for security professionals.

Elite Salary Data: We break down the 2026 salary bands, where AI Security Engineers are earning between $152,000 and 210,000 and LeadAISecurityArchitects arer eaching 280,000 and up.

High-Value Skills Employers Want: Learn why practical, hands-on skills like tool poisoning, runtime prompt injection, and supply chain security are pulling the strongest premiums compared to theoretical knowledge.

Resume Mastery: Discover how to transform a "weak" resume into a "strong" one by using specific metrics and named attack types that prove you can harden real-world AI pipelines.

A Fast-Track to Certification: We introduce the Certified MCP Security Expert (CMCPSE) pathway, which uses 30+ hands-on labs to make professionals job-ready in approximately two months.

MCP security is currently in a rare window where demand far outstrips supply. This episode provides the roadmap for security engineers, DevSecOps professionals, and architects to walk into high-paying roles before these specialized skills become normalized.

Tune in to discover how to secure the AI integration layer and your next major career jump.

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The high-speed adoption of AI agents has a new "default" language: The Model Context Protocol (MCP).

 While MCP provides a seamless way for Large Language Models (LLMs) to interact with tools, databases, and APIs, it has also introduced significant new attack surfaces. In this episode, we break down the MCP Security Best Practices: 2026 Playbook to help security engineers and developers move beyond theory and into hardened, production-ready defense.

What’s at Stake?

Recent research has exposed a "wild west" of MCP implementations. Security scans of nearly 2,000 publicly accessible MCP servers found that every single verified instance granted access to internal tool listings without any authentication. Furthermore, many servers remain bound to all interfaces (0.0.0.0), inadvertently allowing arbitrary code execution.

Key Topics Covered:

The 2026 Threat Model: We explore the six critical attack patterns targeting AI agents, including Confused Deputy attacks, Tool Poisoning (where a malicious server injects prompts via tool descriptions), and SSRF during OAuth discovery.

The 10 Non-Negotiable Best Practices: A deep dive into the mandatory security controls for 2026, including:
OAuth 2.1 Integration: Why strict token audience validation is now the required standard for non-stdio servers.
Sandboxing & Least Privilege: Using containerization and syscall filtering (seccomp/gVisor) to isolate tool execution.
Human-in-the-Loop: Why high-risk actions (deleting data or sending money) must default to "deny" without explicit user approval.
Credential Management: Moving away from static secrets in environment variables and toward short-lived, vaulted tokens.

Supply Chain Integrity: The importance of cryptographic signing, version pinning, and SBOM tracking for MCP server packages.

The Quick Audit Checklist: A 10-point "Go/No-Go" list for teams ready to take their MCP servers live.

Featured Certification: CMCPSE

We also discuss the shift toward hands-on training. The Certified MCP Security Expert (CMCPSE) program is highlighted as the gold standard for 2026, focusing on browser-based labs where professionals attack and defend real MCP code rather than memorizing theory.

Whether you are building autonomous agents or securing the infrastructure they run on, this episode provides the research-backed insights you need to ship safely in an agentic world.

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In this episode, we dive into the "MCP Security Risk Framework for Enterprise CISOs", exploring how to secure AI agents against the unique threat of agentic amplification. 

Ready to lead your organisation’s AI security strategy? Upskill your team with the Certified MCP Security Expert (CMCPSE) course, featuring over 30 hands-on labs to attack, defend, and pen test MCP servers

Unlike standard API risks, which are bounded to specific data or functions, MCP risks are non-linear because a single compromised connection can cascade across every capability an agent holds. This "capability multiplier" effect means a compromised agent could autonomously read emails, execute code, and write to databases.

We break down the Four-Domain MCP Risk Taxonomy used to assess these threats:

Domain 1: Identity & Access Risk – Focusing on identity management and overpermissioned tool scopes.

Domain 2: Data Access & Exfiltration Risk – Addressing the risk of sensitive data being leaked through injected instructions.

Domain 3: Operational Integrity Risk – Mitigating unintended actions like unauthorised write operations or communications.

Domain 4: Supply Chain & Third-Party Risk – Managing risks from third-party tool vendors and manifest tampering.

For CISOs looking to bridge the gap between fast-moving business units and security, we discuss a ninety-day implementation plan built on the MCP Security Maturity Model.

Days 1–30: Establish a baseline by identifying all agents, assessing authentication, and assigning ownership.

Days 30–60: Deploy foundational controls like authentication and logging, and brief the board on the current posture.

Days 60–90: Build toward a "Managed" state by implementing session-scoped authorisation and running red team injection exercises.

We also provide a strategy for board-level reporting, framing risks in terms of data exposure, operational integrity, and third-party trust rather than just technical severity. 

You will learn the key signals for moving between maturity tiers; such as transitioning from having no audit logs (Tier 1) to implementing automated manifest drift detection and employing staff holding Certified MCP Security Expert (CMCPSE) credentials (Tier 4).

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In this episode, we dive deep into the OWASP MCP Top 10, the first official security framework dedicated to the Model Context Protocol (MCP). 

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Get hands-on experience in tool poisoning labs, OAuth 2.1 hardening, MCP red-teaming, and shadow server detection. This is the definitive certification to secure agentic AI in 2026.

This framework addresses a critical shift in the threat model: as agentic AI moves into production, agents no longer rely on a small, hardcoded toolset but instead discover tools at runtime from any reachable server. This transition has turned every MCP server into a high-stakes trust boundary.

We explore the sobering reality of 2026 security, where over 30 CVEs targeting MCP were filed in the first two months of the year alone; with shell injections making up 43% of those attacks. We break down the most critical risks, including:

MCP01 (Token Mismanagement): How attackers exploit hard-coded credentials and long-lived tokens through prompt injection.

MCP03 (Tool Poisoning): The danger of malicious instructions hidden in tool descriptions that the model reads, but the user never sees.

MCP05 (Command Injection): The leading attack pattern in 2026, where agents build dangerous shell commands from untrusted input.

MCP09 (Shadow MCP Servers): The risk of rogue servers impersonating trusted ones to hijack tool calls.

Finally, we discuss a week-by-week prioritization strategy to help security teams close the most dangerous gaps first, starting with token hygiene and OAuth 2.1 implementation. With a massive skills gap currently facing the industry, mastering these categories is no longer optional for AppSec engineers.

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Transitioning from a technical engineer to an Application Security (AppSec) Manager is rarely a straight line; it requires balancing technical expertise with the strategic mindset needed to lead a department. 

In this episode, we break down the realistic 5–8 year career path for aspiring leaders, moving from hands-on development to managing end-to-end security programs. We dive into the "messy reality" of the role, where you must act as the bridge between fast-moving engineering teams and CTOs focused on the bottom line.

