This episode examines keys, encryption, and attestation as core mechanisms for ensuring confidentiality, integrity, and trust in AI systems. Keys form the foundation of cryptographic operations, and encryption protects data at rest and in transit, as well as sensitive model artifacts such as weights and parameters. Attestation provides proof that systems or hardware are running trusted code, ensuring that AI workloads have not been tampered with. For certification purposes, learners must be able to define these concepts, differentiate between symmetric and asymmetric encryption, and describe their relevance to AI security contexts.
Practical considerations include encrypting training datasets stored in the cloud, applying strong key management practices using hardware security modules, and verifying container integrity with remote attestation. Troubleshooting scenarios highlight risks of weak key rotation policies, hard-coded credentials, or relying on unverified execution environments. Best practices involve adopting customer-managed keys for cloud services, enabling trusted execution environments for sensitive inference, and aligning with compliance requirements such as FIPS 140-3 or ISO/IEC standards. For exams, candidates should be prepared to connect cryptographic safeguards to AI-specific risks, demonstrating how they protect against theft, tampering, and unauthorized disclosure. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.