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The OECD’s Due Diligence Guidance for Responsible AI, published on 19 February 2026, provides a practical structure for this work. Its six-step cycle is to embed policies and management systems, identify and assess adverse impacts, cease/prevent/mitigate impacts, track implementation and results, communicate actions, and provide for or cooperate in remediation when appropriate.
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1. Start with the decision, not the vendor
Write a one-page decision brief before comparing providers. It prevents a persuasive demonstration or a prominent founder from becoming a substitute for evidence.
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- Describe the task, model features, users, human review, and deployment environment.
- List the data involved: personal data, confidential business information, customer content, regulated records, source code, or copyrighted material.
- Identify affected people, including customers, employees, applicants, patients, students, or members of the public.
- List every jurisdiction in which the provider, your organization, users, data, and affected people are located.
- Describe what happens if the system is wrong, unavailable, manipulated, or used outside its intended purpose.
- Record realistic alternatives, including a different provider, an in-house system, a rules-based process, or no automation.
An internal drafting assistant normally has lower consequences than a system that ranks job applicants, advises on health, controls industrial equipment, or influences access to housing or credit. Set risk thresholds before you see vendor scores. For example, you might require demonstrable human review for decisions affecting legal rights, prohibit retention of sensitive prompts, or reject a provider without a tested fallback.
Set decision gates
Turn the brief into pass/fail gates and questions that can be answered with evidence. Typical gates include lawful data handling, minimum security controls, incident-notification time, human override, geographic hosting, continuity planning, and an exit path. A provider that fails a mandatory gate should not be rescued by a high marketing score elsewhere.
2. Map the provider and its relationships
The company named on a sales page may not be the entity that develops the model, hosts your data, operates safety monitoring, or controls updates. Build a relationship map and identify which party is accountable for each function.
Entities to identify
- Contracting entity and model developer.
- Cloud or inference host, data-center region, and backup operator.
- Subprocessors, resellers, system integrators, and support providers.
- Data suppliers, annotators, and partners that contribute training or evaluation material.
- Parent companies, major owners, investors, and entities with board or strategic influence.
- Government, defense, public-sector, or other strategic relationships that could affect access, confidentiality, or continuity.
Ask who can change the model, system prompts, safety filters, retention settings, access controls, pricing, or service availability. Establish who receives an incident report, who can order deletion, and who can restore service if a subcontractor fails. The OECD guidance treats these business relationships as part of responsible-AI due diligence; relevant policies should be communicated to suppliers, sales partners, users, and other relationships, and supplier pre-qualification should reflect the risks of the engagement.
Questions that expose hidden dependency
- Which legal entity signs the contract and where is it established?
- Which entities process prompts, outputs, logs, telemetry, and support tickets?
- Can any subcontractor use our data for training, quality review, or product development?
- How much notice is provided for a material model, policy, ownership, or subprocess change?
- Can we export prompts, configurations, fine-tuned weights, evaluations, and audit records if we leave?
- What is the recovery plan if the provider suffers an outage, sanctions restriction, insolvency, or forced service interruption?
3. Collect policy statements and operating evidence
Policies describe intent. A defensible evaluation asks for artifacts showing how intent is implemented for your use case. Request only material that is relevant and proportionate, but do not accept a generic trust page as a complete assessment.
Governance and accountability
- Board or executive responsibility for AI risk.
- Named owners for safety, privacy, security, legal compliance, and incident response.
- Model or system documentation describing purpose, limits, known failure modes, and prohibited uses.
- Change-management records and the process for approving significant updates.
- Internal escalation, whistleblowing, and user-complaint channels.
Testing and evaluation
- Pre-release and post-release evaluation methods tied to the intended deployment.
- Red-team or adversarial-testing summaries, including scope, date, major findings, and remediation status.
- Performance and failure analysis for relevant languages, populations, domains, and edge cases.
- Monitoring for drift, abuse, privacy leakage, prompt injection, and unsafe outputs.
- Evidence that safeguards are tested after model, policy, or infrastructure changes.