Learn why the Security Champion phase is the most critical step in your journey, helping you develop the "influence without authority" and communication skills that define successful managers. 

We also explore the KPIs that actually matter to leadership—like Mean Time to Remediate (MTTR) and developer adoption rates—and the essential technical skills in SAST, DAST, and threat modeling you'll need to stay sharp. Whether you are a developer looking to pivot or a senior engineer ready for the manager's seat, this episode provides a step-by-step blueprint for running a modern AppSec program.

Ready to accelerate your career? The transition from individual contributor to security leader happens in the Security Champion phase. Don't just find vulnerabilities—learn to build the systems that fix them. Enroll in the Certified Security Champion (CSC) course today for just $599. Gain hands-on experience with 40+ guided exercises in secure CI/CD pipelines, SAST/SCA tooling, and threat modeling to prove you’re ready for the next level.

[Enroll in the Certified Security Champion Course Now]

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Welcome to The DevSecOps Edge, the podcast dedicated to helping you become one of the top 1% of cybersecurity engineers in the industry. In a world where APIs account for 80% of internet traffic and 94% of web breaches start at the API layer, staying ahead of the curve isn't just an advantage—it's a necessity.

In our featured episodes, we tackle the biggest questions facing security professionals today. Our deep-dive comparison, "CDP vs. ECDE: Which DevSecOps Certification Is Worth Your Time?", breaks down the critical differences between the Certified DevSecOps Professional (CDP) and EC-Council’s Certified DevSecOps Engineer (ECDE). We explore why seasoned practitioners are moving away from traditional multiple-choice exams (MCQs) in favour of hands-on, practical assessments.

What you’ll learn in this series:

Practical vs. Theoretical: Why the CDP’s 6-hour practical exam and 100+ browser-based labs are considered the gold standard for proving real-world capability compared to the 4-hour MCQ format of the ECDE.

Career & Salary Impact: A look at the data showing that CDP holders frequently see a 15–20% salary increase within 12 months of certification, with senior roles in the US reaching average salaries of $174,900.

The Toolset of 2026: How to master the tools engineers actually use, including GitLab CI, GitHub Actions, OWASP ZAP, and DefectDojo.

Specialised Security Frontiers: Briefings on emerging tech, including AI Security (CAISP), Cloud-Native Security (CCNSE), and Software Supply Chain Security (CSSE).

Lifetime Value: The benefits of a lifetime credential with no renewal fees or expiry-driven recertification cycles.

This podcast is designed for Security Engineers, DevOps Engineers, Application Security Analysts, and Penetration Testers who want to demonstrate real-world pipeline security skills rather than just theoretical knowledge. Hosted by industry experts and drawing on insights from Practical DevSecOps—a specialist provider trusted by organisations like IBM, PwC, and Accenture—we provide research-backed insights you can actually use.

Stop memorising study guides and start building secure CI/CD pipelines. Subscribe to The DevSecOps Edge and take the next step in your professional journey

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Is your API documentation telling the truth? In this episode, we dive into the uncomfortable reality that API documentation is often a "lie" because of the gap between Swagger files and what is actually running in production. We explore how attackers exploit this gap using advanced fuzzing techniques and JWT manipulation, and why a centralised defense strategy using Kong API Gateway is the only way to effectively secure modern microservices.

Key Topics Covered:

The JWT Illusion: We debunk the myth that JSON Web Tokens (JWTs) are inherently secure. Because JWTs are encoded rather than encrypted, anyone who intercepts a token can read its payload in seconds. We discuss how attackers exploit servers that "trust" whatever a token says without a second opinion, leading to unauthorized admin access through signature flaws or "alg: none" exploits.

The Power of API Fuzzing: Learn how attackers use the predictability of REST naming conventions to guess hidden routes. We highlight the use of high-speed tools like ffuf to fire tens of thousands of requests at a server to map out an application's shadow attack surface.

The 405 Signal: Discover the "single most useful technique" in API discovery: the 405 Method Not Allowed response. While many security teams ignore this, it tells an attacker exactly where hidden admin or registration endpoints exist, even if they are unauthorized to access them at that moment.

The Microservice Security Trap: Why writing security logic into every individual microservice is a "losing strategy". We explain how this creates a patchwork of inconsistent controls where one weak, legacy service can compromise the entire perimeter.

Centralising Defense with Kong Gateway: We break down how Kong acts as a gatekeeper, ensuring no request reaches the backend without passing through global security controls. Learn how to use rate limiting to kill automated attacks and the critical importance of disabling direct access to backend server IP addresses.

Featured Experts: This episode draws on a hands-on workshop led by Marudhamaran Gunasekaran, Principal Security Consultant, and insights from Aditya Patni, Security Research Writer at Practical DevSecOps.

Call to Action: Stop relying on optional security suggestions. If you want to build real-world API security skills, check out the Certified API Security Professional (CASP) program, which focuses on hands-on labs rather than multiple-choice theory. You can also watch the full API Security Workshop on the Practical DevSecOps YouTube channel to see these exploits and defenses in action.

Don't let an attacker find your hidden endpoints before you do.

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n this episode, we tackle the rapidly evolving landscape of artificial intelligence and the critical need for specialized security expertise. As Large Language Models (LLMs) and autonomous agents become integrated into the modern enterprise, they bring a new set of risks, including prompt injection, training data poisoning, and insecure plugin designs. 

To help you navigate your career path in this high-demand field, we provide an in-depth comparison of two premier certifications: the Certified AI Security Professional (CAISP) from Practical DevSecOps and the Advanced AI Red Teaming (OSAI) from OffSec.

What You’ll Learn in This Episode:

The Full-Spectrum Defensive Path: We explore why CAISP is the top choice for security engineers, AppSec leads, and DevSecOps professionals. Discover how it covers the full AI security lifecycle, from threat modeling with STRIDE and StrideGPT to securing AI pipelines against "poisoned pipeline" attacks.

The Offensive Specialist Path: We dive into the OffSec OSAI, a certification designed for dedicated Red Teamers. Learn about its focus on adversarial operations, Retrieval Augmented Generation (RAG) abuse, and its grueling 48-hour endurance exam.

Practical Skills for the Real World: We discuss the importance of hands-on experience. CAISP offers browser-based labs that allow you to start practicing immediately, covering essential frameworks like the OWASP LLM Top 10 and MITRE ATLAS.

Career Growth and ROI: Understand the market demand that is driving a 15-20% salary increase for professionals who transition into AI-focused roles. We also explain how digital badges from platforms like Credly can help you prove your expertise to hiring managers.

