Decision Assurance Infrastructure
Summit Cognitive
§ Solutions — NIST AI RMF

The framework asks for evidence. Receipts are evidence.

The NIST AI Risk Management Framework is how U.S. organizations — and increasingly their suppliers — are expected to demonstrate trustworthy AI. Its four functions all reduce to one demand: show that you know what your AI systems decide, under what controls, with what evidence. Summit's mappings to the AI RMF and NIST 800-53 are published, versioned, and readable by your assessors before your first call with us.

§ 01

The mandate, in plain language

The AI RMF is voluntary in name and contractual in practice — it arrives through procurement language, agency guidance, and supplier questionnaires. Each function asks something specific of your decision infrastructure.

Function 1

GOVERN

Policies, accountability, and oversight for AI risk. The hard part is proving the policies actually executed — that the gate fired, the human reviewed, the exception was recorded.
Function 2

MAP

Know your AI systems and their context: which decisions they touch, from what sources, with what downstream impact. An inventory of models is not an inventory of decisions.
Function 3

MEASURE

Assess and track AI risk with evidence. Measurement requires reproducibility — a metric you cannot recompute from recorded inputs is an anecdote.
Function 4

MANAGE

Act on risk: prioritize, respond, document. When an AI decision goes wrong, response depends on being able to replay exactly what happened.
§ 02

What Summit produces against it

Receipts and runtime governance generate the framework's evidence as a byproduct of operating — not as a documentation project bolted on afterward.

Mechanism

Receipts → GOVERN & MANAGE→

Every AI decision carries its policy evaluation results, reviewer identity, and verdict — signed and replayable. Governance stops being asserted in a policy document and starts being demonstrated per decision.
Mechanism

Decision inventory → MAP→

Instrumented workflows produce a live inventory of machine-generated decisions, their sources, and their dependencies — the IDENTIFY discipline of the Cognitive Security Framework, applied to the RMF's MAP function.
Mechanism

Deterministic replay → MEASURE→

Decisions reproduce bit-identically from recorded inputs, so risk measurement is recomputable rather than estimated. Verification certificates extend the same rigor to multi-agent coordination.
Artifact

20 control statements → 800-53→

Twenty published NIST 800-53 control implementation statements, plus a compliance evidence exporter that renders live receipts into ATO-package artifacts — SSP sections and POA&M inputs generated from the system of record.

To be precise about claims: Summit is aligned and mapped to the AI RMF and NIST 800-53. Frameworks of this kind do not certify products — they assess programs. What we provide is the evidence layer that makes your program's assessment defensible. The mappings live in the open standards library.

§ 03

How to start

Begin where the framework will be tested: one AI-assisted workflow that your RMF profile names as consequential.

  1. 01
    Pick the workflow from your AI inventory
    The one with the highest-impact decisions — the workflow your GOVERN documentation already promises is controlled.
  2. 02
    Instrument it for ten days
    Receipts for every decision, policy gates live, replay verified. The pilot produces the function-by-function evidence in place of assertions.
  3. 03
    Export the evidence
    Control-mapped artifacts from the evidence exporter, plus a governance findings memo — what the framework calls measurement, delivered in writing.

Turn your RMF profile into demonstrated practice.

The 10-Day Decision Assurance Pilot instruments one workflow your framework profile names — and delivers the control-mapped evidence your assessors will ask to see.

Start a PilotFederal pathway