GOVERN-2.2: The organization’s personnel and partners receive AI risk management training to enable them to perform their duties and responsibilities consistent with related policies, procedures, and agreements.

To meet GOVERN-2.2, you must run role-based AI risk management training for both internal personnel and relevant third parties, then prove it happened and that it maps to your AI policies, procedures, and contractual obligations. Operationalize it by defining training audiences, required modules, completion triggers, and an evidence bundle that stands up in audits. ¹

Key takeaways:

• Training must cover personnel and partners who build, deploy, operate, or oversee AI, not just engineers. ¹
• “Training” must be job-relevant and tied to your AI governance artifacts (policies, procedures, agreements). ¹
• The pass/fail test is evidence: you need assignment records, completion proof, content, and exception handling that auditors can trace.

The fastest way to fail GOVERN-2.2 is to treat it like generic annual compliance training. NIST’s wording is narrower and more operational: your people and partners must receive AI risk management training that enables them to perform their duties consistent with your internal governance and your third-party agreements. That means you need a defined training scope, role-based content, and an execution mechanism that reaches beyond employees to contractors, service providers, and other third parties who touch your AI lifecycle. ¹

For a CCO or GRC lead, the practical challenge is less “what should we teach” and more “how do we prove the right populations were trained at the right times, with content that matches our AI policies and contractual guardrails.” A workable implementation looks like a control: clear owner, cadence and trigger events, audience rules, a training curriculum mapped to obligations, and reliable evidence capture. ²

This page gives requirement-level guidance you can implement quickly: a control design, step-by-step rollout, the evidence bundle to retain, audit questions to pre-answer, and common mistakes that create gaps during customer diligence and regulator inquiries.

Requirement: GOVERN-2.2 training for personnel and partners

Target keyword: govern-2.2: the organization’s personnel and partners receive ai risk management training to enable them to perform their duties and responsibilities consistent with related policies, procedures, and agreements. requirement

Plain-English interpretation

You must ensure:

1. Internal personnel with AI-related responsibilities receive AI risk management training, and
2. Partners (third parties) with AI-related responsibilities also receive appropriate training, and
3. The training is practical for their role and aligned to your policies, procedures, and agreements. ¹

A strong operator interpretation: “If someone can change an AI system, influence model behavior, set requirements, approve use cases, provide data, monitor outputs, or communicate AI-driven decisions, they need training that matches those responsibilities.”

Regulatory text

“The organization’s personnel and partners receive AI risk management training to enable them to perform their duties and responsibilities consistent with related policies, procedures, and agreements.” ¹

What you must do operationally: establish a training program that (a) identifies the in-scope populations, (b) delivers role-appropriate AI risk management training, and (c) demonstrates training aligns to internal governance and third-party obligations through mapping and retained evidence. ¹

Who it applies to (entity and operational context)

Entities

• Organizations developing AI systems
• Organizations deploying or operating AI systems
• Service organizations supporting AI development or operations ²

Operational scope (who must be trained)

Build your audience rules based on AI lifecycle touchpoints, not org chart titles. Typical in-scope groups:

• Governance and oversight: AI governance committee members, risk/compliance, privacy, legal, internal audit, model risk management (where applicable), security leadership.
• Product and delivery: product managers, solution architects, engineering, data science/ML, data engineering, QA, SRE/operations.
• Business functions: marketing or sales teams making AI-related claims, customer support handling AI-driven outcomes, HR teams using AI in hiring workflows.
• Third parties (“partners”): contractors developing models, consultants advising on AI deployment, MSPs operating AI infrastructure, data providers, annotation vendors, and any service provider administering AI tooling. ¹

A practical boundary: if a third party can introduce AI risk to your organization’s customers, employees, or regulated obligations, they are part of your GOVERN-2.2 story.