Requesting a test summary is not the same as demanding every sensitive security detail. A useful response explains the methodology, coverage, limitations, and unresolved findings while protecting information that would enable abuse.
Data, intellectual property, privacy, and security
- Data-provenance and rights practices for training, fine-tuning, retrieval, and evaluation data.
- Whether customer content is retained, reviewed by humans, used for training, or shared with subprocessors.
- Deletion, correction, access, portability, and legal-hold procedures.
- Encryption, key management, identity controls, privileged access, logging, vulnerability management, and independent testing.
- Incident classification, notification deadlines, forensic cooperation, and post-incident reporting.
- Controls for tenant isolation, prompt and output logging, backups, and administrator access.
A NIST-hosted comment document on the Govern subcategory “Legal and regulatory” specifically highlights standardizing provider assessments and considering intellectual-property, privacy, and security risks. It is supporting context rather than a binding NIST standard, so use it to sharpen questions, not as a certification.
Rank #2
Human oversight and recourse
Find out when a person must review an output, how that person is qualified, and how an affected individual can challenge an outcome. Ask whether the provider supports explanations, correction, appeal, and restoration after an automated error. If your organization remains the decision-maker, the contract and operating procedure should say so explicitly.
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4. Evaluate political ties without turning them into a verdict
Political exposure matters when it can change the risk of your specific deployment. There is no universal political-affiliation score established by the OECD, the European Commission, or the cited NIST material. Build an organization-specific method and show your reasoning.
Use a four-part evidence record
- Connection: describe the donation, public statement, ownership, executive relationship, lobbying activity, government contract, or policy position.
- Source and date: prefer an official filing, contract, corporate statement, speech transcript, or other primary record. Preserve the exact date and document version.
- Relevance: explain the plausible connection to jurisdictional exposure, customer or employee values, governance, regulatory relationships, continuity, or concentration risk.
- Uncertainty and response: record disputed facts, alternative interpretations, the provider’s explanation, and any mitigation or residual risk.
Decision-relevant questions
- Could a government relationship create access, disclosure, sanctions, export-control, or data-location exposure?
- Could a public policy position affect your ability to sell, operate, hire, or serve customers in a market?
- Does ownership or a close relationship create a conflict with your organization’s commitments or procurement rules?
- Are public statements consistent with written policies, contract terms, and observed actions?
- Would a leadership or ownership change alter the risk, and can you exit if it does?
Do not infer intent from association, treat lawful political activity as proof of misconduct, or use political alignment as a proxy for technical quality. A provider should have an opportunity to clarify disputed information. Record the evidence and the consequence you are managing, not a moral label.
5. Check regulatory applicability by use, role, and location
Regulatory duties depend on what the system does, who supplies it, who deploys it, and where it operates. Do not copy a provider’s claim that a product is “compliant” into your risk register without checking the underlying obligation.
European Union general-purpose AI obligations
European Commission material on general-purpose AI providers describes requirements to establish copyright policies and publish summaries of training content, and it refers to evaluation and documented adversarial-testing duties for relevant provider categories. Confirm the model’s classification, the provider’s role, the effective date, exemptions, and whether your organization has separate deployer duties.
High-risk systems and changing guidance
The Commission page titled Guidelines for providers and deployers of AI high-risk systems says the guidance is a draft and non-binding. The page, last updated 6 July 2026, reported consultation through 23 July 2026 and an updated enforcement timeline: certain high-risk areas from 2 December 2027, and systems integrated into products such as robotics and industrial machinery from 2 August 2028. Treat those dates and interpretations as volatile. Verify the live official position and applicable law before relying on them in a procurement or legal decision.
