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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsThere is no single universal owner, documentation checklist, or review calendar for AI compliance. Responsibilities depend on the jurisdiction, the organization’s role, the system’s intended purpose and risk category, and the laws that apply. The EU AI Act sets role-specific duties for covered systems; the NIST AI Risk Management Framework (AI RMF) is voluntary U.S. guidance, not a generally binding compliance law. For any organization, a practical starting point is to assign owners, document use and risk, retain evaluation and decision records, and set a review schedule proportionate to risk.
Who is responsible for AI compliance?
Responsibility is shared across the people and organizations involved in building, supplying, deploying, and overseeing an AI system. A governance lead can coordinate the process, but should not automatically become the sole accountable owner. NIST recommends defining roles and responsibilities across AI risk management; the EU AI Act assigns duties according to the actor and the system’s scope.
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Provider responsibilities under the EU AI Act
For covered high-risk AI systems, providers must establish, document, and maintain a risk management system; prepare and keep technical documentation up to date; and establish proportionate post-market monitoring. These are examples, not an exhaustive account of every duty, exception, or system category. Consult the consolidated EU AI Act text to determine which provisions apply to a particular system and actor.
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Deployer responsibilities under the EU AI Act
For covered high-risk systems, deployers must take measures to use the system in accordance with its instructions, assign human oversight to people with appropriate competence, training, authority, and support, and monitor system operation. The applicable obligations depend on the system, actor, and circumstances.
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Practical ownership inside an organization
For each AI use case, identify an accountable executive or business owner, operational and technical contacts, and the people authorized to approve changes or pause use. Define escalation and incident routes. This is practical governance advice, not a claim that every organization is legally required to use those exact job titles or structures.
What should be documented for AI systems?
A useful governance record should help the organization understand what the system does, who is responsible for it, how risks were assessed, and what happens when conditions change. The following is a practical baseline, not a universal statutory checklist.
- An inventory of AI systems and use cases, including an owner and current status.
- The intended purpose, users, affected people, operating context, and important dependencies.
- Provider, deployer, and internal role assignments, including decision and escalation authority.
- Risk identification, assessment, prioritization, mitigations, residual-risk decisions, and approvals.
- Relevant data and system information, subject to privacy, security, trade-secret, and other legal constraints.
- Pre-deployment and in-operation evaluation plans, tests, metrics, limitations, and results.
- Human oversight arrangements, monitoring signals, incident handling, and change records.
- Review dates and outcomes, changes made, and the reasons for accepting, reducing, or escalating risks.
NIST’s AI RMF supports inventories and systematic documentation as aids to transparency and accountability. Its Measure function covers testing, performance assessment, uncertainty, benchmarking, monitoring, and documentation. See the NIST AI RMF Playbook’s Govern guidance and the AI RMF 1.0 publication.
Additional documentation for covered high-risk systems
Under the EU AI Act, providers of covered high-risk AI systems must prepare technical documentation before placing the system on the market or putting it into service, and keep that documentation up to date. It must demonstrate compliance and provide information needed for assessment. The Act also addresses logging, risk management, and post-market monitoring. Exact requirements depend on the relevant provisions, the actor and system classification, and any applicable exceptions.
How often should AI systems be reviewed?
The sources covered here do not establish one universal interval for every AI system. NIST says organizations should plan ongoing monitoring and periodic review, with the organization determining the frequency. It also calls for testing before deployment and regularly during operation.
For covered high-risk systems, the EU AI Act describes risk management as a continuous, iterative lifecycle process that requires regular systematic review and updating. It separately provides for deployer monitoring of operation and provider post-market monitoring. The cited provisions do not set one number of months between every review.
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Set a risk-proportionate schedule
Choose a baseline review cadence that reflects the system’s risks and potential consequences, and record who sets it. Define how monitoring results can bring a review forward. As practical triggers, consider a material change in intended use, model or data, deployment environment, affected population, supplier, observed performance, or incident history. This trigger list is implementation advice, not a quoted legal requirement.
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How should an organization compare frameworks and responsibilities?
Before deciding what process to follow, identify the law and guidance that apply to the particular use case. The EU AI Act is a regulation within its scope; NIST AI RMF 1.0 is voluntary guidance. NIST’s resource page says version 1.0 is being revised, so check it for updates. The material here covers those sources, not every national, state, or sector-specific rule.
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- Legal force and geography: Is the source binding regulation or voluntary guidance, and does it apply in the relevant jurisdiction?
- Actor and system scope: Is the organization acting as provider, deployer, or another type of actor, and what category and intended use does the system have?
- Risk approach: How does the source address classification, assessment, mitigation, and monitoring?
- Evidence: What inventories, technical documentation, logs, evaluations, approvals, and post-deployment records are relevant?
- Oversight and operations: Who reviews outputs, monitors behavior, handles incidents, and can pause use?
- Review expectations: Does the source set a fixed interval, or does the organization need to establish a proportionate cadence?
The EU obligations and NIST’s Govern, Map, Measure, and Manage functions provide different but complementary reference points. The NIST AI RMF Core describes the framework; the NIST AI RMF resource page provides current framework information.
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