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This approach separates binding requirements from voluntary frameworks. The European Union’s AI Act is a current binding example; NIST’s AI Risk Management Framework, OECD principles and Singapore’s Model AI Governance Framework are useful governance references but do not replace local legal review.
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What a multi-jurisdiction AI compliance assessment must answer
A defensible assessment should let an auditor, regulator, executive or product team answer six questions for every AI system:
- What does the system do, and what is its intended purpose?
- Which people, organizations or communities can be affected?
- Where is it developed, supplied, deployed, used and monitored?
- What role does your organization play: provider, deployer, importer, distributor or another role?
- Which laws, sector rules, contracts and official guidance apply?
- What controls, evidence, owners and review dates show that risks are being managed?
Do not infer territorial scope from a vendor’s headquarters alone. A system can create obligations because of its users, market, affected people, sector, deployment site or operational connection to a jurisdiction.
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Keep legal conclusions tied to the exact system and version reviewed. A general statement such as “the model is compliant” is too broad to be useful: the answer can change with a new purpose, data source, user group, model version or country.
Step 1: Build an inventory of every AI-enabled feature
Start with an inventory that includes internal tools, customer-facing features, embedded vendor capabilities, pilots and systems used by contractors. “AI system” should not be limited to a product marketed with that label. Include ranking, recommendation, classification, generation, biometric, forecasting and decision-support functions when they can affect people or regulated processes.
Record the system and its purpose
- System name, version, owner and business process.
- Specific intended purpose, prohibited or out-of-scope uses and foreseeable alternative uses.
- Model, API, fine-tuning, retrieval and software dependencies, including vendor and subcontractor dependencies.
- Inputs, outputs, training or reference data, retention periods and data-access permissions.
- Human decision points: who reviews outputs, who can override them and what happens when the system is unavailable.
- Groups affected directly or indirectly, including employees, applicants, customers, students, patients, citizens or children where relevant.
- Development, testing, production, support and retirement dates.
Map data and operational flows
Draw the path from collection to deletion. Identify where prompts, logs, model weights, evaluation data and outputs are stored or processed. Note cross-border transfers, administrator access, support access and onward sharing. The inventory is not a substitute for a privacy or security assessment; it is the factual base those reviews need.
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Capture deployment context
List each country or territory in which the system is offered, operated, monitored or capable of affecting a person. Add the sector and the decision or service involved. A general-purpose assistant used for low-impact drafting may have a different risk profile from the same capability integrated into hiring, credit, benefits, safety or access decisions.
Step 2: Determine roles and territorial connections
Obligations often depend on what an organization does, not simply on who built the model. For each deployment, mark whether your organization develops, provides, deploys, imports, distributes, procures or uses the system. Record contractual relationships separately from statutory roles; a contract cannot change a legal classification by itself.
Use a role-and-territory worksheet
| Question | Evidence to record |
|---|---|
| Who created or substantially modified the system? | Development records, model card, change log and contracts. |
| Who makes it available or places it into service? | Product owner, release record, customer terms and distribution path. |
| Who operates it in a decision or workflow? | Business process, user roles, approval path and operating procedure. |
| Where are people or organizations affected? | User and subject locations, service availability and market scope. |
| Where are development, hosting and support activities performed? | Data-center regions, personnel locations and subprocessors. |
| Which sector or regulated activity is involved? | Process owner, licence, regulator and applicable sector rule. |
Document the reason a jurisdiction is included or excluded. If the answer depends on an unsettled interpretation, flag it for counsel or the relevant regulator instead of converting uncertainty into a confident classification.
Step 3: Screen for legal and governance obligations
Run the inventory through several screens. A system can trigger more than one category, and a voluntary framework can help organize work without creating a legal safe harbour.
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Prohibited or restricted uses
Check current primary law for banned practices, restrictions on sensitive inference, manipulation, discrimination, surveillance, safety-critical use and other prohibited or specially controlled activities. Keep the exact legal text, version date and classification reasoning in the register. Do not rely on a vendor’s marketing description.
