EU AI Act compliance is an operating model, not a single certificate. IT leaders need to know which AI systems exist, why they are used, which legal role the organisation holds and what evidence supports each decision. The current timeline also matters: Article 50 transparency duties apply from 2 August 2026, while high-risk rules follow later transition dates.
Use the separate risk-classification guide when assessing individual systems.
Record internal, SaaS and API-based AI use. For each system, capture its business owner, vendor, model, intended purpose, affected people, data categories, deployment region and downstream decisions. Include pilots and department-level tools; an inventory limited to centrally procured software will miss shadow AI.
Assign a review date and an accountable owner. An inventory that is not maintained quickly becomes unreliable.
Separate the organisation’s role as provider, deployer, importer or distributor. Then assess prohibited practices, high-risk categories, Article 50 transparency duties and any general-purpose AI considerations. A model is not classified solely by brand or technology: intended purpose and deployment context are central.
Do not automatically label every HR, medical or infrastructure tool high-risk, and do not assume an internal tool is exempt. Document the reasoning and escalate ambiguous cases to qualified counsel.
For systems that require controls, define what must exist before release: risk records, instructions, data governance, logging, performance tests, cybersecurity measures, human oversight and incident handling. Keep evidence connected to a specific system version and use case.
Human oversight must be practical. Name the reviewer, define the information they receive and give them authority to pause or override the system.
Request current documentation for the exact product and contract tier. Review data-processing terms, retention, subprocessors, security controls, model-change notices, incident notification and exit arrangements. A provider’s compliance statement does not settle the deployer’s obligations or make the customer’s use case lawful.
Track evidence expiry. Product features, model versions and regulatory guidance change; a questionnaire completed once is not continuous assurance.
Monitor real outcomes, not only model benchmarks. Record material errors, overrides, complaints and incidents. Reassess classification when the intended purpose, data, model or level of autonomy changes. Give employees and affected users a clear route to report problems.
An initial compliance programme is credible when every material AI system has an owner, purpose, role assessment, documented classification, evidence set and review date; high-consequence outputs have meaningful human control; and changes or incidents trigger reassessment. This does not guarantee legal compliance, but it creates a testable governance baseline.
This article provides general information and is not legal advice.
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