Certified - AI Security Audio Course

About Certified - AI Security Audio Course

The AI Security & Threats Audio Course is a comprehensive, audio-first learning series focused on the risks, defenses, and governance models that define secure artificial intelligence operations today. Designed for cybersecurity professionals, AI practitioners, and certification candidates, this course translates complex technical and policy concepts into clear, practical lessons. Each episode explores a critical aspect of AI security—from prompt injection and model theft to data poisoning, adversarial attacks, and secure machine learning operations (MLOps). You’ll gain a structured understanding of how vulnerabilities emerge, how threat actors exploit them, and how robust controls can mitigate these evolving risks. The course also covers the frameworks and best practices shaping AI governance, assurance, and resilience. Learners will explore global standards and regulatory guidance, including NIST AI Risk Management Framework, ISO/IEC 23894, and emerging organizational policies around transparency, accountability, and continuous monitoring. Through practical examples and scenario-driven insights, you’ll learn how to assess model risk, integrate secure development pipelines, and implement monitoring strategies that ensure trust and compliance across the AI lifecycle. Developed by BareMetalCyber.com, the AI Security & Threats Audio Course blends foundational security knowledge with real-world application, helping you prepare for advanced certifications and leadership in the growing field of AI assurance. Explore more audio courses, textbooks, and cybersecurity resources at BareMetalCyber.com—your trusted source for structured, expert-driven learning.

Publisher

Jason Edwards

Category

education · technology

Language

en

Explicit

No

First episode

15 Sept 2025

Latest episode

14 Oct 2025

Latest episodes

51 episodes in the feed.

1. 

   14 Oct 2025

   Trailer

2. 

   15 Sept 2025

   Episode 50 — Automated Adversarial Generation

   This episode examines automated adversarial generation, where AI systems are used to create adversarial examples, fuzz prompts, and continuously probe defenses. For certification purposes, learners must define this concept and understand how automation accelerates the discovery of vulnerabilities. Unlike manual red teaming, automated adversarial generation enables self-play and continuous testing at scale. The exam relevance lies in describing how organizations leverage automated adversaries to evaluate resilience and maintain readiness against evolving threats. In practice, automated systems can generate thousands of prompt variations to test jailbreak robustness, create adversarial images for vision models, or simulate large-scale denial-of-wallet attacks against inference endpoints. Best practices include integrating automated adversarial generation into test pipelines, applying scorecards to track improvements, and continuously updating adversarial datasets based on discovered weaknesses. Troubleshooting considerations highlight the resource cost of large-scale simulations, the difficulty of balancing realism with safety, and the need to filter noise from valuable findings. For learners, mastery of this topic means recognizing how automation reshapes adversarial testing into an ongoing, scalable process for AI security assurance. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.

3. 

   15 Sept 2025

   Episode 49 — Confidential Computing for AI

   This episode introduces confidential computing as an advanced safeguard for AI workloads, focusing on hardware-based protections such as trusted execution environments (TEEs), secure enclaves, and encrypted inference. For exam readiness, learners must understand definitions of confidential computing, its role in ensuring confidentiality and integrity of model execution, and how hardware roots of trust enforce assurance. The exam relevance lies in recognizing how confidential computing reduces risks of data leakage, insider attacks, or compromised cloud infrastructure. Practical applications include executing sensitive healthcare inference within a TEE, encrypting models during deployment so that even cloud administrators cannot access them, and applying attestation to prove that computations are running in secure environments. Best practices involve aligning confidential computing with key management systems, integrating audit logging for transparency, and adopting certified hardware modules. Troubleshooting considerations emphasize performance overhead, vendor lock-in risks, and the need for continuous validation of hardware supply chains. Learners must be prepared to explain why confidential computing is becoming central to enterprise AI security strategies. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.

Reach and audience

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Apple Podcasts (US)

5.0 / 5

1 ratings

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