EU AI Act Compliance Guide

Establish, implement, and maintain a risk management system throughout the AI lifecycle. Covers risk identification, evaluation, mitigation, and continuous monitoring.

Training, validation, and testing data must meet quality criteria. Covers collection processes, representativeness, bias examination, and data governance practices.

Technical documentation must be prepared before market placement and kept up to date. Must demonstrate compliance and contain Annex IV elements.

Systems must allow automatic recording of events (logs) over their lifetime, enabling traceability and post-market monitoring.

Systems must be transparent. Deployers must receive instructions covering intended purpose, performance, limitations, human oversight, and maintenance.

Systems must be designed for effective human oversight. Humans must understand capabilities, monitor operation, and be able to override or halt the system.

Systems must achieve appropriate accuracy, be resilient to errors and adversarial attacks, and resist unauthorized third-party manipulation.

Provider obligations: ensure compliance, maintain quality management, keep documentation, perform conformity assessment, and cooperate with authorities.

Providers must implement a documented quality management system covering compliance strategy, design control, testing, data management, risk management, and accountability.