A governance framework in AI refers to the structured set of policies, procedures, roles, and oversight mechanisms that guide how artificial intelligence systems are developed, deployed, and managed within an organisation or across a regulatory ecosystem. Governance frameworks are foundational to AI assurance as they provide the infrastructure for ensuring systems are not only effective, but also safe, ethical, and legally compliant.

In the context of high-stakes applications — such as defence, public safety, and critical infrastructure — governance frameworks must address complex risk profiles, dynamic environments, and evolving legal standards. These frameworks help organisations align AI development and deployment with internal controls, regulatory obligations, and stakeholder expectations.

Key components of an effective AI governance framework include:

• Clear roles and responsibilities for developers, deployers, and oversight teams

• Policy guidelines on acceptable uses, data governance, model lifecycle, and oversight requirements

• Risk classification mechanisms that determine assurance levels based on system impact

• Documentation and auditability requirements to ensure traceability and transparency

• Incident response plans for handling failures, breaches, or ethical concerns

From an assurance perspective, the governance framework sets the foundation for how testing, certification, monitoring, and accountability practices are executed. It ensures that assurance is embedded throughout the AI lifecycle — not treated as an afterthought.

Well-structured frameworks also support alignment with external standards and regulations. For example, the EU AI Act requires that high-risk systems be managed under formal risk management and quality management systems. Similarly, NIST’s AI Risk Management Framework offers a voluntary structure that many organisations adopt to guide internal governance.

AI governance frameworks can be internal (organisation-specific) or sectoral (industry-level), and they must evolve as new risks, capabilities, and societal expectations emerge. Assurance providers assess governance maturity to determine whether systems are being managed responsibly, and whether risk mitigations are being implemented effectively.