The Ultimate Comparison: We break down the key differences in exam styles—CAISP’s 6-hour practical challenge versus OSAI’s 48-hour red team engagement—to help you decide which path aligns with your professional goals.

Which Certification is Right for You? Whether you are looking to build and defend production AI systems or specialize in high-level offensive exploitation, this episode provides the roadmap you need to stay relevant. CAISP is the industry favourite for those needing versatile, job-aligned skills to manage supply chain risks with AIBOMs and model signing, while OSAI is the definitive choice for full-time penetration testers.

Join us as we break down the complexities of AI security and help you take the next step in your cybersecurity journey.

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In this episode, we dive deep into the SLSA (Supply-chain Levels for Software Artifacts) framework, the definitive standard for securing your software supply chain. With software supply chain attacks increasing by 742% between 2019 and 2022, understanding frameworks like SLSA—pronounced "salsa"—is no longer optional; it is an operational reality.

We explore the origins of SLSA, which began at Google as "Binary Authorization for Borg" before being contributed to the Open Source Security Foundation (OpenSSF) in 2021. We break down what SLSA provides: a common vocabulary for security maturity, verifiable provenance metadata, and incremental security levels that align with NIST SSDF and EO 14028 requirements.

Join us as we dissect the four SLSA security levels, from Level 0 (the default state of no provenance) to Level 3, which mandates hardened builds with isolated and ephemeral environments. We discuss how these Level 3 protections could have potentially stopped major breaches like the SolarWinds attack by preventing persistent access to build environments and isolating signing keys. We also touch on other high-profile incidents like Codecov and Log4Shell that highlight the urgent need for artifact integrity.

The episode also covers the technical mechanics of SLSA, specifically "provenance"—the tamper-evident metadata that answers who built an artifact, what sources were used, and how it was constructed. We examine the Sigstore toolchain, including Cosign, Fulcio, and Rekor, which enables the "keyless" cryptographic signing essential for modern supply chain security.

For those ready to move from theory to practice, we outline a implementation roadmap starting from Level 1 (fully scripted builds) to Level 3 (enforced verification in production), a journey that typically takes between three to six months. We also highlight the critical roles of different stakeholders, from developers signing commits to organizations establishing policy enforcement at deployment boundaries.

Finally, we address the limitations of the framework—noting that it focuses on build integrity rather than code quality or runtime security—and point you toward the Certified Software Supply Chain Security Expert (CSSE) course for those ready to master these concepts through hands-on labs.

Whether you are an AppSec engineer, a security professional, or a cybersecurity analyst, this episode provides the practical, research-backed insights you need to defend against source tampering, dependency poisoning, and provenance forgery.

Key Topics Covered:

Defining SLSA and its role in the OpenSSF.

The 742% increase in supply chain attacks and lessons from SolarWinds.

The roadmap from Level 0 to Level 3 "Hardened Builds".

The power of Sigstore and cryptographic provenance.

Common implementation mistakes, such as skipping Level 1 or ignoring verification.

How to get certified as a Software Supply Chain Security Expert.

Upgrade your security career today by mastering the framework that secures the world's most critical workloads.

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In this episode, we explore the explosive growth of the DevSecOps market, which is projected to reach between USD 8.58 billion and USD 10.88 billion by 2026. Driven by cloud-native transitions, AI integration, and intensifying regulatory pressures, the industry is witnessing a compound annual growth rate (CAGR) of up to 22.10%.

Course Page: 

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What You’ll Learn in This Episode:

• The Financial Landscape: Why DevSecOps engineering has become a high-demand career with massive salary potential. We break down the 2026 salary benchmarks, where entry-level roles average $100,000 and senior-level experts earn up to $210,000.

• The Rise of AI & Emerging Threats: How AI-generated code is expanding attack surfaces and why 75% of organizations are now using or planning to use AI/ML bots for code reviews.

• Skills That Move the Needle: Discover the high-value expertise in Kubernetes security, Terraform, Infrastructure as Code (IaC), and CI/CD automation that can lift your pay by 20-40% over traditional roles.

• Market Dynamics: A look at why North America holds a dominant 36.5% market share, fueled by federal SBOM mandates, while the Asia-Pacific region emerges as the fastest-growing market with a 22.7% CAGR.

Deep Dive into Education & Certification:

We discuss the critical importance of specialized training to stay competitive. The sources highlight essential certifications like the Certified DevSecOps Professional (CDP), which focuses on securing the SDLC, and the Certified AI Security Professional (CAISP), covering the OWASP Top 10 for LLMs and MITRE ATLAS defenses. We also examine the role of Certified Cloud Native Security Experts (CCNSE) and Threat Modeling Professionals (CTMP) in building resilient, "shift-smart" workflows.

Strategic Insights for 2026:

• The Speed vs. Risk Tradeoff: Why nearly half of development teams still deploy vulnerable code under time pressure despite achieving 60% faster release cycles.

• Vulnerability Trends: An analysis of why infrequently deployed services have 47% more outdated dependencies, often leaving them vulnerable to unpatchable CVEs.

• The Shift to Managed Services: Why organizations are increasingly turning to managed services for AI tuning and red-teaming support.

Whether you are looking to break into the field or are a seasoned professional aiming for the top 1% of cybersecurity engineers, this episode provides the research-backed insights and practical roadmaps needed to navigate the 2026 DevSecOps landscape.

Tune in to learn how to integrate security into every stage of your workflow and secure your place in this multi-billion dollar industry.

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In this episode, we dive deep into one of the most pressing financial and security threats facing organizations in 2026:

Featured Resource: If you are responsible for securing AI infrastructure, this episode highlights the technical controls covered in the Certified AI Security Professional (CAISP) course, which includes hands-on labs for defending against the OWASP Top 10 LLM vulnerabilities and mastering the MITRE ATLAS framework.

LLM Jacking. While many security discussions focus on prompt injection or model poisoning, LLM jacking is a different beast entirely—it is a direct infrastructure compromise where attackers hijack your cloud credentials to consume your expensive AI resources.

A single hijacked Large Language Model can cost an organization over $46,000 a day in fraudulent charges. We break down why this has moved from a theoretical risk to a daily reality for security architects and AI developers.

In this episode, we cover:

• Defining the Threat: Understand why LLM jacking is an infrastructure failure, distinct from model manipulation like prompt injection.

• The 3-Stage Anatomy of an Attack: We trace the attacker’s journey from the Initial Compromise (often through leaked API keys or unpatched software) to Discovery and Weaponization, where stolen access is sold or used to generate malicious content.

• The "Smoking Gun": Learn the technical indicators of compromise (IoCs), such as specific ValidationException errors in AWS Bedrock or unusual geographic spikes in API traffic.

