MAP-4.2: Internal risk controls for components of the AI system, including third-party AI technologies, are identified and documented.

MAP-4.2 is a documentation and accountability requirement disguised as a mapping exercise. If you cannot point to the specific controls that manage the risks of each AI component, you will struggle to defend the system’s risk posture, onboard new third-party AI capabilities safely, or pass a serious internal audit. The requirement explicitly includes third-party AI technologies, which means your control environment must extend beyond what you build in-house to what you buy, call via API, or embed through pre-trained models and libraries ¹.

For a Compliance Officer, CCO, or GRC lead, the fastest way to operationalize MAP-4.2 is to treat it as a structured control-to-architecture mapping. You produce an authoritative list of AI components, define the risk control objectives that apply (security, privacy, model governance, data governance, monitoring, change control, incident response), and record the control owner and evidence that proves the control operates. Then you wire it into your intake and change-management workflows so new components cannot ship without being mapped and approved.

NIST AI RMF is a framework, not a law, but MAP-4.2 creates a defensible baseline for governance and exam readiness across sectors ². Auditors and regulators often ask for this outcome even when they do not use NIST’s numbering.

Regulatory text

Excerpt: “Internal risk controls for components of the AI system, including third-party AI technologies, are identified and documented.” ¹

Operator interpretation: You must (1) identify the components that make up the AI system, (2) identify which internal risk controls apply to each component (including components sourced from third parties), and (3) document the result in a maintained record that supports governance, testing, and change control ¹. Documentation must be good enough that a reviewer can understand coverage, gaps, ownership, and how you know the control operates.

Plain-English interpretation (what MAP-4.2 really demands)

MAP-4.2 expects a “control overlay” for your AI system:

• Every component is named and scoped. If it contributes to training, inference, ranking, moderation, retrieval, decisioning, or output delivery, it counts.
• Every component has controls. Controls can be preventive (access control), detective (monitoring), or corrective (rollback).
• Third-party AI is included. If you rely on an external model, hosted endpoint, embeddings service, model monitoring tool, data labeling shop, or evaluation vendor, you still need internal controls that manage the risks of that dependency ¹.
• Documentation is the deliverable. You are building an artifact that stays current and ties into how the business actually ships changes.

Who it applies to (entity and operational context)

MAP-4.2 applies to any organization developing or deploying AI systems ¹. In practice, it is most relevant when:

• You have production AI that affects customers, employees, pricing, eligibility, safety, financial decisions, or regulated workflows.
• You use third-party AI technologies such as hosted LLM APIs, pre-trained models, AI feature stores, vector databases, evaluation tools, or managed MLOps platforms.
• Multiple teams touch the stack (data science, engineering, product, security, legal, procurement), and responsibility can blur without an explicit control map.

If you are only experimenting, MAP-4.2 still matters. The “internal risk controls” may be lightweight (sandbox restrictions, no production data), but they must still be identified and documented if the system is considered part of your AI environment ¹.

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

Step 1: Define the AI system boundary (what’s “in scope”)

Create a one-page scope statement:

• System purpose and users.
• Where training happens (if any) and where inference happens.
• Data flows: inputs, processing, outputs, storage.
• Environments: dev/test/prod.
• Third parties involved (AI and non-AI).

Output: AI System Scope & Boundary document owned by a named function (usually GRC with Engineering).

Step 2: Build a component inventory (component register)

List components at a level that supports control decisions. Common component categories:

• Models (first-party and third-party), including versions.
• Data pipelines and datasets (training, fine-tuning, retrieval corpora).
• Feature stores / embedding models / vector databases.
• Inference endpoints, gateways, prompt orchestration, agents.
• Human-in-the-loop tooling (review queues, labeling platforms).
• Monitoring, evaluation, red-teaming tools.
• CI/CD and model registry artifacts.

For each component, capture:

• Owner (team and accountable individual).
• Deployment location (cloud account, region, on-prem).
• Third-party relationship (contract, DPA, sub-processors if tracked).
• Change triggers (what constitutes a “material change”).

Output: AI Component Register (spreadsheet, GRC tool, or CMDB extension).

Step 3: Identify internal risk controls (control library relevant to AI components)

Start from what you already have:

• Information security controls (access, logging, vulnerability management).
• Privacy controls (data minimization, retention, DPIA triggers).
• SDLC controls (code review, approvals, segregation of duties).
• Third-party risk controls (due diligence, contract clauses, ongoing monitoring).
• Model governance controls (model approval, evaluation, monitoring, rollback).

Then add AI-specific control statements if missing, such as:

• Pre-deployment evaluation requirements for safety, bias, and performance.
• Prompt and output logging policies with privacy constraints.
• Guardrails for prohibited use cases and restricted data types.

Output: AI Control Library (can be a subset view of your enterprise control library).

Step 4: Map controls to components (the core MAP-4.2 deliverable)

Create a Component-to-Control Matrix. Minimum fields:

• Component name and ID.
• Risk themes relevant to that component (security, privacy, safety, reliability).
• Applicable controls (control ID or name).
• Control owner.
• Evidence source (logs, tickets, approvals, test results).
• Frequency/trigger (event-based, release-based, periodic review).
• Residual risk notes and open gaps.

Practical tip: If your matrix contains controls like “security review completed,” require a link to the actual review record. A control without evidence becomes a narrative, not an operational control.

Step 5: Include third-party AI technologies explicitly

For each third-party AI component, document:

• The dependency type (API, embedded model, managed platform).
• Your internal controls around it (not just the provider’s claims), such as:
  • Procurement gating (security/privacy review before purchase).
  • Contractual controls (security addendum, audit rights where feasible, incident notification).
  • Technical controls (API key management, egress restrictions, rate limiting, encryption).
  • Monitoring controls (availability/performance checks; output sampling; abuse monitoring).
  • Exit/rollback plan (fallback model, alternative provider, feature kill switch).

