AI in government, governed — and therefore fielded.

Government organizations don't get to adopt AI the way startups do. A hallucinated citation is an embarrassment in the private sector; in a federal workflow it can be a legal, operational, or mission failure. AI governance is the discipline that makes adoption possible anyway — and done right, it accelerates deployment rather than blocking it, because governed systems are approvable systems.

The framework that matters

The NIST AI Risk Management Framework gives agencies and contractors a shared vocabulary: govern, map, measure, manage. In practice it means knowing which decisions AI touches, what could go wrong at each point, how you'd detect it, and who owns the response. If that sounds like RMF thinking applied to models — that's exactly the point, and exactly why security-accreditation people (our people) adapt to it fast.

What governed AI looks like in the field

Human-in-the-loop where it counts. AI drafts, ranks, and summarizes; accountable humans decide. The gate sits at consequence, not at convenience.

Retrieval over recall. Systems answer from governed, current, access-controlled sources — with citations — instead of free-associating from training data. Access control follows the user, not the model.

Evaluation before and after deployment. Defined test sets before go-live; drift, bias, and failure monitoring after. 'It seemed fine in the demo' is not an evaluation strategy.

An audit trail. Who asked, what was retrieved, what was generated, what was decided. When the IG asks — and eventually the IG asks — the answer is a query, not an archaeology project.

Our AI practice is built on this premise: start with the process, not the model. Workflow automation, knowledge systems, and decision support that a compliance office can sign — because the governance was engineered in, not bolted on.