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

Step 1: Create a control card (make the requirement runnable)

Write a one-page control card that includes:

• Control objective: personnel and partners receive role-based AI risk management training aligned to policies, procedures, and agreements. ¹
• Control owner: usually GRC, with execution support from L&D and vendor/third-party risk management.
• In-scope populations: roles, teams, and third-party categories.
• Trigger events: onboarding into an AI role; access granted to AI systems/data; policy updates; material AI system changes; onboarding of a new third party supporting AI.
• Cadence: defined retraining cycle plus event-driven updates (your choice; document it).
• Exception rules: how you handle temporary contractors, M&A transitions, or delayed training. ²

Step 2: Build a role-based curriculum mapped to governance artifacts

Create modules that map directly to “policies, procedures, and agreements.” ¹

A workable module structure:

• All-hands AI risk baseline (everyone in scope):
  • Approved use and prohibited use of AI tools
  • Data handling rules for AI prompts/inputs/outputs
  • Human oversight expectations and escalation paths
  • Incident reporting and unsafe output reporting
• Builder track (engineering/data science):
  • Data quality and provenance expectations
  • Testing/monitoring responsibilities and documentation standards
  • Secure development practices for AI components
• Operator track (IT/SRE/support):
  • Monitoring obligations, drift/issue triage, rollback triggers
  • Logging, access control, and change management expectations
• Decision-maker track (product/leadership/risk sign-off):
  • Use-case risk assessment expectations
  • Required approvals and gating artifacts
  • Contractual commitments to customers and regulators (where applicable)
• Third-party track (partners):
  • Contractual do’s and don’ts relevant to the service delivered
  • Data use restrictions, subprocessor rules, security and incident notice obligations
  • Your org’s escalation contacts and change notification rules ¹

Keep a simple mapping table: module → policy/procedure/agreement clause/topic. Auditors like to see explicit alignment.

Step 3: Decide training delivery methods for employees vs. third parties

Use delivery that matches the relationship:

• Employees: LMS assignment, onboarding path, and completion tracking.
• Contractors with enterprise identity: treat like employees for LMS and attestations.
• External partners without LMS access: require (a) your training package + attestation, or (b) acceptance of partner’s equivalent training plus a documented equivalency review. ¹

Equivalency review should be a checklist signed by GRC and the service owner. Store it with the third-party file.

Step 4: Integrate training into access, procurement, and third-party onboarding

Training becomes enforceable when it gates something real:

• Access gating: require training completion before granting access to AI tooling, model repos, or sensitive datasets.
• Procurement gating: add a step in third-party onboarding to identify AI-touching services and assign partner training/attestation requirements.
• Change gating: if a policy, procedure, or agreement changes, trigger a targeted update module for impacted roles. ²

Step 5: Define the minimum evidence bundle (what you must be able to produce fast)

For each training cycle or trigger event, retain:

• Audience definition and assignment logic (role list, partner categories)
• Training content (slides, videos, job aids) and version history
• Mapping of content to AI policies/procedures/agreements
• Completion records (LMS export or signed attestations)
• Exception log and approvals (who waived, why, compensating controls)
• Communications artifacts (assignment emails, onboarding checklist entries)
• Periodic control health check results and remediation tickets to closure ²

Step 6: Run control health checks (prove it operates, not that it exists)

Health checks should verify:

• In-scope population coverage is current (new hires, role changes, new partners)
• Training content matches current policies and key agreements
• Exceptions are rare, time-bound, and approved
• Evidence is complete and centrally stored ²

If you use Daydream, treat this as a single control with recurring evidence requests, auto-reminders to control owners, and an audit-ready evidence folder per cycle. Daydream fits well when you need to unify training evidence across HR/LMS, security, and third-party files without chasing screenshots.

