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education
Hosted by Jason Edwards · education · EN · 51 episodes
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 aroun
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Signup to Generate a PitchCertified - AI Security Audio Course is a education podcast hosted by Jason Edwards, with 51 episodes on record and a Required Pod Score of 80.
Jason Edwards hosts Certified - AI Security Audio Course, a education show with 51 episodes published.
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Episode #50
<p>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.</p><p>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...
Episode #49
<p>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.</p><p>Practical applications include executing sensitive healthcare inference within a TEE, encrypting models during deployment so that even cloud administrators cannot access them...
Episode #48
<p>This episode covers guardrails engineering, emphasizing the design of policy-driven controls that prevent unsafe or unauthorized AI outputs. Guardrails include policy domain-specific languages (DSLs), prompt filters, allow/deny lists, and rejection tuning mechanisms. For certification purposes, learners must understand that guardrails do not replace security measures such as authentication or encryption but provide an additional layer focused on content integrity and compliance. The exam relevance lies in recognizing guardrails as structured output management that reduces the risk of harmful system behavior.</p><p>Applied scenarios include using rejection tuning to gracefully block unsafe instructions, applying allow lists for structured...
Episode #47
<p>This episode examines on-device and edge AI security, focusing on models deployed in mobile, IoT, or embedded systems where resources are constrained and connectivity may be intermittent. For certification purposes, learners must understand the unique risks of on-device AI, including theft of model files, tampering with local execution environments, and loss of centralized monitoring. The exam relevance lies in being able to describe why edge environments demand different safeguards compared to centralized cloud AI deployments.</p><p>Practical scenarios include attackers extracting proprietary models from mobile apps, manipulating IoT devices to alter inference results, or exploiting offline execution to...
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