MAP-5.1: Likelihood and magnitude of each identified impact (both potentially beneficial and harmful) based on expected use, past uses of AI systems in similar contexts, public incident reports, feedback from those external to the team that

MAP-5.1 requires you to document, for every material impact you identified for an AI system, (1) how likely it is to occur and (2) how big the impact could be, including both benefits and harms, using evidence such as expected use, analogous past uses, public incident reports, and external feedback. Your fastest path is to create an “impact register” with likelihood/magnitude scoring and named supporting sources. ¹

Key takeaways:

• Build and maintain an impact register that scores likelihood and magnitude for both beneficial and harmful outcomes. ¹
• Ground scoring in multiple inputs: expected use, similar deployments, public incident reports, and feedback from outside the build/deploy team. ¹
• Keep audit-ready evidence: sources consulted, rationale, dissenting views, and decision records tying scores to risk treatment. ¹

Compliance leaders struggle with AI risk documentation for a simple reason: teams can list “risks,” but they often cannot defend how probable each impact is, how severe it could be, and what information they used to reach those judgments. MAP-5.1 closes that gap by requiring documented likelihood and magnitude for each identified impact, including beneficial impacts (value, efficiency, safety improvements) and harmful impacts (discrimination, safety incidents, privacy harms, financial loss, operational disruptions). ¹

Operationalizing MAP-5.1 is less about picking a perfect math model and more about running a repeatable, evidence-driven assessment that survives scrutiny. The requirement explicitly points you to concrete evidence types: expected use, prior uses of AI in similar contexts, public incident reports, and feedback from people outside the team that developed or deployed the system. ¹

This page gives requirement-level implementation guidance: who owns it, how to set up the workflow, what artifacts to retain, and what auditors typically challenge. It’s written for a CCO, compliance officer, or GRC lead who needs a defensible approach that can be executed across multiple AI use cases without stalling engineering. ²

Regulatory text

Requirement (excerpt): “Likelihood and magnitude of each identified impact (both potentially beneficial and harmful) based on expected use, past uses of AI systems in similar contexts, public incident reports, feedback from those external to the team that developed or deployed the AI system, or other data are identified and documented.” ¹

What the operator must do:

1. For each impact already identified in your mapping work, assign and document a likelihood rating and a magnitude rating. ¹
2. Support those ratings with traceable inputs, explicitly including expected use, comparable AI deployments, public incident reports, and external feedback (for example, from Legal, Compliance, affected business owners, customer advocates, or independent reviewers). ¹
3. Retain the documentation so a reviewer can reproduce how you reached the ratings and see what evidence you consulted. ¹

Plain-English interpretation

MAP-5.1 means: “Don’t stop at listing impacts. Estimate how often each impact could happen and how bad (or good) it could be, and write down why you believe that.” ¹

For a serious operator, the standard to meet is defensibility:

• A neutral third party should be able to read your record and understand the impact, the assumptions about use, the evidence reviewed, and why the final scores make sense. ¹
• You must cover beneficial impacts, not only harms. This prevents one-sided analysis and supports balanced governance decisions (for example, when approving constrained pilots). ¹

Who it applies to

Entity scope: Any organization developing or deploying AI systems. ¹

Operational contexts where it matters most:

• AI used in customer-facing decisions (eligibility, pricing, complaints handling).
• AI that can affect safety, health, or critical operations (workplace safety, industrial processes).
• AI that generates or transforms content used externally (marketing claims, customer communications).
• AI embedded in third-party products you deploy, where you still own outcomes to customers and regulators.

Functional owners (typical RACI):

• Accountable: CCO/GRC lead or designated AI risk owner.
• Responsible: Product owner / model owner with Risk or Compliance facilitation.
• Consulted: Legal, Security, Privacy, Data Governance, HR (if workforce impact), Procurement/TPRM (if a third party model), customer or patient advocacy as relevant.
• Informed: Internal Audit, senior risk committee.

