NIST AI RMF evidence readiness

Operationalise NIST AI RMF with records, controls and evidence.

Gamut helps teams move from NIST AI RMF awareness to practical AI assurance: inventory what exists, map context, measure risk and evidence quality, manage gaps and produce leadership-ready reporting.

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The challenge: NIST AI RMF is useful, but it does not run itself

NIST AI RMF gives organisations a strong way to think about AI risk. The hard part is operationalising it: turning principles into records, accountable owners, repeatable assessment workflows, evidence requests, findings, remediation and reporting.

Framework awareness is not evidence

A team can understand the framework and still lack proof of what AI exists, how risk was assessed and which controls are active.

AI use is distributed

AI appears in SaaS tools, internal experiments, vendor products, copilots and agentic workflows. The records are often fragmented.

Reviewers need traceability

Boards, buyers, auditors and legal teams need to trace AI systems to risk decisions, controls, evidence, gaps and remediation.

How Gamut maps to Govern, Map, Measure and Manage

Govern

Define ownership, accountability, policy expectations, risk appetite, approval rules, escalation paths, oversight roles and leadership reporting.

  • AI ownership and accountability records
  • Governance roles and approval routes
  • Board and CISO reporting structure

Map

Identify AI systems, agents, business context, intended purpose, affected users, data exposure, supplier dependencies and operating environment.

  • AI inventory and agent register
  • Purpose, context and user impact records
  • Data, system and supplier mapping

Measure

Assess risk level, control expectations, evidence quality, human oversight, testing, findings, exceptions and residual risk.

  • Risk classification workflow
  • Control and evidence quality review
  • Findings and residual risk records

Manage

Track remediation, approvals, monitoring, exceptions, escalation, review cycles, evidence refresh and continuous improvement.

  • Remediation plans and owners
  • Exception and approval records
  • Evidence refresh and review cadence

Evidence outputs Gamut helps produce

AI system inventory

Records of systems, agents, tools, suppliers, purposes, lifecycle state, owners and business context.

Risk classification

Consistent classification based on purpose, impact, data exposure, oversight, user context and autonomy.

Control evidence

Evidence requests, artefacts, ratings, decisions, control status, findings and remediation records.

Model and system cards

Structured summaries of purpose, use, risks, ownership, limitations, controls and assurance status.

Risk register

Findings, owners, priority, treatment decisions, due dates, status and residual risk.

Leadership reporting

Board-ready view of AI exposure, readiness, evidence quality, gaps and decisions required.

NIST AI RMF for agentic AI

Agentic AI raises the stakes because governance has to cover action, access and autonomy, not just model performance or acceptable use. Gamut extends the evidence model to cover AI agents, tool calls, approval gates, Gateway decisions and bounded execution through Claw.

Agent inventory

Record each agent's purpose, owner, lifecycle state, autonomy level, data access and tool permissions.

Human oversight

Map human-in, human-on and human-over-the-loop controls for different action types and risk levels.

Runtime evidence

Capture records of decisions, approvals, blocked actions, escalations, findings and remediation.

Can you prove this today?

  • Which AI systems and agents exist?
  • Who owns them?
  • What business purpose do they serve?
  • What data do they process or access?
  • Which suppliers and tools are involved?
  • How was risk classified?
  • What controls were required?
  • What evidence supports those controls?
  • What findings remain open?
  • What needs leadership action?

Start with a NIST AI RMF Evidence Review

For teams that want a practical starting point, Gamut can be used to run a focused evidence review against your existing AI systems, GenAI tools, copilots or agents.

Review outputs

  • AI inventory baseline
  • Govern, Map, Measure, Manage evidence review
  • Control and evidence gap report
  • Priority remediation plan
  • Board-ready readiness summary

Best fit

  • Teams formalising AI governance
  • CISOs preparing for AI assurance questions
  • AI vendors facing buyer scrutiny
  • Organisations deploying agents or copilots
  • Boards asking for a clearer AI risk picture

Why Gamut

Designed for operating evidence

Gamut is built to hold the records, evidence, findings and workflow needed to make AI governance reviewable.

Built for systems and agents

The platform covers AI systems, GenAI tools, copilots and agentic workflows, including approval gates and runtime evidence.

Control depth

GTSAF provides broad control depth, with 358 controls across 17 domains to support practical assurance activity.

Founder-led judgement

Gamut was created by a cybersecurity and AI assurance practitioner with 27+ years' experience and CISSP, CCSP, CCZT and TAISE credentials.

Turn framework into evidence

Make NIST AI RMF operational, reviewable and useful.

Use Gamut to build the AI inventory, control evidence and leadership reporting needed to move from framework awareness to assurance readiness.

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