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

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