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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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.

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.

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.

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.