Practical guidance on securing AI agents, tools, and the systems they act on, written by the team building the trust layer for AI agents.
A consolidated RAG security checklist covering access control, ingestion, vector database hardening, multi-tenant isolation, and privacy, for a systematic review.
Threat modeling a RAG system means mapping every source feeding retrieval, tracing access control through the pipeline, and assuming any retrieved content is untrusted.
Data subject requests against AI systems have to reach prompts, logs, embeddings, and fine-tuning data, not just the primary database, or the response is incomplete.
Privacy-preserving logging for AI systems captures metadata and decisions by default, treats raw content capture as an exception, and controls access tightly.
Secrets end up in AI context windows through copied configuration, retrieved documents, and tool responses, and the fix is keeping them out, not hiding them better.
Minimizing personal data in AI workflows means including only fields a task needs, redacting the rest, and questioning defaults that pass full records for convenience.
AI data retention needs explicit limits for prompts, responses, embeddings, and logs separately, since a single blanket policy tends to under-cover some of them.
Financial RAG needs material non-public information controls, strict need-to-know retrieval scoping, and caution about generated numbers being treated as authoritative.
Healthcare RAG needs minimum-necessary access, business associate review of any third-party model provider, and accuracy safeguards given the clinical stakes involved.
Legal document RAG needs privilege-aware access control, since attorney-client privilege and matter-specific confidentiality do not map to typical role-based permissions.
Customer-facing RAG assistants face external adversaries directly, making per-customer retrieval isolation and public-source injection defense the top priorities.
Employee knowledge base RAG needs retrieval that respects existing role and department boundaries, since the biggest risk is accidental cross-team exposure, not attack.
