GOVERN-1.1: Legal and regulatory requirements involving AI are understood, managed, and documented.

To meet govern-1.1: legal and regulatory requirements involving ai are understood, managed, and documented. requirement, you need a repeatable compliance mechanism that identifies AI-relevant obligations, assigns ownership, maps them to your AI use cases, and preserves evidence that you monitor and act on changes. Treat it like a living control with triggers, reviews, and auditable artifacts, not a one-time memo.

Key takeaways:

• Maintain an AI legal/register and map obligations to specific AI systems and third parties ¹.
• Operationalize ownership, triggers, and evidence via a control card and an evidence bundle ².
• Prove it runs: cadence-based health checks, change management, and tracked remediation through closure ².

GOVERN-1.1 sits in the “govern” function of the NIST AI Risk Management Framework and is easy to under-build because the sentence looks simple. Auditors and customers rarely accept “Legal reviewed it” as a control. They expect a documented method that shows: (1) what legal and regulatory requirements apply to your AI activities, (2) who owns them, (3) how you track changes, and (4) how you translate obligations into operational requirements for product, data, security, procurement, and third-party oversight.

This requirement matters even when no AI-specific law applies to you. Most AI risks are regulated through existing regimes (consumer protection, privacy, anti-discrimination, sector rules, marketing claims, records retention, cybersecurity, model risk, and contracting). GOVORN-1.1 forces you to connect those obligations to the AI lifecycle: data sourcing, training, testing, deployment, monitoring, and incident response. The “documented” part is the make-or-break point. If you cannot produce a current obligations inventory plus system-level traceability, you will struggle to defend your governance posture ¹.

Regulatory text

Requirement (excerpt): “Legal and regulatory requirements involving AI are understood, managed, and documented.” ¹

Operator interpretation (plain English)

You must be able to show, on demand, a maintained view of what laws and regulations apply to your AI systems and AI-enabled processes, how those obligations are translated into internal requirements, and how the organization stays current as obligations change ¹.

“Understood” means you can articulate applicability and impact by AI use case, geography, customer segment, and third-party relationship.
“Managed” means there is ownership, a workflow, and escalation paths when obligations change or gaps are found.
“Documented” means the whole mechanism leaves a trail that an examiner, customer, or internal audit can follow without interviewing five people.

Who it applies to

GOVERN-1.1 applies to:

• AI developers building models or AI features for internal or external use ¹.
• Organizations deploying AI systems, including buying/embedding third-party AI in business processes ¹.
• Service organizations providing AI-enabled services to customers, including managed services and SaaS with AI functionality ¹.

Operational contexts where this becomes “high heat” quickly:

• AI used in customer-impacting decisions (eligibility, pricing, ranking, content moderation, claims, hiring workflows).
• AI processing personal data or sensitive data (even if the model is hosted by a third party).
• AI features marketed as “automated,” “fair,” “unbiased,” “accurate,” or “compliant” (marketing and disclosures create enforceable expectations).
• AI supplied by or embedded within third parties, where contracting and oversight determine what you can prove.

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

Step 1: Define scope and inventory what counts as “AI”

Start with a scoping statement that’s usable in governance:

• What AI system types are in scope (ML models, LLM features, rules-based automation if treated as AI internally)?
• Where AI is used (customer-facing, internal operations, security, finance)?
• Which third parties provide AI components (APIs, hosted models, annotation providers, data brokers)?

Deliverable: AI system/use-case inventory with owners and deployment locations.

Step 2: Build an AI legal and regulatory obligations register

Create a register that is structured for operations, not legal prose. Minimum fields:

• Obligation name and short description (plain language)
• Jurisdiction and applicability criteria (where/when it applies)
• Business unit and AI use cases impacted
• Control expectations (what must be true in design/operation)
• Evidence expectations (what documents prove it)
• Owner (Legal, Privacy, Compliance, Product, Security, HR, Procurement) and approver
• Monitoring trigger (new market entry, new dataset, new model release, third-party change, incident)

Practical tip: A good register has decision rules (“applies if model influences eligibility decisions for consumers”) instead of only citations.

Deliverable: AI obligations register mapped to AI systems.

Step 3: Translate obligations into “control card” runbooks

For each material obligation area, create a requirement control card that an operator can execute. Include:

• Control objective aligned to GOVORN-1.1
• Owner and backup
• Trigger events (release gating, procurement, data onboarding, geography expansion)
• Execution steps (what is reviewed, how approval happens, where recorded)
• Exception rules (who can approve exceptions, what documentation is required)
• Escalation path to the CCO/Legal for high-risk deltas

This is the fastest way to move from “we’re aware of laws” to “we run a control.” ²

Deliverable: Control cards for AI legal/reg monitoring and applicability assessments.

Step 4: Define the minimum evidence bundle (and retention location)

Audits fail on evidence sprawl. Define a standard “evidence bundle” per cycle:

• Inputs: regulatory updates, legal memos, third-party notices, product change tickets
• Decision records: applicability determinations, approvals, sign-offs, exception decisions
• Outputs: updated obligations register, updated policies/standards, updated product requirements
• Retention: single system of record (GRC tool, controlled repository), with naming conventions

Then publish where evidence lives and who must attach it. ²

Deliverable: Evidence bundle checklist + retention map.

