GOVERN-2.1: Roles and responsibilities and lines of communication related to mapping, measuring, and managing AI risks are documented and are clear to individuals and teams throughout the organization.

To meet GOVERN-2.1, you must document who owns AI risk mapping, measurement, and management, and make escalation and communication paths unambiguous across the business. Operationalize it by publishing a role-and-responsibility model (RACI), defining decision rights and triggers, and proving people know how to use the pathways with repeatable evidence. ¹

Key takeaways:

• Write down AI risk ownership, decision rights, and escalation paths in a form teams can execute (RACI + runbooks). ¹
• Tie communication lines to real triggers: model changes, incidents, third-party updates, and approvals. ²
• Keep an “evidence bundle” that proves the roles and communication pathways operate, not just exist on paper. ²

GOVERN-2.1 is a governance execution requirement, not a policy-writing exercise: the organization needs documented, clear roles and responsibilities plus defined lines of communication for AI risk work, specifically for mapping, measuring, and managing AI risks. If you cannot point to named owners, decision-makers, and escalation routes, the AI risk program becomes informal and inconsistent, especially across product, data science, IT, legal, and third parties. ¹

For a CCO or GRC lead, the fastest way to implement GOVERN-2.1 is to treat it like a control you can run: define the minimum set of roles, define the “AI risk workflow” stages and who is accountable at each stage, define escalation triggers, and publish the communication routes in the systems people actually use (ticketing, model registry, SDLC, incident management). Then collect evidence that teams know where to go and that approvals/escalations happen through the defined channels. ²

This page gives requirement-level implementation guidance you can hand to operators: a step-by-step build, the artifact list auditors and customers request, common hangups, and a practical execution plan you can adapt to your organization’s AI footprint. ¹

Regulatory text

Requirement (excerpt): “Roles and responsibilities and lines of communication related to mapping, measuring, and managing AI risks are documented and are clear to individuals and teams throughout the organization.” ¹

Operator interpretation: You must (1) define and document who does what for AI risk mapping, AI risk measurement, and AI risk management, and (2) define and document how information flows between those roles (including escalation). “Clear throughout the organization” means the documentation is accessible, understandable, assigned to real functions or named roles, and embedded into day-to-day workflows. ¹

Plain-English interpretation (what “good” looks like)

A passing implementation has these properties:

• Named accountability for each AI risk activity. People can answer “who owns this?” without debate. ¹
• Decision rights are explicit. Teams know who can approve a model release, who can block deployment, and who must be consulted for high-risk changes. ²
• Communication routes are mapped to triggers. A fairness regression, a data drift alert, a third-party model update, or a customer complaint has a defined path and destination. ¹
• Evidence exists that the pathways operate. You can show tickets, approvals, meeting minutes, incident reports, and training attestations tied to the documented roles. ²

Who it applies to

Entities: Any organization developing or deploying AI systems, including service organizations that provide AI-enabled services or components. ¹

Operational contexts where it matters most:

• Central ML/AI platform teams supporting multiple product lines.
• Decentralized data science teams building models inside business units.
• AI features embedded in regulated workflows (credit, insurance, healthcare operations, HR decision support).
• Material third-party AI dependencies (foundation models, scoring APIs, fraud tools, identity tools), where communication lines must include third-party risk and procurement workflows. ²

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

Step 1: Define the AI risk lifecycle you will govern

Write down your minimum lifecycle stages for AI risk work:

• Map: inventory AI systems, intended use, users, data inputs, and impact areas.
• Measure: evaluate performance, robustness, privacy/security, bias/fairness, and monitoring signals.
• Manage: decide mitigations, accept residual risk, approve release, monitor production, and handle incidents/complaints. ¹

Make this lifecycle the spine of your role mapping. If your organization already has an SDLC, model registry, or change management process, align to it rather than inventing parallel steps. ²

Step 2: Publish a RACI that is specific enough to run

Create a RACI (Responsible, Accountable, Consulted, Informed) for each lifecycle stage and for key governance decisions.