What you actually need to do (step-by-step)

Step 1: Stand up an “Impact Register” as the system of record

Create a register (spreadsheet, GRC tool, or Daydream workflow) with one row per identified impact. Minimum fields:

• System name, version, owner, deployment context.
• Impact statement (who/what is affected, how).
• Impact type: beneficial or harmful.
• Affected stakeholders (customers, employees, applicants, patients, partners).
• Likelihood rating + rationale.
• Magnitude rating + rationale.
• Evidence sources consulted (links, titles, dates).
• External feedback summary and disposition.
• Open questions / assumptions.
• Risk treatment linkage (controls, monitoring, human review, go/no-go decision).

Practical note: keep impacts granular. “Bias” is too broad; “higher false negative rate for a protected class in screening workflow” is scorable and testable.

Step 2: Define a scoring rubric that teams can apply consistently

You need consistency across systems. Pick a simple rubric:

• Likelihood: rare / possible / likely (or a 1–5 scale).
• Magnitude: low / moderate / high (or a 1–5 scale).

Document what each level means in your environment (financial, legal, safety, customer, and operational dimensions). Avoid pretending you have statistical precision if you don’t; MAP-5.1 asks for identification and documentation based on evidence, not perfect quantification. ¹

Step 3: Ground the assessment in the required evidence types

For each impact, require at least one input from each relevant category:

1. Expected use: intended users, decisions influenced, automation level, human override, scale of deployment, and foreseeable misuse. ¹
2. Past uses in similar contexts: internal pilots, prior versions, peer-industry analogs, or third-party deployments you can describe. ¹
3. Public incident reports: credible public reports describing failures in similar systems (documentation should capture what you reviewed and how it changes likelihood/magnitude). ¹
4. External-to-team feedback: structured input from outside the build/deploy team (for example, a formal review meeting, red team report, customer advocate review, privacy office assessment). ¹

If a category is not applicable, force a short justification (example: “No comparable internal prior use exists; documented search performed”).

Step 4: Run a structured review and record dissent

Hold an “impact scoring review” with cross-functional attendees. For each impact:

• Confirm the impact statement is specific.
• Agree on likelihood and magnitude scores.
• Record assumptions (data drift expectations, user training quality, dependency uptime).
• Record dissenting opinions and what resolved them (or why you accepted residual disagreement).

Auditors and regulators tend to focus on whether governance can surface uncomfortable information. Keeping dissent records is a practical way to show you actively sought external feedback, as MAP-5.1 expects. ¹

Step 5: Tie the scores to risk treatment and monitoring

MAP-5.1 is mapping and documentation, but you should connect it to action:

• High likelihood + high magnitude harms should map to pre-deployment controls (testing gates, human-in-the-loop requirements, usage constraints).
• Medium risks should map to monitoring and incident response triggers.
• Material beneficial impacts should map to success metrics and post-deployment validation, so benefits are not assumed.

This linkage is where tooling helps. Daydream can track the impact register, route external feedback approvals, and collect recurring evidence so MAP-5.1 stays current as models and use cases change. ²

Required evidence and artifacts to retain

Maintain an audit-ready package per AI system (or per release for high-change systems):

1. Impact Register (current + prior versions) with change history and approvers.
2. Scoring rubric with definitions and calibration guidance.
3. Expected use documentation (intended use, limitations, user groups, operating environment).
4. Comparable-use notes (internal past deployments, vendor documentation if applicable, lessons learned).
5. Public incident review log (what was searched, what sources were reviewed, summary of relevance).
6. External feedback records:
   • meeting minutes, review memos, red team outputs,
   • issues raised and how resolved,
   • sign-offs or documented exceptions.
7. Decision records linking high-risk impacts to controls, launch constraints, or go/no-go outcomes.

Common exam/audit questions and hangups

Expect reviewers to ask:

• “Show me how you determined likelihood for this specific harm. What did you use besides internal opinion?” ¹
• “Where is external-to-team feedback captured, and what changed because of it?” ¹
• “How do you ensure consistency across products and business units?”
• “How do you update likelihood/magnitude when the model, data, or user workflow changes?”
• “Where are beneficial impacts documented, and how do you validate them post-deployment?” ¹

Common hangups:

• Teams provide a risk list with no scoring rationale.
• Scoring exists, but there is no evidence trail (no incident review log, no external feedback records).
• Impacts are too broad to score consistently.

