Certification in the context of AI assurance refers to a formal, independent evaluation process that determines whether an AI system meets specific standards, guidelines, or regulatory requirements. It typically involves assessment by an accredited third-party body and results in the issuance of a certificate of conformity, which serves as official recognition that the system adheres to predefined benchmarks for quality, safety, robustness, and ethical compliance.

Certification plays a crucial role in building trust among users, regulators, and stakeholders, particularly when AI is deployed in high-risk sectors such as defence, critical infrastructure, healthcare, or financial services. Unlike internal evaluations or voluntary claims, certified systems undergo structured scrutiny by an external body, lending credibility to the assurance process and helping organisations demonstrate due diligence.

The certification process often begins with a conformity assessment, during which the system’s design, data management practices, model performance, and operational behaviour are reviewed against established standards. These may include international standards such as ISO/IEC 42001 for AI management systems, ISO/IEC 24028 for trustworthiness, or sector-specific regulatory frameworks such as the EU AI Act. Testing protocols are tailored to the system’s intended use case, risk level, and deployment environment.

Key components typically evaluated during certification include:

• Data provenance and quality controls

• Model robustness and resilience to adversarial inputs

• Performance metrics and limitations

• Governance and risk management structures

• Human oversight and fallback mechanisms

• Transparency, documentation, and explainability

For AI assurance providers, certification is a tool to demonstrate that a system is not only functional but also trustworthy. It enables market access, regulatory approval, and procurement eligibility in jurisdictions where formal validation is required. For governments and organisations deploying AI, certification reduces uncertainty and helps mitigate legal, reputational, and operational risks.

However, certification is not a one-off event. As AI systems evolve — through retraining, new data inputs, or software updates — they may need to undergo re-certification or periodic reassessment. Ongoing monitoring and change management protocols are therefore essential components of a robust certification framework.