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Assess the use, not just the tool: document what the AI will do, who will use it, who could be affected, what data it handles, and where it will be deployed. Then identify the rules that apply to that specific use, choose controls proportionate to its possible harms, and set a named owner and review triggers. A framework can organize this work, but it cannot establish legal compliance by itself.
Start with the real use, not the product label
The same model can present very different risks depending on whether it drafts internal notes, ranks job applicants, or helps determine access to a service. A vendor’s description, an “AI-powered” label, or a classification for one use does not settle the risk of another.
Before assessing controls, create a use record for the particular deployment. Include:
- System: tool, model and vendor identifiers, version if available, connected services, and material vendor settings.
- Purpose and users: intended task, who operates the system, who receives its output, and whether it is used internally or offered to others.
- People affected: groups whose opportunities, rights, safety, services, or treatment could be influenced, including people who do not use the tool themselves.
- Data: input and output data types, whether personal or sensitive information is involved, data sources, and where information is sent or stored if known.
- Decision role: what the system produces, whether it recommends or makes a decision, how much human review occurs, and whether a person can challenge or override the result.
- Deployment context: countries and regions, sector, scale, decision stakes, foreseeable misuse, and what happens when the system is wrong or unavailable.
Keep the record specific enough that another reviewer can distinguish this use from other uses of the same product.
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Identify who is responsible and which rules may apply
Map the organization’s role for the use under review. Depending on what it does, an organization may act as a provider, a deployer, or both. Do not assume that buying a third-party product transfers every obligation to its vendor, or that the same duties apply to each role.
List the places where the tool is developed, supplied, used, and affects people. Then check potentially relevant AI-specific rules alongside privacy and data-protection requirements, sector rules, and employment or consumer requirements. The applicable mix depends on the deployment and jurisdiction; there is no universal legal checklist that can classify every use.
Use the EU AI Act as a scoped example
The EU AI Act is not a single set of identical duties for every AI tool. Its requirements depend on factors that include the system’s intended purpose, risk category, and the organization’s role. For a particular use, check the current binding text and any relevant official guidance rather than inferring a category from a vendor’s marketing or from another use of the system.
The European Commission’s classification guidance is non-binding, and its examples are not exhaustive. A general-purpose product is not automatically low-risk in every deployment. If the intended use or classification is uncertain and consequences are substantial, seek qualified legal and domain review before launch.
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Under Article 9 of the EU AI Act, providers of high-risk AI systems must establish, implement, document, and maintain a continuous risk-management process. The Article addresses known and reasonably foreseeable risks, risks from intended use and reasonably foreseeable misuse, information from post-market monitoring, and targeted risk measures. This is a requirement for high-risk systems, not a universal Article 9 obligation for every AI use.
Article 27 requires a fundamental-rights impact assessment before deployment for specified high-risk uses and specified classes of deployers, including certain public bodies and private entities providing public services. It is not a general impact-assessment mandate for every organization using AI. Check the current consolidated text for the exact scope and any exceptions.
Assess harms, exposure, and controls
Evaluate the use across the people and processes it touches. NIST’s voluntary AI Risk Management Framework identifies trustworthiness characteristics that include validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy enhancement, and management of harmful bias. These are useful lenses for an assessment, not a universal scoring formula.
Ask what could go wrong and who bears the cost
- Could inaccurate, inconsistent, or poorly validated outputs cause a harmful decision?
- Could people be treated unfairly, excluded, misled, or unable to understand or contest an outcome?
- Could data be exposed, used beyond its expected purpose, or entered by someone without authorization?
- Could the system be manipulated, misused, or relied on in a setting for which it was not intended?
- Could automation make it difficult to identify who is accountable or to reverse a harmful outcome?
- Which affected groups face greater exposure, and can the organization detect and remedy harm to them?
For each plausible harm, record its severity and likelihood, who is exposed, whether the impact can be reversed, and what evidence supports the estimate. Note uncertainty where evidence is weak; do not turn incomplete testing into an assurance of safety.
Select controls for the particular deployment
Choose controls that prevent, detect, or contain the identified harms. Depending on the use, these may include data minimization, restricted access, testing against relevant cases and groups, human review, clear user notice, output limits, a fallback process, vendor assurances, and incident response. A human reviewer is only a meaningful control if they have the context, authority, and time to question or override the system.
Record the control owner, how the control will be checked, and the residual risk after controls are applied. If the organization cannot monitor outcomes, correct errors, or contain a failure, that limitation should affect the launch decision rather than disappear into a general risk score.
Make the review repeatable
NIST’s AI RMF is voluntary guidance intended to help organizations incorporate trustworthiness into AI design, development, use, and evaluation. Its Generative AI Profile is a cross-sector companion resource for risks associated with generative AI. Neither resource is a substitute for checking binding law or proving that a specific deployment complies with it. NIST says the framework is being revised, so record the version and date of the guidance used.
- Describe the deployment. Complete the use record, including purpose, system identifiers, people affected, data, decisions, and locations.
- Map roles and jurisdictions. Determine whether the organization is acting as provider, deployer, or both, and identify relevant national, regional, and sector rules.
- Classify the specific use. Apply the relevant legal definitions and official guidance to the intended purpose and context; document uncertainties instead of assuming a category.
- Assess hazards and exposure. Identify foreseeable misuse, affected groups, potential impacts, likelihood, reversibility, and the evidence behind those judgments.
- Approve controls and residual risk. Assign accountable owners, specify how controls will operate, and have an authorized decision-maker accept or reject remaining risk.
- Monitor and reassess. Keep incident channels open, review performance and vendor changes, and update the record when the deployment or governing rules materially change.
- Verify before launch. Compare the assessment with current binding text and official guidance for each relevant jurisdiction; obtain specialist advice for high-consequence or legally uncertain uses.
Track regulatory change without restarting from scratch
As of 4 October 2026, the European Commission’s AI Act overview lists transparency rules as taking effect in August 2026. It describes disclosure duties for specified interactions and certain AI-generated content; this does not mean every AI interaction or output has the same disclosure duty. The overview also says the Act introduces no rules specifically for systems deemed minimal or no risk, which does not remove duties that may arise under other laws.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11The Commission’s high-risk guidance page gives 2 December 2027 for specified high-risk areas, including biometrics, critical infrastructure, education, employment, migration, asylum, and border control, and 2 August 2028 for certain AI systems integrated into products such as robotics and industrial machinery. These are the dates stated on that guidance page; the page describes its guidance as non-binding. Check the current official timeline and applicable text before relying on a date for a deployment.
Maintain a change log with the system and use assessed, reviewer, decision, evidence, and date. Assign someone to monitor official sources and vendor notices, then connect each material change to the affected use records. Practical reassessment triggers include:
- a new purpose, user group, or decision process;
- a different model, major version, vendor, or connected feature;
- a change in data types, source, scale, or handling;
- a new deployment location or jurisdiction;
- a material incident, unexpected outcome, or control failure; and
- a relevant change to binding rules or official guidance.
This trigger list is a governance practice, not a verbatim list of statutory triggers. A focused reassessment can identify which assumptions, controls, or legal conclusions need updating without treating every minor product change as a completely new deployment.
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