Step 5: Implement change monitoring and triggers

Build a lightweight but real “change detection” mechanism:

• Intake channels: Legal newsletter monitoring, procurement notices, security/privacy advisories, customer contract requirements
• Internal triggers: new AI feature request, model retraining, new dataset, third-party model swap, new region launch
• Governance routing: who must be consulted and who can block a release

Deliverable: AI regulatory change intake workflow tied to product and third-party change management.

Step 6: Run recurring control health checks and track remediation

Establish recurring checks that answer two questions:

• Is the obligations register current for our AI inventory?
• Did we execute required reviews and store evidence?

Track issues to closure with owners, dates, and validation steps. ²

Deliverable: Control health check logs + remediation tracker with closure evidence.

Required evidence and artifacts to retain

Keep these artifacts in a controlled repository with access controls and retention rules:

• AI system/use-case inventory (current + prior versions)
• AI legal/regulatory obligations register (current + change history)
• Requirement control cards (approved versions)
• Evidence bundle checklist and examples of completed bundles
• Change monitoring records (alerts received, triage decisions)
• Applicability assessments for new AI systems and material changes
• Approval records (Legal/Compliance sign-off, release gating evidence)
• Exception approvals and compensating controls
• Control health check results and remediation closure evidence ²

Common exam/audit questions and hangups

Expect variations of:

• “Show me your inventory of AI systems and who owns each.”
• “How do you determine which legal requirements apply to each system?”
• “What triggers a legal re-review? Show the workflow.”
• “Provide evidence from the last AI release that legal/reg requirements were assessed.”
• “How do you manage AI-related obligations inherited from third parties?”
• “Show issues found and how you remediated them to validated closure.” ²

Hangups that slow audits:

• No link between the obligations register and the system inventory.
• Reviews happen in email/Slack with no durable record.
• “One-time assessment” with no monitoring for change.

Frequent implementation mistakes (and how to avoid them)

1. Policy-only compliance. A policy that says “we comply with applicable laws” is not a control. Fix: build control cards with triggers and evidence expectations ².
2. Register without decision rules. Lists of laws without applicability criteria become stale. Fix: add “applies if…” conditions tied to use cases.
3. No third-party integration. If a third party provides a model or dataset, you still need to manage obligations. Fix: add procurement triggers and contract review checkpoints into the workflow.
4. Evidence scattered across teams. Audits fail on retrieval, not intent. Fix: standardize the evidence bundle and retention location ².
5. No operational ownership. Legal cannot be the only owner of ongoing monitoring. Fix: split ownership: Legal interprets; Compliance/GRC runs the control; Product/Security execute embedded requirements.

Enforcement context and risk implications

No public enforcement cases were provided in the source catalog for this page, so this guidance stays framework-based. Practically, GOVORN-1.1 is a “defensibility” requirement: when something goes wrong (customer complaint, model incident, regulator inquiry), your ability to produce a dated, owned, and executed compliance mechanism often determines whether the event is treated as a control failure or an unforeseeable defect ¹.

Practical 30/60/90-day execution plan

First 30 days: Stand up the mechanism

• Confirm what counts as AI for governance and create the initial AI inventory.
• Draft the AI obligations register template and populate it for your top AI use cases.
• Create the GOVORN-1.1 control card with owners, triggers, steps, and exception rules ².

By 60 days: Connect it to operations

• Embed triggers into product release and procurement intake (tickets/forms required before launch or purchase).
• Publish the minimum evidence bundle checklist and train owners on where to store artifacts ².
• Run one “tabletop” on a recent AI change to prove the workflow end-to-end.

By 90 days: Prove repeatability

• Execute the first control health check and open remediation items for any gaps ².
• Validate closure for at least one remediation item with evidence.
• Prepare an “audit ready” package: current inventory, current register, last executed review bundle, and health check log.

Where Daydream fits naturally: Daydream works well as the system of record for requirement control cards, evidence bundle checklists, and recurring control health checks. The value is simple: faster retrieval, fewer ad hoc requests, and clean traceability from an obligation to a system to evidence of execution.

Frequently Asked Questions

Do we need a lawyer to “own” GOVORN-1.1?

Legal should own interpretation of laws, but an operator (Compliance/GRC) should own the control runbook and evidence. Examiners usually want named ownership for execution, not just consultation.

We only use third-party AI APIs. Does this still apply?

Yes. You still must understand and document applicable obligations for your use case and how the third party’s terms, documentation, and controls affect what you can claim and prove ¹.

What counts as “documented” for this requirement?

A current obligations register mapped to AI systems, plus execution evidence (reviews, approvals, and change monitoring records) stored in a retrievable location. If decisions live only in chat threads, treat that as undocumented for audit purposes.

How do we avoid boiling the ocean across every jurisdiction?

Start with your highest-impact AI use cases and the jurisdictions where you actually operate or sell. Add decision rules so new jurisdictions and new use cases trigger targeted analysis rather than redoing everything.

What’s the minimum viable evidence bundle for an AI release?

A release ticket referencing the impacted AI system, an applicability check against the obligations register, Legal/Compliance approval (or recorded exception), and any resulting requirement changes (e.g., disclosures, testing, monitoring) stored together ².

How often should we refresh the obligations register?

Refresh on triggers (new AI system, material model/data change, new jurisdiction, third-party change) and run a recurring health check to confirm it stays current ².

Footnotes

1. NIST AI RMF Core

2. NIST AI RMF 1.0