• Real-World Case Study: We examine a fintech startup’s nightmare scenario—how a single static AWS key committed to GitHub led to a 700% cost overrun in just two weeks.

• Defense & Incident Response: From architecting Zero Trust AI pipelines to a 15-minute containment playbook, we provide actionable strategies to protect your environment.

• The Future of AI Security: Why the rising cost of model inference and the move toward proprietary, fine-tuned models make AI infrastructure a high-value target for 2026 and beyond.

Tune in to learn how to ensure security is a foundational part of your AI strategy, rather than a costly afterthought.

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In this episode, we explore the remarkable career transformation of Hiroshi Tanaka, a security veteran with 15 years of experience in offensive security, penetration testing, and red team operations. 

Despite his extensive background in a Fortune 500 company, Hiroshi realised that his ability to "break things" was no longer sufficient as his organisation transitioned towards DevOps and cloud-native development.

He shares his candid journey of overcoming the fear of becoming "irrelevant" and the challenge of preventing vulnerabilities during development rather than just finding them in production. We dive deep into the solution that changed his career trajectory: the Certified DevSecOps Professional (CDP) programme.

Key Discussion Points:

• The 60-Day Pivot: How Hiroshi transitioned from offensive security to a secure SDLC mindset through 100+ hands-on labs covering CI/CD integration, SCA, SAST, and DAST.

• Infrastructure-as-Code (IaC): Mastering the security of automated pipelines using tools like Jenkins, GitLab CI, Ansible, and Terraform.

• Tangible Results: Within 30 days of his certification, Hiroshi automated security scanning that caught 23 high-severity vulnerabilities before they reached production—issues that previously would not have been caught for months.

• The Professional ROI: The business impact of reducing deployment delays from two weeks to two days and how this pivot led to a promotion to AppSec Lead with a 40% salary increase.

Hiroshi explains how gaining technical credibility allowed him to speak the "same language" as DevOps teams, shifting his role from a quarterly auditor to a key player embedded in sprint planning.

Looking Forward: We also touch upon emerging trends for 2026, including the necessity of securing AI supply chains and data pipelines through certifications like the Certified AI Security Professional (CAISP).

Whether you are looking to master Kubernetes security, API security, or Threat Modeling, this episode serves as a comprehensive guide for any security professional or developer looking to upgrade their career and join the top 1% of cybersecurity engineers.

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AI Security Certification and Training:

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To address these challenges, the Certified AI Security Professional (CAISP) certification provides the skills needed to secure the AI supply chain and infrastructure. The course covers:

The emergence of Agentic AI represents a fundamental paradigm shift in cybersecurity. Unlike traditional, static software, agentic systems are defined by their autonomy, planning capabilities, and ability to use tools to execute multi-step goals. This shift means defenders are no longer just securing code, but rather dynamic, goal-driven entities that can be turned against their creators.

The Taxonomy of Threats

The attack surface for these agents is vast, with several critical vectors identified in the sources:

• Prompt Injection and Jailbreaking: This is the primary method for hijacking an agent. Attackers use direct injection (malicious commands fed directly) or indirect injection (poisoning data the agent processes, such as a webpage or document) to override core instructions.

• Autonomous Exploitation: A compromised agent can effectively become an autonomous hacker. It can independently scan for "one-day vulnerabilities" or execute website exploits without further human intervention.

• Multi-Agent Mayhem: When agents collaborate using protocols like MCP (Machine Communication Protocol), risks multiply. Attackers can exploit these protocols for impersonation or to coordinate multiple agents into a "digital crime syndicate" to bypass security controls.

• Unchecked Autonomy: The speed of AI operation means a minor error can escalate into a major incident before a human can intervene, making minimal oversight a critical vulnerability

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This episode focuses on how these principles fit into the DevSecOps Maturity Model (DSOMM), a structured framework that enables organisations to embed security practices from the start, ensuring that rapid delivery does not come at the cost of protection.

Ready to take the first step?

The Certified DevSecOps Professional (CDP) course is the ultimate starting point for those looking to automate security and lead organisational change. Through 100+ hands-on labs, the CDP program teaches you to build secure CI/CD pipelines using SCA, SAST, and DAST tools. You will learn to automate security gates, apply Infrastructure as Code techniques, and successfully progress an organisation from DSOMM Level 0 to Level 2. Don't just follow the trends—lead them by becoming a certified expert today

We break down the five critical security dimensions—Test and Verification, Patch Management and Design, Process, Application and Infrastructure Hardening, and Logging and Monitoring—to show how they create a multi-layered defence. 

With the global cybersecurity workforce facing a 4 million professional shortage, there has never been a more lucrative time to specialise. DevSecOps experts earn 18-28% more than traditional security roles, with certified professionals commanding an additional 12-15% salary premium.

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Secure your future in the most critical career path in tech by enrolling in the Certified AI Security Professional (CAISP) course today!

In this episode, we explore the definitive guide to the Top 10 Emerging AI Security Roles for 2026. The shift toward AI-integrated operations is not a future concern—it is happening now, and it has opened a "chasm" in the workforce that only specialised professionals can fill. 

We break down the responsibilities, required skills, and massive salary potential for the roles that will define the next decade of cybersecurity.

Key Roles Discussed in This Episode:

• AI/ML Security Engineer: The front-line soldier responsible for securing development pipelines and validating model integrity (152K–210K).

• AI Security Architect: The strategist designing secure AI ecosystems and embedding security into the MLOps lifecycle (200K–280K+).

• LLM / Generative AI Security Engineer: A specialist focused on defending Large Language Models against prompt injection and data leakage (160K–230K).

• Adversarial ML Specialist: The AI "Red Teamer" who breaks models via evasion and data poisoning to expose flaws before attackers do (160K–225K).

• AI-Powered Threat Hunter: Using AI as a weapon to analyse petabytes of data and automate incident response (140K–195K).

• AI GRC Specialist: Ensuring AI use is ethical, safe, and compliant with laws like the EU AI Act (130K–190K).

• Secure AI Platform Engineer: Building the hardened, containerised infrastructure (Kubernetes/Docker) where models are trained and deployed (150K–210K).

Why Specialise Now?

We also address the common fear: Will AI automate these jobs away? The answer is a definitive no. AI will automate tasks, not roles, making the professionals who leverage these tools 100x more effective than those who do not.

Whether you are a cybersecurity analyst looking to transition or an experienced engineer aiming for the top 1% of earners, this episode provides a clear roadmap. We discuss why Python mastery, cloud expertise (AWS/Azure/GCP), and a zero-trust mindset are the non-negotiable foundations for your new career.