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Build a jurisdiction matrix
| Question | Evidence to collect | Owner |
|---|---|---|
| What rules apply to the use? | Use classification, sector rules, regulator guidance, and legal advice where needed | Legal or compliance |
| What rules apply to the provider? | Provider role, establishment, service location, and contractual entity | Procurement and legal |
| What rules apply to the data? | Data categories, transfers, retention, deletion, and access controls | Privacy and security |
| What must be communicated? | Notices, records, explanations, user recourse, and regulator reporting | Product owner |
6. Compare business and operational risk on common axes
Only score providers after collecting evidence. Use the same questions, weighting, and evidence standard for every option. Mark a judgment as a judgment; do not present it as a verified fact.
| Axis | What to test | Warning signs |
|---|---|---|
| Use-case fit | Evidence for your task, users, languages, domain, and failure tolerance | Benchmark claims unrelated to your deployment; no edge-case analysis |
| Data and IP | Training use, retention, rights, deletion, transfers, and customer-content controls | Ambiguous terms or unilateral policy changes |
| Security and privacy | Access, isolation, encryption, logging, testing, and incident response | Undated assurances; no notification commitment |
| Governance | Named accountability, evaluations, monitoring, transparency, and remediation | No owner for unresolved findings |
| Legal fit | Provider and deployer duties in every relevant jurisdiction | “Compliant” label without scope or evidence |
| Continuity | Financial and operational resilience, concentration, portability, and fallback | Single dependency; no export or exit plan |
| Political and policy exposure | Concrete effect on access, values, regulation, continuity, or stakeholders | Unverified claims or guilt by association |
| Commercial terms | Total cost, liability, audit rights, service levels, and responsibility allocation | Provider can change material terms without notice |
A transparent scoring method
Choose a simple scale, such as 0 for no evidence, 1 for partial evidence, and 2 for strong evidence. Weight axes according to the decision brief; a rights-affecting system may weight legal fit, human oversight, and recourse more heavily than price. Attach a source, date, evaluator, and confidence note to every score. A low score caused by missing evidence is different from a low score caused by a documented control failure, and both differ from a high score based only on a marketing statement.
7. Apply the OECD six-step due-diligence cycle
The OECD report is 61 pages and is designed as practical guidance for enterprises implementing OECD standards on responsible business conduct and the OECD AI Principles. Its quoted purpose is: “This report provides practical guidance to enterprises for implementing OECD standards on responsible business conduct (RBC) and the OECD AI Principles when developing and using artificial intelligence (AI).” Use the six steps as a living management cycle rather than a one-time questionnaire.
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Approve an AI policy, assign accountable executives, define escalation, train staff, and integrate provider review into procurement, security, privacy, and product development.
Step 2: Identify and assess adverse impacts
Consider direct and indirect effects across the value chain, including suppliers, users, affected people, and communities. Assess severity, likelihood, reversibility, and who bears the harm.
Step 3: Cease, prevent, or mitigate
Stop an unacceptable use, redesign the workflow, add human review, restrict data, negotiate controls, or require the provider to remediate. Prioritize the most severe impacts rather than attempting to fix every issue at once.
Step 4: Track implementation and results
Use measurable control owners, deadlines, test results, incident trends, complaints, and remediation verification. Keep records of systems, risks, feedback, decisions, and actions.
Step 5: Communicate actions
Tell relevant stakeholders what the system does, what safeguards apply, how to raise concerns, and what limitations remain. Match detail to the audience and legal requirements.
Step 6: Provide for or cooperate in remediation
Define correction, appeal, refund, restoration, deletion, and other remedies. Cooperate with legitimate investigations and document whether a remedy actually resolved the impact.
8. Put protections in the contract and workflow
Translate material findings into enforceable terms. Depending on the use, seek:
- Defined purposes, prohibited uses, data categories, retention periods, deletion proof, and limits on training or human review.
- Security requirements, audit or evidence rights, vulnerability disclosure, and incident notification deadlines.
- Advance notice and approval rights for material model, ownership, subprocessors, hosting, or policy changes.
- Evaluation, monitoring, documentation, and cooperation duties, including access to relevant test summaries.
- Human-oversight, user-notice, appeal, correction, and remediation responsibilities.
- Service levels, disaster recovery, portability, export formats, transition assistance, and termination rights.