Risk classification
Identify whether the jurisdiction uses risk tiers, sector lists, product-safety rules, model-specific duties or another trigger. Record the factual test used and the evidence supporting the result. For the European Union, the AI Act is a binding, risk-based regulation; the Commission’s materials distinguish roles and provide guidance for providers and deployers.
Transparency and human oversight
Check duties to inform people that they are interacting with AI, label or disclose generated content, explain relevant decisions, provide instructions for use, maintain human oversight and offer routes for correction or appeal. The exact trigger and format vary by law and use case.
Data protection, employment, consumer, safety and cybersecurity rules
Map privacy and data-governance requirements alongside employment, consumer-protection, product-safety, accessibility, records-management, cybersecurity and procurement obligations. This article does not establish a complete country-by-country or sector-by-sector inventory; the register must name the current primary source for every conclusion.
Model-provider and general-purpose AI duties
Where a law distinguishes general-purpose models, foundation models or downstream systems, determine whether your organization is a provider, deployer or user under that law. Keep model documentation, evaluation results, usage restrictions and incident contacts linked to the system record.
How major frameworks differ
Compare instruments on legal force, territorial and sector scope, covered roles, definitions, classification triggers, control and documentation duties, transparency, oversight, reporting, post-market monitoring and effective dates. The following comparison prevents a voluntary framework from being mistaken for legislation.
| Instrument | Legal force and purpose | What it contributes to an assessment | What it does not do |
|---|---|---|---|
| European Union AI Act (Regulation (EU) 2024/1689) | Binding, risk-based law. | Role-based obligations, prohibited practices, risk classifications, documentation, oversight and lifecycle duties. | It is not a universal checklist for systems outside its scope; classification and dates must be checked against the current text. |
| NIST AI RMF 1.0 | Voluntary U.S. framework for incorporating trustworthiness into AI design, development, use and evaluation. | A common vocabulary and process for governing, mapping, measuring and managing risk; resources include a Generative AI Profile and crosswalks. | It is not a federal AI compliance statute and does not displace state, sectoral or contractual requirements. |
| OECD AI Principles and policy resources | Principles and policy-coordination material. | A way to compare national approaches and discuss trustworthy, human-centred AI. | They do not determine an organization’s legal obligations in a specific country. |
| Singapore Model AI Governance Framework for Generative AI | Voluntary operational guidance. | Nine dimensions: accountability; data; trusted development and deployment; incident reporting; testing and assurance; security; content provenance; safety research; and public good. | It is not a substitute for Singaporean or other local law, regulator interpretation or counsel advice. |
European Union: use the AI Act as a dated, role-specific reference
Regulation (EU) 2024/1689 is the clearest binding example in the current source set. Read the consolidated text on EUR-Lex, especially Article 9, and verify amendments before relying on a date or classification.
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Article 9 requires continuous risk management
For high-risk AI systems, Article 9 requires an established, implemented, documented and maintained process that identifies and analyzes known and reasonably foreseeable risks, evaluates risks including foreseeable misuse and emerging risks, and adopts targeted measures. The regulation states:
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Treat this as an operating process, not a one-time approval. Link each identified risk to a measure, responsible owner, verification evidence and residual-risk decision.
Application dates stated by current Commission materials
| Date | Scope stated in Commission materials | How to use it |
|---|---|---|
| 2 August 2026 | General application date for the AI Act. | Confirm the current consolidated law and any transition provisions for the system under review. |
| 2 December 2027 | Application date stated for high-risk systems listed in Annex III. | Check whether the use falls within Annex III and whether a later or transitional rule applies. |
| 2 August 2028 | Application date stated for high-risk AI systems embedded in products covered by Annex I. | Coordinate the AI Act review with the applicable product-safety and conformity process. |
The European Commission’s AI Act overview and timeline and enforcement framework were current sources for these dates on 13 October 2026. Dates and transition rules can change; recheck them when publishing or making a compliance decision.
