EU AI Act Compliance Requirements: Building Your AI Governance Framework

The AI Act establishes seven core requirements that high-risk AI systems must meet throughout their lifecycle.

Organizations must implement continuous risk management covering the entire AI system lifecycle. This includes identification and analysis of known and foreseeable risks, estimation and evaluation of risks when the system is used as intended, evaluation of risks from reasonably foreseeable misuse, and adoption of appropriate risk management measures.

Risk management must be iterative, updated throughout the system's lifecycle, and documented comprehensively.

Training, validation, and testing datasets must meet quality standards including relevance to the intended purpose, representativeness of the intended use population, appropriate statistical properties for the application, and examination for possible biases.

Data governance practices must address data collection, preparation, labeling, and validation processes. Organizations must document data provenance and maintain data quality throughout the system lifecycle.

Providers must compile technical documentation demonstrating compliance before market placement. Documentation must cover general system description, detailed description of system elements, description of development and training processes, description of measures for human oversight, and information for deployers and users.

This documentation must be kept up-to-date and made available to market surveillance authorities upon request.

High-risk AI systems must include automatic logging of events relevant to identifying national-level risks and substantial modifications. Logs must enable tracing the system's operation and facilitate post-market monitoring.

Logging periods must be appropriate to the intended purpose of the system and must comply with relevant data protection requirements.

AI systems must be designed to ensure transparency for deployers. Information provided must include provider identity and contact details, system characteristics and capabilities, intended purpose and limitations, accuracy levels and known risks, and input data specifications.

High-risk AI systems must be designed to allow effective oversight by natural persons. Human oversight must prevent or minimize risks to health, safety, or fundamental rights and enable intervention, correction, or system shutdown.

Oversight measures must be appropriate to the risk level and degree of autonomy of the AI system.

Systems must achieve appropriate levels of accuracy, robustness, and cybersecurity. This includes resilience against errors and inconsistencies, protection against unauthorized modification, and appropriate handling of adversarial inputs.

Before placing high-risk AI systems on the market, providers must undergo conformity assessment. This process varies based on the AI system type.

For products covered by existing EU legislation, conformity assessment integrates with existing procedures. For standalone high-risk AI systems, providers may use internal control procedures or third-party assessment by notified bodies.

Successful conformity assessment results in CE marking and registration in the EU AI database.

Organizations should approach compliance systematically. Phase 1 covers inventory and classification by identifying all AI systems and determining risk levels. Phase 2 addresses gap analysis by assessing current practices against requirements. Phase 3 involves framework development by establishing policies, procedures, and governance structures. Phase 4 focuses on implementation by deploying technical and organizational measures. Phase 5 ensures ongoing monitoring through continuous compliance verification.

Compare approaches: See [EU AI Act vs US Regulations](/articles/eu-ai-act-vs-us-regulation) for global compliance strategies.