Review AI-generated code for secrets, dependencies, authorization, provenance and test quality. Includes a local static-analysis example and a practical pull-request policy template.
Compare LLM guardrail rules, classifiers and model reviewers. Includes a runnable output validator, action-policy boundaries and a practical testing checklist.
Design permission-aware RAG with trusted identity, document-level authorization, cache boundaries and revocation tests. Includes a runnable policy example.
Threat-model AI agents with prompt injection paths, a least-privilege tool matrix, approval boundaries and adversarial tests for unsafe tool calls.
AI introduces new security risks, from data leakage to prompt injection. These best practices help you adopt AI safely and responsibly.
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