Core

Audit Trails and Traceability

An audit trail is an immutable, queryable record that lets you reconstruct, months later, exactly who triggered a given AI output, with which model and prompt version, over which data. It is evidence for a regulator or a customer's security team, not a debugging log, and the difference is design: correlation IDs threaded end to end, tamper-evidence, and a retention policy. Regulated buyers will not sign without it.

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RELATED CONCEPTS

Observability for AI Systems AI Governance (SOC2, EU AI Act)PII Handling and Redaction

LESSONS THAT TEACH THIS

The FDE EngagementCarry the caller’s permissions through every data and action boundary28 min→The FDE EngagementMake named actions safe to authorize, retry and recover30 min→The FDE EngagementRecreate the release and prove that its state can recover28 min→

PRACTICE THIS IN REAL QUESTIONS

MLOps & ML EngineeringWhy use a model registry instead of just storing model artifacts in S3?→MLOps & ML EngineeringHow do you ensure reproducibility in ML workflows?→AI Security, Privacy & GovernanceHow would you defend a RAG system against poisoned retrieved context and vector/embedding attacks?→System Design & Production EngineeringYour product deploys into dozens of customer environments. Design secrets management across all of them.→RAG & Agent System DesignA customer wants to expose internal APIs to a model via MCP. Design it safely: auth, least privilege, versioning.→AI Security, Privacy & GovernancePrompts and outputs contain personal data. What does GDPR mean for each call to an external LLM endpoint?→