Minimum roles to cover (adapt names to your org):

• Business/Product owner (intended use, customer impact)
• Model owner / ML engineering (build, testing, monitoring)
• Data owner / data engineering (data sourcing, quality, lineage)
• Security (threat modeling, access control, incident response)
• Privacy (data minimization, notices/consent where applicable)
• Legal/Compliance (regulatory alignment, claims review, approvals)
• Risk management / Model Risk (if applicable) (independent challenge)
• Internal Audit (independent assurance)
• Third-party risk / Procurement (external model/service governance) ²

Practical tip: Keep “Accountable” to one role per activity. Shared accountability reads well and fails in incidents. ¹

Step 3: Define lines of communication as a routing model, not a narrative

Document communication in three layers:

1. Operational routing: where work goes (ticket queue, model registry workflow, GRC issue, Slack/Teams channel with owners).
2. Escalation routing: when risk crosses a threshold or timeline, who gets paged and who decides.
3. Governance reporting: recurring reporting path to the AI governance forum, enterprise risk committee, or equivalent. ¹

Your routing model should cover at least:

• Pre-release approvals (who signs off, who can block).
• Material change management (data changes, model architecture changes, prompt/template changes, vendor model version changes).
• Production monitoring and alerts (who receives, who triages).
• Incidents and complaints (who investigates, who communicates externally). ²

Step 4: Create a “control card” runbook for GOVERN-2.1

Turn the requirement into an executable control description:

• Objective: clear ownership and communication lines for AI risk mapping/measurement/management.
• Owner: typically GRC, Model Risk, or an AI Governance Lead.
• Trigger events: onboarding a new AI system, significant model change, third-party model update, incident/complaint, periodic governance refresh. ²
• Execution steps: update RACI, validate contact lists, test escalation path, confirm documentation publishing.
• Exceptions: what happens for prototypes, internal-only tools, or low-risk use cases.
• Outputs: approved RACI, communication matrix, updated runbooks. ²

If you use Daydream, implement this as a requirement control card with owners, triggers, and evidence fields so teams stop treating GOVERN-2.1 as a one-time policy task and start running it as an operational control. ²

Step 5: Train and validate “clarity”

“Clear throughout the organization” needs proof. Use lightweight validation:

• Publish the RACI and routing in a searchable location (policy portal, wiki, GRC system).
• Add it to onboarding for data science/product/security stakeholders.
• Run a tabletop or “routing drill” using a realistic scenario (e.g., drift alert, harmful output, third-party model change). Capture who escalated to whom and what decision was made. ²

Step 6: Operate a recurring health check

Run periodic checks that:

• Role owners are still current (org changes break governance fastest).
• Communication channels still work (queues monitored, distribution lists valid).
• AI inventory changes are reflected in ownership and escalation paths. ²

Required evidence and artifacts to retain (minimum evidence bundle)

Keep these artifacts in a single “GOVERN-2.1 evidence” folder with version control:

• AI governance RACI covering map/measure/manage activities and key decisions. ¹
• Communication & escalation matrix (including incident/complaint routing). ¹
• Control card / runbook for how GOVERN-2.1 is executed (owner, triggers, steps, exceptions). ²
• Org chart snippets or role descriptions showing authority/decision rights for accountable roles. ²
• Training/awareness records (attestations, onboarding materials, completion logs where applicable). ²
• Proof of operation: a sample of tickets/approvals/escalations and governance meeting minutes where AI risk issues were raised and assigned. ²
• Change log showing updates to the RACI and routing after reorganizations or major AI system changes. ²

Common exam/audit questions and hangups

Auditors, customers, and internal assurance teams tend to probe:

• “Who is accountable for AI risk decisions for this specific model?” Bring the RACI plus the model’s owner record. ¹
• “Show the escalation path for a safety or fairness issue found in production.” Provide the escalation matrix and an incident/ticket example. ²
• “How do third parties fit into governance?” Show how third-party risk, procurement, and vendor management are in the routing model for third-party model updates and issues. ²
• “How do you keep this current as teams reorganize?” Show the health check cadence and change log. ²