Output: Third-Party AI Dependency Record tied back to the matrix.

Step 6: Operationalize through workflows (so documentation stays true)

Embed MAP-4.2 into:

• AI intake: no new component enters production without a register entry and control mapping.
• Change management: model/version updates require reassessing impacted controls.
• Incident response: component inventory helps scope impact and notifications.

If you use Daydream, set MAP-4.2 as a requirement with a control owner, map it to your policies and procedures, and schedule recurring evidence collection so the matrix doesn’t rot between audits ¹.

Required evidence and artifacts to retain

Keep artifacts in a system of record with access controls and retention:

• AI System Scope & Boundary statement.
• AI Component Register with owners and third-party identifiers.
• Component-to-Control Matrix (the primary MAP-4.2 artifact).
• Control narratives for AI-specific controls (what it is, how it works, who operates it).
• Evidence packets per control (examples):
  • Access reviews, IAM policies, key rotation records.
  • Change tickets and approvals for model deployments.
  • Evaluation reports and sign-offs prior to release.
  • Monitoring dashboards and alert runbooks.
  • Third-party due diligence results and contract addenda (where allowed to store).

Retention should match your enterprise governance approach. The key is consistency and retrieval speed during audits.

Common exam/audit questions and hangups

Auditors and internal risk committees usually push on:

• “Show me all AI components in production. How do you know the list is complete?”
• “Which controls apply to the third-party model API, and who owns them internally?”
• “Where is the evidence that the controls operated for the last major release?”
• “How do you detect unauthorized changes to prompts, models, or retrieval corpora?”
• “What happens if the third-party AI service fails, degrades, or changes behavior?”

Hangups to expect:

• Teams provide a vendor SOC report and call it “control coverage.” MAP-4.2 asks for your internal controls mapped to the component, even if the provider has controls.
• Control mappings exist, but no one can show operating evidence.

Frequent implementation mistakes (and how to avoid them)

1. Inventory stops at “the model.”
   Fix: inventory the full chain: data, retrieval, orchestration, monitoring, and human review tooling.

2. Third-party AI treated as a procurement checkbox.
   Fix: require technical controls (key management, logging, output monitoring) and document them in the matrix.

3. Controls are too generic to test.
   Fix: write control statements that produce evidence (“Model deployments require approval in ticketing system X”).

4. No ownership.
   Fix: every control mapped to a component has a named owner and backup owner.

5. Documentation becomes stale after initial launch.
   Fix: tie the matrix update to change management triggers and periodic attestations; automate evidence collection where possible.

Enforcement context and risk implications

No public enforcement cases were provided in the source catalog for MAP-4.2. Treat MAP-4.2 as a governance expectation that reduces avoidable failures: undocumented components, unmanaged third-party dependencies, and controls that exist only in policy. Those are common root causes of security incidents, privacy issues, and model behavior surprises that escalate into regulatory inquiries and customer escalations.

Practical 30/60/90-day execution plan

First 30 days (Immediate foundation)

• Assign an executive sponsor and an accountable MAP-4.2 owner.
• Produce the AI system boundary for your highest-impact AI system (or the first production system).
• Stand up the AI Component Register and capture all third-party AI technologies in use.
• Draft the Component-to-Control Matrix skeleton with owner fields and evidence placeholders.

Days 31–60 (Control mapping and evidence wiring)

• Build or refine the AI Control Library view from your enterprise control set.
• Complete control mapping for all in-scope components, prioritizing third-party AI dependencies.
• Define evidence sources for each mapped control (tickets, logs, dashboards, approvals).
• Add change triggers: “new model version,” “prompt template change,” “new dataset,” “new third party.”

Days 61–90 (Operationalization and audit readiness)

• Run a tabletop review: pick one component and trace controls to evidence end-to-end.
• Close mapping gaps or document risk acceptance with approvals.
• Implement recurring attestations by control owners and component owners.
• Package a MAP-4.2 audit-ready folder: scope, register, matrix, and sample evidence.

Frequently Asked Questions

Do we need to document controls for open-source model libraries and tooling?

Yes if they are components of the AI system as deployed. Treat open-source dependencies as components, map internal controls like dependency scanning, patching, and change approval to them ¹.

If a third-party model provider has a SOC 2 report, is that enough for MAP-4.2?

A SOC report can support your third-party assessment, but MAP-4.2 still expects your internal risk controls for that component to be identified and documented. Your matrix should show what you do internally to manage access, logging, monitoring, and change risk ¹.

How detailed should the component inventory be?

Detailed enough that controls can be assigned and tested. If you cannot answer “who owns it, what changed, and what controls apply,” the component is not described at the right level.

Who should own the Component-to-Control Matrix: Security, Engineering, or Compliance?

Compliance or GRC usually owns the framework mapping and evidence expectations, while Engineering and Security own operation of the controls. The matrix should make that split explicit with accountable owners per control and per component.

How do we keep the documentation current without creating a paperwork trap?

Tie updates to real workflows: AI intake, deployment approvals, and change management. Automate evidence capture where possible, and require component owners to attest when material changes occur.

What is the minimum acceptable “documentation” format?

A structured register and matrix in a controlled repository is acceptable if it is complete, versioned, and linked to evidence. A slide deck without owners, control IDs, and evidence links usually fails audit scrutiny.

Footnotes

1. NIST AI RMF Core

2. NIST AI RMF program page