← 🛡️ AI Security, Privacy & Governance
Core
PII Handling and Redaction
Personal data leaks into AI systems through three doors: the prompt you send a model API, the logs you keep for debugging, and the traces you store for evaluation. Handling it means detecting and redacting personal data before it crosses any of those boundaries, then minimizing, encrypting, access-controlling, and expiring whatever you must keep. In regulated industries, logging a raw prompt is the single most common compliance failure.
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RELATED CONCEPTS
LESSONS THAT TEACH THIS
Foundations of Forward Deployed EngineeringSpecify the ownership, trust, network and meaning at each boundary28 min→Foundations of Forward Deployed EngineeringValidate configuration before serving and make credential changes testable30 min→The FDE EngagementCarry the caller’s permissions through every data and action boundary28 min→
PRACTICE THIS IN REAL QUESTIONS
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.→AI Security, Privacy & GovernanceCompare PII detection methods, regex, entropy, NER, external APIs. Why can't regex alone find a person's name?→AI Security, Privacy & GovernanceShould PII redaction live at the AI gateway or in the application layer? Argue the trade-offs.→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 & GovernanceDesign a PII redaction system for an LLM pipeline, PII enters at user input, retrieved context, tool results, and output. Guard all four.→