- Clear allocation of regulatory, privacy, intellectual-property, and third-party claims, subject to negotiated limits.
A contract cannot eliminate every risk. Pair it with technical controls such as data minimization, access restrictions, output review, rate limits, logging, and a tested fallback process.
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9. Decide, record, and monitor
Decision record
Store the decision brief, relationship map, evidence register, scores, assumptions, accepted risks, required safeguards, rejected alternatives, accountable owner, approval date, and next review date. Note which facts were verified, which came from the provider, and which remain uncertain.
Reassessment triggers
- Model, safety-policy, pricing, retention, or terms update.
- Ownership, executive, board, hosting, or major subcontractor change.
- New government or strategic relationship that changes access or continuity exposure.
- Material security, privacy, safety, discrimination, or reliability incident.
- Expansion to a new user group, data type, decision, product, or jurisdiction.
- New legislation, enforcement action, or authoritative regulatory guidance.
- Evidence that a mitigation is ineffective or a promised control is not operating.
Review cadence
Assign a named owner and choose a cadence proportionate to risk. A high-impact deployment may need continuous monitoring and quarterly formal review; a low-impact internal tool may use a longer cycle, provided trigger-based reviews occur immediately. The interval is an organizational control, not a universal number prescribed by the OECD.
10. Common evaluation mistakes
- Relying on a single certification or pledge: it may cover only one control area, date, entity, or service.
- Scoring before gathering evidence: this encourages confirmation bias and hides missing information.
- Equating politics with product behavior: a political signal must be connected to a plausible operational consequence.
- Ignoring the value chain: a reseller, host, data supplier, or integrator can introduce material exposure.
- Treating draft guidance as law: verify current binding rules, dates, and role-specific duties.
- Leaving exit until an incident: test exports, replacement options, and fallback procedures before launch.
- Failing to revisit the decision: AI systems, ownership, policies, and regulation change after procurement.
11. A compact worksheet you can reuse
- Purpose: What exact task will the AI perform, and what decisions remain human?
- Impact: Who could be harmed by an error, misuse, outage, or discriminatory outcome?
- Relationships: Which entities develop, host, process, support, resell, or supply data?
- Evidence: Which dated documents verify governance, testing, data, privacy, security, incidents, and continuity?
- Political relevance: What documented connection could affect jurisdiction, values, regulation, access, or continuity?
- Controls: Which technical, procedural, and contractual safeguards are mandatory?
- Residual risk: What remains after mitigation, who accepts it, and why?
- Monitoring: Who reviews results, when, and which events trigger an immediate reassessment?
This worksheet creates an auditable chain from evidence to decision. It also gives the provider a fair opportunity to correct facts and demonstrate improvements rather than forcing an opaque pass/fail judgment based on reputation.
Frequently Asked Questions
Do a provider’s political donations or public statements prove that its AI is unsafe?
No. They are signals to investigate only when they have a documented connection to your jurisdictional exposure, values, governance, regulatory relationships, continuity, or concentration risk. Technical, legal, privacy, security, and operational evidence must be assessed separately.
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No. The 2026 OECD guidance is practical due-diligence guidance built on responsible-business-conduct principles. It does not certify a provider or replace applicable law, contracts, testing, or professional legal advice.
What should we do if a provider refuses to share evaluation or incident information?
Record the refusal as an evidence gap, assess how it affects your risk threshold, and seek contractual disclosure, independent assurance, compensating controls, a different provider, or a narrower use. Do not convert missing evidence into a favorable score.
How often should an AI-provider assessment be repeated?
Set a cadence based on impact and change velocity, then reassess immediately after a material model, ownership, policy, subcontractor, incident, use, jurisdiction, or regulatory change. Assign a named owner so the review cannot become an unowned task.
Can a contract solve political or concentration risk?
A contract can allocate duties, require notice, support portability, and provide remedies, but it cannot guarantee continued access or eliminate geopolitical, ownership, or market concentration exposure. Maintain monitoring and a tested fallback.
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