Ready to start? The AI security landscape is a permanent shift in the industry. Claim your spot in this high-paying discipline by getting certified today.

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Enroll now in the Certified AI Security Professional (CAISP) course by Practical DevSecOps! This highly recommended certification is designed for the engineers , focusing intensely on the hands-on skills required to neutralize AI threats before attackers strike. 

The CAISP curriculum moves beyond theoretical knowledge, teaching you how to secure AI systems using the OWASP LLM Top 10 and implement defenses based on the MITRE ATLAS framework. 

You will explore AI supply chain risks and best practices for securing data pipelines and infrastructure. Furthermore, the course gives you hands-on experience to attack and defend Large Language Models (LLMs), secure AI pipelines, and apply essential compliance frameworks like NIST RMF and ISO 42001 in real-world scenarios. 

By mastering these practical labs and successfully completing the task-oriented exam, you will prove your capability to defend a real system.

This episode draws on a comprehensive guide covering over 50 real AI security interview questions for 2026, touching upon the exact topics that dominate technical rounds at leading US companies like Google, Microsoft, Visa, and OpenAI.

Key areas explored include:

Attack & Defense Strategies: You will gain insight into critical attack vectors such as prompt injection, which hijacks an AI's task, versus jailbreaking, which targets the AI's safety rules (e.g., the "Grandma Exploit"). 

Learn how attackers execute data poisoning by contaminating data sources, illustrated by the famous Microsoft’s Tay chatbot incident. Understand adversarial attacks, such as using physical stickers (adversarial patches) to trick a self-driving car’s AI into misclassifying a stop sign, and the dangers of model theft and vector database poisoning. 

Essential defense mechanisms are detailed, including designing a three-stage filter to block prompt injection using pre-processing sentries, hardened prompt construction, and post-processing inspectors. 

Furthermore, you will learn layered defenses, such as aggressive data sanitation and using privacy-preserving techniques like differential privacy, to stop users from extracting training data from your model.

Secure System Design: The discussion covers designing an "assume-hostile" AI fraud detection architecture using secure, isolated zones like the Ingestion Gateway, Processing Vault, Training Citadel (air-gapped), and Inference Engine.

Strategies for securing the entire pipeline from data collection to model deployment involve treating the process as a chain of custody, generating cryptographic hashes to seal data integrity, and ensuring only cryptographically signed models are deployed into hardened containers. 

Security tools integrated into the ML pipeline should include code/dependency scanners (SAST/SCA), data validation detectors, adversarial attack simulators, and runtime behavior monitors. When securing AI model storage in the cloud, a zero-trust approach is required, including client-side encryption, cryptographic signing, and strict, programmatic IAM policies.

Threat Modeling and Governance: Explore how threat modeling for AI differs from traditional software by expanding the attack surface to include training data and model logic, focusing on probabilistic blind spots, and aiming to subvert the model's purpose rather than just stealing data. 

We cover the application of frameworks like STRIDE to AI systems, modeling threats like Spoofing (adversarial patches), Tampering (data poisoning), and Information Disclosure (leaking training data). 

The importance of knowing the OWASP LLM Top 10 is emphasized, listing threats like Model Denial of Service and Insecure Plugin Design. We also detail the s

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The cybersecurity market is currently experiencing a massive talent shortfall in the emerging field of Artificial Intelligence security, driving compensation for specialized roles to unprecedented heights. 

AI security roles are projected to pay between 180K–280K in 2026, but the majority of cybersecurity professionals lack the necessary qualifications,. We break down exactly what skills are commanding this premium and how to close the gap.

Organizations are urgently seeking experts who can secure LLM deployments, stop prompt injection attacks, and lock down complex AI pipelines. 

Generalist security certifications are no longer enough; adding a specialized certification, such as the Certified AI Security Professional (CAISP), correlates with a significant 15–20% salary premium over peers with only generalist security knowledge,.

We explore the paths to becoming an expert practitioner versus a strategic leader:

The Practitioner Track: For DevSecOps Engineers, Red Teamers, and AI/ML Security Engineers, the focus must be on hands-on technical execution. 

The CAISP certification is highlighted as a technical benchmark, requiring candidates to learn how to execute adversarial attacks on LLMs, identify OWASP Top 10 vulnerabilities, secure AI deployment pipelines using DevSecOps tooling, and apply AI threat modeling with STRIDE methods. 

This course focuses heavily on ‘doing,’ providing 30+ hands-on exercises and 60-day lab access to work with real GenAI pipelines and LLM vulnerabilities.

The Strategic Track: For CISOs, Security Managers, and Compliance Officers, the focus shifts to strategic oversight, policy, and governance,. Certifications like ISACA’s Advanced in AI Security Management (AAISM) focus on AI Governance, Risk Management, and ensuring algorithmic accountability, which is increasingly vital as regulations like the EU AI Act tighten in 2026,.

We detail the compensation projections for top-tier specialized roles in 2026, including the Lead AI Security Architect (projected up to 280,000+), LLMRedTeamSpecialist(160,000–230,000),and DevSecOps for AI Pipelines (150,000–$210,000).

 If you are ready to master the technical realities of AI security and leverage the immense talent gap for significant leverage in salary negotiations, this episode is essential listening.

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Cybercrime drains trillions of dollars globally each year. Today's threat landscape is defined by smart, adaptable adversaries: 40% of all cyberattacks use AI to find hidden weaknesses, and nearly all companies (93%) now face these advanced threats daily. 

The Certified AI Security Professional (CAISP) course compresses the typical 2–4 years needed to become an AI Security Engineer into just 8 weeks through daily hands-on labs with vulnerable AI systems. 

This episode describes the roadmap for defending against sophisticated AI threats, drawing from the AI Security Engineer Roadmap: Skills for 2025 & Beyond.

AI security engineers are crucial experts who understand both AI systems and security methods. Their primary focus is protecting AI systems from various attacks that target data, models, and infrastructure. They stop bad actors from poisoning training data, stealing sensitive information, or tricking AI into making dangerous decisions.

The role is comprehensive, blending technical cybersecurity and machine learning expertise. Responsibilities include securing machine learning systems from development through deployment, conducting vulnerability assessments against AI models, building defenses against AI-based attacks, and enforcing data privacy protocols. 

They conduct critical security duties, such as fully modelling threats and vulnerabilities and developing incident response plans. They also work directly with Data scientists and Developers to integrate security from the beginning of the AI product lifecycle.

The difference with current AI systems is that AI-powered cyber threats can have a real-life effect on organizations and people. These evolving threats include criminals using their own AI techniques to write malware adaptable to defenses. Therefore, specialists must have a deep understanding of non-standard machine learning concepts and AI security principles.

