Why this matters
Most organizations cannot answer basic questions about their own AI footprint: which models are in use, what data reaches them, which agents can take action on live systems, and who approved any of it. That gap is a governance problem before it is a security problem. It shows up as failed audits, shadow deployments no one owns, sensitive data leaving through channels nobody is watching, and autonomous actions with no reviewable trail behind them.
Our solution and results
We start with discovery, because you cannot govern what you have not enumerated. From there we map your AI usage against the NIST AI Risk Management Framework and the federal guidance that applies to your mission, define policy that engineering teams can actually implement, and put monitoring in place around model access, data flows, and agent authority. The result is not a policy binder — it is an inventory you trust, controls that hold, and evidence you can put in front of an auditor.
- 01A maintained inventory of AI systems, models, and agents in use
- 02Policy mapped to NIST AI RMF and applicable federal guidance
- 03Documented approval paths for new AI use cases
- 04Monitoring of data flows into and out of AI systems
- 05Bounded authority and audit trails for autonomous agents
- 06Evidence packages ready for assessment and oversight