Hangup to expect: teams will claim governance exists “by practice” in Slack DMs. That fails because it is not documented, not durable, and not testable. ¹

Frequent implementation mistakes (and how to avoid them)

Mistake	Why it fails	Fix
High-level policy language only	Doesn’t assign accountability or routes	Publish a RACI + escalation matrix tied to triggers. 1
“AI governance committee” with unclear authority	Committees discuss, but nobody decides	Define decision rights: who can approve, who can block, who must be consulted. 2
Separate processes for each team	Inconsistent controls across products	Standardize the minimum lifecycle, allow team-specific add-ons. 2
No third-party pathway	External models change outside your SDLC	Add third-party update triggers and routing through procurement/TPRM. 2
No evidence of “clarity”	“Clear” becomes subjective	Train, run a routing drill, and retain artifacts. 2

Enforcement context and risk implications

No public enforcement cases were provided in the source catalog for this requirement, so you should treat this as a governance expectation rather than a penalty-tied rule. ¹

Risk if you under-implement:

• Operational risk: unresolved alerts, duplicated work, or unowned remediation.
• Compliance and customer diligence risk: inability to show accountability, approval pathways, and escalation discipline during assessments.
• Third-party risk: unmanaged model updates or failures that bypass internal review. ²

Practical 30/60/90-day execution plan

First 30 days (stabilize ownership)

• Identify your AI system population sources (model registry, app inventory, procurement list) and pick an authoritative inventory feed for governance routing. ²
• Draft the lifecycle (map/measure/manage) and publish a first-pass RACI for the highest-impact AI systems. ¹
• Stand up an escalation matrix for production issues and third-party model changes. ²
• Create the GOVERN-2.1 control card in your GRC system (Daydream or equivalent) with owner, triggers, steps, and evidence fields. ²

By 60 days (embed in workflows)

• Integrate RACI checkpoints into SDLC/change management: release approvals reference accountable roles. ²
• Add routing into incident management: AI-specific incident category and required notifications. ²
• Publish documentation in a single, searchable place; require it in onboarding for relevant teams. ²
• Define the minimum evidence bundle and start collecting artifacts for at least one end-to-end cycle (release or change). ²

By 90 days (prove it operates)

• Run a tabletop test of the escalation path (drift or harmful output scenario) and document outcomes and action items. ²
• Perform the first control health check: validate owners, distribution lists, queues, and committee reporting. ²
• Close gaps with tracked remediation items and validated closure evidence (tickets, updated docs, approvals). ²

Frequently Asked Questions

Do we need named individuals, or can we assign roles by function?

Start with functions for durability, then map each function to current named role-holders in an owned contact list. Audits usually fail when “Legal” is listed but no one can show who responds to escalations. ¹

We have multiple AI teams. Do we need one RACI per team?

Maintain one enterprise baseline RACI and allow appendices per team for local specifics. The baseline keeps decision rights consistent, and the appendices reflect real operating differences. ²

How do we cover third-party AI services under GOVERN-2.1?

Add third-party risk, procurement, and the service owner to the RACI, and define triggers for vendor model changes, outages, and security events. Keep evidence that those triggers route into your internal approval and incident processes. ²

What counts as “lines of communication” in practice?

A documented routing path that people actually follow: ticket queues, approval workflows, incident paging, governance forums, and escalation contacts. A diagram helps, but you still need examples of it being used. ¹

We’re early-stage and moving fast. What’s the minimum viable implementation?

Publish a lightweight RACI for map/measure/manage, define one escalation channel for AI issues, and store evidence of approvals and escalations for each production release. Expand depth as your inventory and risk grow. ²

How do we demonstrate “clear throughout the organization” without heavy training programs?

Require a short onboarding module for impacted teams and run an escalation drill. Keep attendance/attestations and the drill record as evidence that people know where to go. ²

Footnotes

1. NIST AI RMF Core

2. NIST AI RMF 1.0