Essential skills required for this high-demand specialization include:

• Understanding how attackers target LLMs, including the OWASP Top 10 LLM attacks.

• Understanding adversarial attack techniques that use subtle changes to input data to fool an AI.

• Possessing skills in detecting data poisoning attempts.

• Securing applications like natural language processing (NLP) against prompt injection attacks and securing computer vision systems against image manipulation.

• Mapping security risk utilizing the MITRE ATLAS framework, which provides an overview of attack patterns and defenses specific to AI.

Beyond technical expertise, the best AI security engineers must think critically and collaborate effectively with data scientists, data engineers, and business leaders who may not be familiar with security issues.

AI security in 2025 offers significant career opportunities as AI systems grow across industries. The development of AI in the security environment generates massive growth in job classification for specializations. 

Sectors like Defense, finance, tech, and healthcare actively hunt for these professionals. The average salary for an AI Security Engineer in the United States is approximately $152,773 per year. 

By following this AI Security Engineer Roadmap, you will secure your future and help maintain the integrity of the technology that is increasingly becoming part of our lives.

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Episode: Securing AI Systems - A Deep Dive into AI Security with Marudhamaran Gunashekaran 

In this episode, Jeremy Daly, Cybersecurity Lead at Lumifi, sits down with Marudhamaran Gunashekaran, Principal Security Consultant and Lead Author of the Certified AI Security Professional (CAISP) course at Practical DevSecOps (a Hysn Technologies company). 

What You'll Learn: 

The conversation cuts through the AI security hype to address what matters. Maran identifies the biggest threat facing organizations today: rapid, uncontrolled AI adoption. 

Companies are rushing to integrate AI systems without proper security oversight, connecting corporate data, healthcare information, and internal systems to AI platforms before security teams can catch up. 

We discuss practical AI security threats, including prompt injection attacks, AI supply chain vulnerabilities, and the emergence of agentic AI systems. 

Maran explains why traditional security skills translate to AI security but also why new knowledge is critical. He draws parallels between the cloud adoption wave of a decade ago and today's AI transformation. 

The episode includes a live demonstration of the CAISP course labs, showing how students work with GPU-powered environments to understand tokenization, model interactions, and real attack scenarios. The course combines 20% video lectures with 80% hands-on practice, supported by 24/7 instructor chat and AI-assisted explanations. 

Looking ahead, Maran warns about shadow AI usage in enterprises and the growing need for securing model context protocols. He predicts an AI arms race where AI systems will increasingly defend against AI-powered attacks. 

His advice for security professionals?

Don't wait. Go to HuggingFace.com today, download a model, and start experimenting. The skills gap is real, and upskilling in AI security isn't optional anymore.

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InfoSec Black Friday Deals 2025: Securing the Future of Cybersecurity

This special offer broadcast details the InfoSec Black Friday 2025 deals, presenting a limited-time chance to advance cybersecurity careers when the demand for security professionals continues to grow. 

Tune in to discover how to save up to $500 on certification bundles and receive 15% off all individual certifications. 

Certified DevSecOps Professional (CDP)

Certified AI Security Professional (CAISP)

Certified Cloud-Native Security Expert (CCNSE)

Certified Threat Modeling Professional (CTMP)

Certified API Security Professional (CASP)

Certified Container Security Expert (CCSE)

Certified DevSecOps Expert (CDE)

Certified Software Supply Chain Security Expert (CSSE)

Certified Security Champion (CSC)

Don't let this limited-time opportunity pass by; accelerating expertise now is key to success in the complex 2025 cybersecurity landscape.

Experts project 3.5 million open cybersecurity positions in 2025, with the market expected to reach $424 billion by 2030. Professionals with certifications are known to earn higher salaries and secure more career options.

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In this episode, we explore the rapid evolution of cybersecurity and the critical rise of a new specialisation: the AI Security Engineer. As artificial intelligence advances, it not only enhances our defensive capabilities but also introduces sophisticated new attack vectors that traditional security measures can't handle.

AI Security Certification - Certified AI Security Professional (CAISP) course

This has created a massive demand for professionals who can secure the AI systems themselves, with an estimated 4.8 million unfilled cybersecurity positions worldwide and a significant shortage of experts skilled in both AI and cybersecurity.

We'll break down the key differences between a traditional Cybersecurity Analyst and an AI Security Engineer. While an analyst typically monitors and responds to threats in existing IT systems, an AI Security Engineer proactively works to secure machine learning models throughout their lifecycle, from development to deployment. 

This involves a shift from passive monitoring to actively protecting AI systems from unique threats like adversarial attacks, data poisoning, model inversion, and inference attacks.

Discover the skills you already possess as a cybersecurity analyst that are directly transferable to an AI security role. Core competencies like threat analysis, incident response, and risk management are essential foundations. We'll discuss how to build upon these by adding knowledge of AI/ML concepts, programming languages like Python, and frameworks such as TensorFlow and PyTorch.

For those ready to make this pivotal career move, we lay out a practical roadmap for the transition, which can take as little as three to four months with focused effort. A key resource highlighted is the Certified AI Security Professional (CAISP) course, designed to equip security professionals with hands-on experience in AI threat modelling, supply chain security, and simulating real-world attacks. The course covers critical frameworks like MITRE ATLAS and the OWASP Top 10 for LLMs and provides practical experience with over 25 hands-on exercises.

Finally, we look at the incredible career opportunities this transition unlocks. AI Security Engineers are in high demand across major industries like finance, technology, government, and healthcare. 

This demand is reflected in significantly higher salaries, with AI Security Engineers in the US earning between $150,000 and $250,000+, often 20-40% more than their cybersecurity analyst counterparts. With the AI security market projected to grow exponentially by 2030, this specialisation represents one of the most promising and lucrative career paths in technology today.

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This episode delves into the critical field of AI Red Teaming, a structured, adversarial process designed to identify vulnerabilities and weaknesses in AI systems before malicious actors can exploit them.

The Certified AI Security Professional (CAISP) course is specifically designed to advance careers in this field, offering practical skills in executing attacks using MITRE ATLAS and OWASP Top 10, implementing enterprise AI security, threat modelling with STRIDE, and protecting AI development pipelines. This certification is industry-recognized and boosts an AI security career, with roles like AI Security Consultant and Red Team Lead offering high salary potential.

It's an essential step in building safe, reliable, and trustworthy AI systems, preventing issues like data leakage, unfair results, and system takeovers.

AI Red Teaming involves human experts and automated tools to simulate attacks. Red teamers craft special inputs like prompt injections to bypass safety controls, generate adversarial examples to confuse AI, and analyse model behaviour for consistency and safety. Common attack vectors include jailbreaking to bypass ethical guardrails, data poisoning to introduce toxic data, and model inversion to learn training data, threatening privacy and confidentiality.