Use the Commission’s checker as orientation
The Commission’s Navigating the AI Act FAQ identifies an official Compliance Checker. Use it to orient stakeholders to possible EU obligations and next steps. It is not a legal opinion, a global checker or a replacement for reviewing the regulation and obtaining advice on an unusual classification.
United States: use NIST to structure work, not to claim legal compliance
NIST AI RMF 1.0 is voluntary. It is useful when several teams or countries need a shared process for trustworthiness, but it does not override applicable federal, state, sector or contractual rules. The AI RMF 1.0 publication, resource page and crosswalks provide the framework, implementation material and mappings to other references.
NIST lists a Generative AI Profile, NIST-AI-600-1, dated 26 July 2024. Record the edition used in your register. A crosswalk can align terminology or reveal missing controls, but it cannot prove that a system satisfies a statute, regulator order or contract.
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Build an obligations register that preserves evidence
A register turns research into an auditable work product. Keep one row per obligation or control rather than one paragraph per jurisdiction.
| Field | What to enter |
|---|---|
| System and version | Unique identifier, model or feature version, environment and review date. |
| Jurisdiction and sector | Country or territory, affected population, market, sector and legal connection. |
| Organizational role | Provider, deployer, importer, distributor, user or another documented role. |
| Source | Law, regulation, official guidance or voluntary framework; include URL, article or section, version and access date. |
| Trigger and classification | Facts that activate the duty and the reasoning for the classification. |
| Requirement | Plain-language obligation, including timing, scope and exceptions. |
| Control | Technical, procedural, contractual or training measure that addresses the requirement. |
| Evidence | Test result, approval, log, notice, contract, training record, incident record or monitoring output. |
| Owner and approver | Named accountable person or team, escalation contact and approval authority. |
| Status and residual risk | Open, implemented, accepted exception or not applicable, with rationale. |
| Review trigger | Law change, guidance update, model release, data change, new use, incident or performance signal. |
Store the classification rationale even when the conclusion is “not in scope.” That record prevents the same question being reopened without the facts and shows how an exclusion was reached.
Assess risk and select controls
Describe foreseeable harm before scoring it
For each use, describe who could be harmed, how the harm could occur, how severe it could be and how likely or difficult to detect it is. Include foreseeable misuse, automation bias, distribution-shift failures, security compromise, privacy exposure, discriminatory impact, unsafe recommendations and loss of meaningful human review where relevant.
If the governing law or framework requires a specific severity or likelihood method, use it and record the scale. If no method is prescribed, document your chosen method rather than presenting a number as an objective fact. A qualitative scale can be useful, but it must be defined and applied consistently.
Match controls to the failure mode
- Data: provenance, quality checks, minimization, access controls, retention and documented limitations.
- Performance: representative evaluation, subgroup testing, calibration, drift monitoring and defined acceptance thresholds.
- Human oversight: trained reviewers, authority to override, workload limits, escalation and a safe fallback when outputs are unavailable.
- Transparency: user notices, instructions, records of AI involvement and explanations appropriate to the decision.
- Security: threat modelling, access control, secrets management, prompt-injection defenses, logging, vulnerability handling and recovery.
- Incident response: detection, triage, notification decision, containment, root-cause analysis and corrective action.
- Accountability: named owner, independent review where warranted, supplier obligations and approval gates for material changes.
Record residual risk and exceptions
After controls are tested, record what risk remains, who accepted it, why the decision is proportionate and when it will be revisited. An exception should have an expiry or review trigger; “accepted indefinitely” is not a monitoring plan.
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- Define scope. List the systems, versions, business processes, jurisdictions, sectors and affected groups included in the review.
- Collect facts. Interview product, engineering, security, privacy, legal, procurement and operations owners; reconcile their answers with architecture and data-flow records.
- Assign roles. Determine the organization’s role in each jurisdiction and document the territorial connection.
- Run legal screens. Check prohibited uses, risk classifications, transparency, oversight, model duties, privacy, employment, consumer, safety, cybersecurity and procurement requirements against current primary sources.
- Choose supporting frameworks. Use NIST, OECD or Singapore guidance to organize controls only where it fits the system and does not obscure binding law.
