Episode 34 — Risk Frameworks in Practice

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Certified - AI Security Audio Course

Jason Edwards

51 episodes

1 day ago

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.

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Episode 34 — Risk Frameworks in Practice

Certified - AI Security Audio Course

22 minutes

1 month ago

Episode 34 — Risk Frameworks in Practice

This episode examines risk frameworks for AI security, focusing on the NIST AI Risk Management Framework and ISO/IEC 42001. These frameworks provide structured approaches to identify, assess, mitigate, and monitor AI-specific risks across technical and organizational domains. For certification exams, learners must understand how these frameworks map to real-world controls and governance practices. The relevance lies in demonstrating how structured risk management enables organizations to move beyond ad hoc responses and implement scalable, repeatable processes for AI system security.

The applied discussion highlights how organizations implement NIST AI RMF categories such as govern, map, measure, and manage, or adopt ISO/IEC 42001 requirements for AI management systems. Scenarios include conducting structured risk assessments for retrieval-augmented generation pipelines, documenting mitigation strategies for privacy leakage, and aligning board reporting with framework metrics. Troubleshooting considerations include balancing framework adoption with organizational maturity, avoiding checklist-style compliance, and ensuring that frameworks drive actionable improvements. For exam preparation, learners must be able to compare frameworks, recognize their strengths and limitations, and apply them pragmatically to AI security environments. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.