Provable AI compliance for government: every decision traceable, signed, and regulator-ready in real time.
Public agencies are under pressure to adopt AI responsibly while preserving transparency, accountability, and auditability. Existing systems deliver reports, not proofs: a snapshot every six to twelve months, assembled by hand, already out of date when it lands.
SentinelFlow™ turns compliance from an after-the-fact documentation task into a continuous, evidence-by-construction framework. Every AI decision is provable, traceable, and regulator-ready the moment it happens, not reconstructed weeks later for an audit.
It is not another compliance dashboard. It's a governance engine for the AI age.
Retrieves only from approved, versioned sources, with real-time scoring for retrieval adequacy and faithfulness.
Enforces least privilege, route transparency, and signed execution logs across every agent action.
A hash-chained, cryptographically signed graph (CEGO) linking prompts, evidence, policies, and human approvals.
A minimal "Evidence Stack" surfaces only high-value, risk-weighted actions, with one-click "Prove It" expansion.
Targets drawn from pilot benchmarks, measured against agencies' current snapshot-based baselines.
SentinelFlow™ is in pilot. The questions in front of me: whether a cryptographically signed evidence graph stays performant at agency scale; how much auto-remediation a reviewer will actually trust; whether an attention-aware interface genuinely cuts oversight fatigue rather than hiding risk; and how cleanly "evidence by construction" maps onto each regulator's expectations.
If you work in or with the public sector on AI assurance, I'd value a conversation about where this holds and where it doesn't.
Real-time assurance, instead of post-hoc audits.
An exploratory pilot by Dr. Nabeel A. Khan. Figures are pilot targets, not guarantees.