The importance of AI Red Teaming is highlighted through real-world examples: discovering unfair hiring programs using zip codes, manipulating healthcare AI systems to report incorrect cancer tests, and tricking autonomous vehicles by subtly altering sensor readings. It also plays a vital role in securing financial fraud detection systems, content moderation, and voice assistants/LLMs. Organisations also use it for regulatory compliance testing, adhering to standards like GDPR and the EU AI Act.

Several tools and frameworks support AI Red Teaming. Mindgard, Garak, HiddenLayer, PyRIT, and Microsoft Counterfit are prominent tools. Open-source libraries like Adversarial Robustness Toolbox (ART), CleverHans, and TextAttack are also crucial.

Key frameworks include the MITRE ATLAS Framework for mapping adversarial tactics and the OWASP ML Security Top 10, which outlines critical AI vulnerabilities like prompt injection and model theft.

Ethical considerations are paramount, emphasising responsible disclosure, legal compliance (e.g., GDPR), harm minimisation, and thorough documentation to ensure transparency and accountability.

For professionals, upskilling in AI Red Teaming is crucial as AI expands attack surfaces that traditional penetration testing cannot address. Essential skills include Python programming, machine learning knowledge, threat modelling, and adversarial thinking.

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Security isn't keeping pace with the swift advancements in AI and the explosion of cloud-native adoption. Many teams find themselves trying to mend broken pipelines with outdated AppSec playbooks, leading to significant vulnerabilities. This episode dives deep into how to bridge this critical gap, equipping you with the skills to truly defend modern systems.

Ready to build these skills and stay ahead of the curve?

Enroll in the Certified DevSecOps Professional and Certified AI Security Professional (CDP + CAISP) bundle today and save! 

Practical DevSecOps, the platform behind these certifications, focuses on realistic, browser-based labs and a vendor-neutral curriculum. Their certifications are not just paper credentials; they require 6–24 hour practical, hands-on exams in production-like lab environments, proving real skill. 

This approach has made them a trusted platform, even listed on the NICCS (National Initiative for Cybersecurity Careers and Studies) platform by CISA, reflecting their rigour and government-trusted structure. Unlike traditional training, these certifications are lifetime with no forced renewals.

By combining the Certified DevSecOps Professional (CDP) and the Certified AI Security Professional (CAISP), you gain a powerful, holistic skillset that prepares you to secure both the underlying infrastructure and the cutting-edge AI systems built upon it. 

As one learner states about AI security, it's "highly relevant to the challenges security experts are facing today". This is how you build real, production-grade security skills and truly become a defender in today's complex threat landscape.

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Welcome to a crucial episode where we delve into the MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) Framework, an exhaustive knowledge base designed to secure our increasingly AI-dependent world. 

As AI and machine learning become foundational across healthcare, finance, and cybersecurity, protecting these systems from unique threats is paramount.

Unlike MITRE ATT&CK, which focuses on traditional IT systems, MITRE ATLAS is specifically tailored for AI-specific risks, such as adversarial inputs and model theft. It provides a vital resource for understanding and defending against the unique vulnerabilities of AI systems.

In this episode, we'll break down the core components of MITRE ATLAS:

Tactics: These are the high-level objectives of attackers – the "why" behind their actions. 

MITRE ATLAS outlines 14 distinct tactics that attackers use to compromise AI systems, including Reconnaissance (gathering information on the AI system), Initial Access (gaining entry into the AI environment), ML Model Access (entering the AI environment), Persistence (establishing continuous access), Privilege Escalation (gaining more effective controls), and Defense Evasion (bypassing security). 

Other tactics include Credential Access, Discovery, Lateral Movement, Collection, Command and Control, Exfiltration, Impact, and ML Attack Staging.

Techniques: These are the specific methods and actions adversaries use to carry out their tactics – the "how". We'll explore critical techniques like Data Poisoning, where malicious data is introduced into training sets to alter model behavior; Prompt Injection, manipulating language models to produce harmful outputs; and Model Inversion, which involves recovering target data from an AI model. 

Other key techniques to watch out for include Model Extraction, reverse-engineering or stealing proprietary AI models, and Adversarial Examples, subtly altered inputs that trick AI models into making errors.

We'll also examine real-world case studies, such as the Evasion of a Machine Learning Malware Scanner (Cylance Bypass), where attackers used reconnaissance and adversarial input crafting to bypass detection by studying public documentation and model APIs. 

Another notable example is the OpenAI vs. DeepSeek Model Distillation Controversy, highlighting the risks of model extraction and intellectual property theft by extensively querying the target model.

To safeguard AI systems, MITRE ATLAS emphasizes robust security controls and best practices. Key mitigation strategies include:

Securing Training Pipelines to protect data integrity and restrict access to prevent poisoning or extraction attempts.

Continuously Monitoring Model Outputs for anomalies indicating adversarial manipulation or extraction attempts.

Validating Data Integrity through regular audits of datasets and model behaviour to detect unexpected changes or suspicious activity.

Join us as we discuss how the MITRE ATLAS Framework transforms AI security, providing practical guidance to defend against the evolving threat landscape.

 You'll learn why it's crucial for every organization to embrace this framework, contribute to threat intelligence, and engage with the wider AI security community to secure AI as a tool of innovation, not exploitation. 

The Certified AI Security Professional Course comprehensively covers the MITRE ATLAS Framework, offering practical experience to implement these defences effectively.

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Are you ready to face the escalating threat of AI attacks? AI system attacks are hitting companies every single day.  Hackers use AI tools to break into major banks and steal millions. It's a critical time for anyone in tech or cybersecurity to understand how to fight back.

In this episode, we delve into why AI security is more crucial than ever in 2025. We reveal that 74% of IT security professionals say AI-powered threats are seriously hurting their companies, and a staggering 93% of businesses expect to face AI attacks daily this year.

These aren't just minor incidents; last year, 73% of organizations were hit by AI-related security breaches, costing an average of $4.8 million each time, with attacks taking an alarming 290 days to even detect.

The good news? Companies are desperately seeking individuals with AI security expertise, offering excellent opportunities for those who are prepared. We discuss how AI security books serve as your secret weapon, providing proven strategies directly from real security experts who have battled actual AI attacks.

We'll touch upon some top resources available, covering everything from:

• Understanding and protecting against Large Language Model (LLM) security threats.
• Practical applications of LLMs for building smart systems.
• Developing your own LLMs from scratch.
• Defending against sophisticated adversarial AI attacks, including prompt injection and model poisoning.
• Navigating AI data privacy, ethics, and regulatory compliance.
• Advanced techniques like AI red teaming to systematically assess and enhance security.