Required evidence and artifacts to retain (audit-ready checklist)

• AI risk management training policy/standard (who must be trained, when, and how)
• Role-to-training matrix (personnel and partner categories)
• Third-party training clause templates (contract language or addendum) and executed agreements where applicable
• Training materials + version control + approval record
• LMS reports or partner attestations (dated, tied to individual/entity)
• Exception register + compensating controls
• Control card/runbook + ownership and cadence
• Health check logs + remediation tracking ³

Common exam/audit questions and hangups

Expect these:

• “Show me the list of personnel with AI responsibilities and proof they completed AI risk training.”
• “Which partners are in scope, and how do you enforce training for them?”
• “How do you ensure training aligns to policies, procedures, and agreements? Show mapping.”
• “What happens when policies change or a new AI tool is introduced?”
• “How do you handle exceptions for short-term contractors?”
• “Prove this is ongoing. What’s your last health check and what did you fix?” ¹

Hangup: teams often have completion data but can’t show why those people were in scope. Keep your scope logic explicit and stable.

Frequent implementation mistakes (and how to avoid them)

1. Training only “AI builders.” Fix: include product, risk, legal, customer-facing teams, and third parties with AI touchpoints. ¹
2. Generic AI ethics content with no connection to your procedures. Fix: map modules to your internal AI policy set and key contract obligations. ¹
3. No partner mechanism. Fix: require attestations or equivalency reviews in third-party onboarding; store in the third-party record. ¹
4. No versioning. Fix: version the course and retain prior versions with effective dates so you can answer “what did they learn at the time?”
5. Evidence scattered across inboxes. Fix: define one system of record for evidence and require uploads as part of completion closeout. ²

Enforcement context and risk implications

No public enforcement cases were provided for this requirement in the supplied source catalog. Treat GOVERN-2.2 as a high-frequency exam and customer diligence item: customers and auditors regularly ask for training coverage and proof of operational governance for AI-related risks. The risk is control failure by evidence gap: you may have training, but cannot demonstrate scope, completion, or alignment to obligations. ²

Practical 30/60/90-day execution plan

First 30 days (stand up the control)

• Name control owner and publish the control card/runbook. ²
• Define “in-scope” roles and third-party categories tied to AI lifecycle activities.
• Inventory the policies/procedures/agreements that training must align to.
• Draft role-to-training matrix and choose delivery method per audience segment.

By 60 days (ship training + capture evidence)

• Publish baseline and role-based modules with versioning and approvals.
• Implement assignment mechanics (LMS rules, onboarding checklists, third-party attestation workflow).
• Create the minimum evidence bundle folder structure and retention standard. ²
• Pilot with one AI product line and one key AI-supporting third party, then correct gaps.

By 90 days (operationalize and prove ongoing operation)

• Expand rollout to all in-scope personnel and prioritized third parties.
• Run the first control health check; log issues and track remediation to closure. ²
• Add triggers for policy updates and new AI system onboarding so retraining happens without ad hoc outreach.
• Prepare an “audit packet” that can be exported on request: scope logic, training content, mapping, completion records, exceptions, and health check results.

Frequently Asked Questions

Do we have to train every employee?

No. GOVERN-2.2 is scoped to personnel and partners who need AI risk management training to perform duties consistent with your policies, procedures, and agreements. Define and document who is in scope and why. ¹

What counts as “partners” for training purposes?

Treat “partners” as third parties that build, operate, support, or materially influence your AI systems or AI-enabled processes. If they can introduce AI risk through their work, include them. ¹

Can we accept a third party’s own AI training instead of ours?

Yes, if you document an equivalency review showing their training covers your required topics and contractual obligations, then retain the review with the third-party record. ¹

How do we show training is “consistent with policies, procedures, and agreements”?

Maintain a mapping table from each training module to specific internal AI policies/procedures and relevant third-party contractual requirements. Keep version history so you can show what applied when. ¹

What evidence do auditors usually ask for first?

They ask for completion records for a defined in-scope population, the training content, and proof of how you decided who was in scope. Have those three items exportable on demand. ²

Our partners won’t access our LMS. What’s the practical alternative?

Provide a short partner training package (or approved summary), require a signed attestation, and embed the requirement into onboarding and contract language so it’s enforceable. Store attestations in your third-party repository. ¹

Footnotes

1. NIST AI RMF Core

2. NIST AI RMF 1.0

3. NIST AI RMF 1.0; Source: NIST AI RMF Core