Frequent implementation mistakes and how to avoid them

Mistake	Why it fails MAP-5.1	Fix
Only documenting harms	Requirement explicitly includes beneficial impacts 1	Add benefit category fields and success metrics; review in same forum
“Likelihood = gut feel”	MAP-5.1 calls for grounding in expected use, similar uses, public incidents, external feedback 1	Require source citations per impact and an evidence checklist
No external feedback loop	Requirement explicitly names feedback from outside the team 1	Create a mandatory cross-functional review step with recorded outcomes
Impacts too vague to score	Magnitude/likelihood becomes meaningless	Rewrite impacts into concrete failure modes and affected parties
Register not maintained after launch	Documentation becomes stale and indefensible	Add triggers: model updates, new data sources, new user groups, incident reports

Enforcement context and risk implications

NIST AI RMF is a framework, not a penalty schedule in the provided sources, but MAP-5.1 aligns with what regulators and litigants often probe after an incident: what you expected could go wrong, how likely you thought it was, and why you shipped anyway. Weak documentation creates two business risks:

• You cannot show due diligence in design and deployment decisions. ¹
• You struggle to prioritize mitigations because likelihood/magnitude is missing or inconsistent.

Treat MAP-5.1 as “decision hygiene” for AI governance: it forces a written record tying risk judgments to evidence. ¹

A practical 30/60/90-day execution plan

First 30 days: Establish the control and run one pilot

• Name the control owner and define the RACI for impact scoring reviews.
• Publish the scoring rubric and an impact register template.
• Pick one AI system in production or near-launch; run the MAP-5.1 workshop and produce the first complete register with evidence attachments. ¹

By 60 days: Scale to the portfolio and add quality checks

• Roll the workflow across additional AI systems based on business criticality.
• Create a lightweight QA check: every impact has likelihood, magnitude, and cited evidence types (expected use, similar uses, public incidents, external feedback) or a documented exception. ¹
• Add a governance checkpoint: launches require the latest approved impact register.

By 90 days: Operationalize ongoing updates

• Define update triggers (material model change, new data source, new deployment population, incident/near-miss).
• Integrate with change management and third-party intake so new AI capabilities cannot bypass impact scoring.
• Move evidence collection into a system (for example, Daydream) that can route approvals, preserve version history, and generate audit exports on demand. ²

Frequently Asked Questions

Do I need quantitative probabilities and dollar impacts for MAP-5.1?

MAP-5.1 requires documented likelihood and magnitude grounded in evidence, but it does not mandate a specific quantitative method in the provided text. Use a consistent rubric your teams can apply, and retain the rationale and sources you relied on. ¹

What counts as “feedback from those external to the team”?

Any structured review from people not on the build/deploy team qualifies if you document it, such as Legal, Compliance, Privacy, Security, business owners, customer advocates, independent testers, or internal audit. Record what they raised and what you changed (or why you did not). ¹

We deploy a third-party AI product. Are we still on the hook for MAP-5.1?

Yes for your governance program: you still need to document impacts, likelihood, and magnitude for your expected use and environment. Third-party materials can be inputs, but your register should reflect your deployment reality and external feedback. ¹

How do we handle “public incident reports” if we can’t find anything relevant?

Keep a short incident review log that shows what you searched and why you concluded results were not relevant. MAP-5.1 is satisfied by documenting the review and rationale, not by forcing a finding. ¹

Should beneficial impacts be scored with the same rubric as harms?

Use the same scale structure so you can compare across impacts, but define magnitude in benefit terms (for example, improved accuracy, reduced workload, faster response). Document how you will validate benefits post-deployment. ¹

How often should we refresh likelihood and magnitude assessments?

Refresh when the expected use changes or new evidence appears (model updates, workflow changes, incidents, or credible new public reports). Tie refresh triggers to your change management process so updates are automatic, not ad hoc. ¹

Footnotes

1. NIST AI RMF Core

2. NIST AI RMF program page