- Analyze harms. Record foreseeable misuse, severity and likelihood method, affected groups, uncertainty and existing safeguards.
- Design and test controls. Assign owners, define evidence and test controls in realistic conditions, including degraded, adversarial and out-of-distribution cases when relevant.
- Approve or stop deployment. Document residual risk, exceptions, conditions, required notices and the person with authority to proceed.
- Publish operating instructions. Give users clear limits, escalation routes, human-review duties and incident contacts.
- Schedule monitoring. Set review triggers for legal updates, guidance, standards, model or data changes, new users, new countries, incidents and performance drift.
Monitor the assessment throughout the lifecycle
Assign a named owner to watch legal amendments, regulator guidance, standards, enforcement communications, supplier changes and new uses. A quarterly calendar can help, but event-based triggers are essential: a new model release or a move into a regulated workflow should reopen the assessment immediately rather than waiting for a calendar date.
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- Purpose, user population, decision authority or sector changes.
- Model, prompt, retrieval source, training data or vendor changes.
- Deployment country, hosting region, support location or data flow changes.
- Performance, fairness, security or reliability signals deteriorate.
- An incident, complaint, audit finding or regulator inquiry occurs.
- A law, official interpretation, standard or contractual requirement changes.
Keep versioned evidence
Retain dated copies of the classification decision, source text or guidance version, risk analysis, test results, approvals, notices, training records, incidents and monitoring output. Link evidence to the exact model and configuration evaluated. A current policy statement without historical evidence cannot show what was known or controlled at the time of deployment.
Use international guidance without creating false equivalence
The OECD resource on generative-AI governance and OECD AI Principles overview can help teams compare national approaches and express common goals. Singapore’s voluntary framework is especially useful for structuring operational discussions about accountability, data, assurance, security, provenance and incidents. Neither establishes a universal legal test.
Use a crosswalk only after the obligations register identifies the binding duties. If a framework control maps to a legal requirement, keep both references: the law is the reason the control is required; the framework is the method used to organize or implement it.
When to escalate to counsel or a regulator
Escalate when classification depends on an unresolved definition, when jurisdictions give conflicting duties, when a high-impact decision lacks meaningful human review, when evidence is incomplete, or when a proposed use may be prohibited. Also escalate before relying on a voluntary framework as the main compliance argument.
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Ask counsel or the relevant regulator to resolve a narrow, documented question. Provide the system purpose, role, affected people, locations, data flow, model version, legal sources considered and the exact uncertainty. Preserve the response and its date in the register; do not generalize an answer for one deployment to every system.
Official resources for the assessment team
- European Commission AI Act overview and timeline.
- European Commission AI Act enforcement framework.
- Regulation (EU) 2024/1689 on EUR-Lex, including Article 9.
- Navigating the AI Act FAQ and Compliance Checker information.
- European Commission AI Pact, a voluntary initiative for organizations preparing for implementation.
- NIST AI RMF 1.0, its resources and crosswalks.
- OECD generative-AI governance resource and OECD AI Principles.
Use these pages as starting points, then verify the current primary law, regulator interpretation and sector requirements for every jurisdiction in scope.
Final quality check before approval
- Every AI feature and version has an owner, purpose, data-flow record and deployment map.
- Roles and territorial connections are documented rather than inferred from supplier location.
- Each legal conclusion cites a current source, provision, version and access date.
- Binding law is clearly separated from NIST, OECD or Singapore voluntary guidance.
- Risk reasoning covers foreseeable misuse, affected groups, uncertainty and residual risk.
- Controls have named owners and dated evidence, not only policy language.
- Human oversight, transparency, security and incident procedures are operational and tested.
- Exceptions, unresolved interpretations and escalation decisions are recorded.
- Legal, model, data, purpose and deployment changes automatically trigger reassessment.
- EU dates and classifications have been checked against the current consolidated AI Act and Commission materials.
A multi-jurisdiction assessment is complete only when another qualified person can reproduce the classification, see why each obligation applies, inspect the evidence and identify what would cause the decision to change.
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