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Federal AI security, from control mappings to threat modeling. How NIST 800-53 Rev 5 actually maps to an LLM, how to defend against prompt injection in production, and how to accelerate the ATO without breaking the RMF.

Security is where federal AI projects die. Not in the model, not in the pipeline — in the control mapping, the SSP, and the assessor's questions about non-determinism and data lineage. The posts below are the ones we send to teams who just got a preliminary IATT and realized the auditor doesn't know what a token is.

Read them in order: NIST 800-53 gives you the control language, the prompt-injection post gives you the threat model, and the ATO playbook gives you the sequencing that turns six months into ten weeks.

Posts in this topic
2026 · Compliance

NIST 800-53 Controls for LLM Systems

A 2026 mapping of Rev 5 control families to production LLM systems — including the SI, SC, and AC families that need LLM-specific implementation statements.
2026 · Threat Model

Prompt Injection Defense for Federal LLMs

The threat class NIST hasn't named yet. Input filtering, output constraints, tool-call allowlists, and the controls that actually hold up in an adversarial red team.
2026 · ATO

ATO Acceleration Playbook for Federal AI

Inheritance strategy, parallel workstreams, assessor-ready artifacts, and the shortcuts that are legitimate versus the ones that bounce your package.
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