Whether you're a beginner looking to understand the basics or an expert aiming for cutting-edge strategies, finding the right learning path in AI cybersecurity is essential. Don't wait – AI threats are growing stronger every day. Tune in to discover how to upskill and become an AI security expert, building solid skills step by step for career development success.

Ready to go further? Our Certified AI Security Professional Course offers an in-depth exploration of AI risks. It combines the best book knowledge with hands-on practice, allowing you to work on real AI security system attacks and learn directly from industry experts. 

Enroll today and upskill your AI Security knowledge with Certified AI Security Professional certification. Plus, for a limited time, you can save 15% on this course, and you can buy it now and start whenever you're ready!

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Join us for an insightful episode as we delve into the critical realm of product security within the Medtech industry. The digital revolution is transforming patient care, but it also introduces significant security risks to medical devices.

We'll explore the complex security environment where devices like pacemakers and diagnostic systems are increasingly connected, making them targets for unauthorised access, data theft, and operational manipulation. 

Discover how breaches can lead to dire consequences, from endangering patient health and damaging manufacturers' reputations, to incurring financial losses and navigating stricter regulatory hurdles.

Learn about the types of medical devices most susceptible to cyber threats, including those with connectivity, remote access features, legacy systems, sensitive data storage (PHI), and life-sustaining equipment.

Our focus shifts to threat modelling – a crucial, proactive process for enhancing medical device security. 

We'll uncover its immense benefits, such as identifying and addressing risks, boosting device resilience against cyberattacks, and ensuring regulatory adherence.

We'll also touch upon the FDA's recent policy update, transitioning from the Quality System Regulation (QSR) to the Quality Management System Regulation (QMSR), which now incorporates ISO 13485:2016 standards, highlighting a greater emphasis on risk management throughout the device lifecycle.

Dive deep into various threat modelling techniques that help manufacturers fortify their products:

Agile Threat Modeling: Integrating security with rapid development cycles, ensuring continuous assessments aligned with ongoing development.

Goal-Centric Threat Modeling: Prioritizing protection for critical assets and business objectives based on impact on functionalities and compliance requirements.

Library-Centric Threat Modeling: Utilizing pre-compiled lists of known threats and vulnerabilities pertinent to medical devices for standardized risk assessment, enhancing scalability and efficiency.

Finally, we'll discuss how specialized training, such as the Practical DevSecOps Certified Threat Modeling Professional (CTMP) course, equips Medtech manufacturers with the essential skills to proactively identify and address security vulnerabilities.

This training focuses on real-world applications and scenarios, ensuring continuous security assessment and compliance with stringent regulatory standards from design to deployment.

Tune in to understand why threat modelling is not just a best practice, but an essential component for safeguarding patient well-being and maintaining integrity in the digital healthcare landscape.

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Welcome to "Securing the Future," the podcast dedicated to navigating the complex world of AI security. In this episode, we unpack the vital role of AI security frameworks—acting as instruction manuals—in safeguarding AI systems for multinational corporations. 

These frameworks provide uniform guidelines for implementing security measures across diverse nations with varying legal requirements, from Asia-Pacific to Europe and North America.

We explore how these blueprints help organizations find weak spots before bad actors do, establish consistent rules, meet laws and regulations, and ultimately build trust with AI users. Crucially, they enable compliance and reduce implementation costs through standardization.

This episode delves into four leading frameworks:
NIST AI Risk Management Framework (AI RMF): We break down its comprehensive, lifecycle-wide approach, structured around four core functions: Govern, Map, Measure, and Manage. 

This widely recognized framework is often recommended for beginners due to its clear steps and available resources. Its risk-based approach is adaptable for specific sectors like healthcare and banking, forming the backbone of their tailored safety frameworks.

Microsoft’s AI Security Framework: This framework focuses on operationalizing AI security best practices. It addresses five main parts: Security, Privacy, Fairness, Transparency, and Accountability. While integrating with Microsoft tools, its principles are broadly applicable for ensuring AI is used correctly and protected.

MITRE ATLAS Framework for AI Security: Discover this specialized framework that catalogues real-world AI threats and attack techniques. We discuss attack types like data poisoning, evasion attacks, model stealing, and privacy attacks, which represent “novel attacks” on AI systems. ATLAS is invaluable for threat modelling and red teaming, providing insights into adversarial machine learning techniques.

Databricks AI Security Framework (DASF) 2.0: Learn about this framework, which identifies 62 risks and 64 real use-case controls. Based on standards like NIST and MITRE, DASF is platform-agnostic, allowing its controls to be mapped across various cloud or data platform providers. 

It critically differentiates between traditional cybersecurity risks and novel AI-specific attacks like adversarial machine learning, and bridges business, data, and security teams with practical tools.

We discuss how organizations can use parts from different frameworks to build comprehensive protection, complementing each other across strategic risks, governance, and technical controls. 

Case studies from healthcare and banking illustrate how these conceptual frameworks are tailored to meet strict government rules and sector-specific challenges, ensuring robust risk management and governance.

Ultimately, AI security is an ongoing journey, not a one-off project. The key takeaway is to start small and build up your security over time.


For more information, read our “Best AI Security Frameworks for Enterprises” blog:

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Discover how a global financial institution transformed its security posture and achieved massive cost savings through targeted threat modeling training. 

Facing challenges like inconsistent practices, difficulty scaling training across 50 countries, and keeping pace with evolving threats, this bank needed a new approach beyond infrequent, in-person workshops.

Their solution? Leveraging the Certified Threat Modeling Professional (CTMP) course from Practical DevSecOps. This program offered a practical learning approach with extensive hands-on labs simulating real banking scenarios and crucial 24/7 expert support via Mattermost. 

It covered key methodologies like STRIDE and PASTA and integrated threat modeling into their DevSecOps pipeline. Structured, role-specific training ensured everyone, from developers to core system engineers, received relevant education.

The results were remarkable:

• $0.5 million annually saved on training and logistics.
• Estimated $10 million reduction in potential breach costs.
• 40% reduced time for threat modeling sessions.
• 30% more potential threats mitigated in the design phase.
• 45% reduction in high-severity production vulnerabilities.
• 150% increase in systems undergoing threat modeling.

Achieved 100% compliance with security assessment regulations.
This success story highlights the power of a scalable, practical, and continuously supported security education programme like the CTMP course in fostering a cultural shift and embedding threat modeling into a global bank's DNA, truly embracing the Shift-left culture. 

Learn how practical training, hands-on experience, and expert guidance can lead to significant efficiency gains, cost reductions, and enhanced security in complex financial environments.

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