New whitepaper released: AI Assurance Forum Report
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AI assurance is the systematic process of evaluating whether an artificial intelligence system meets predefined standards for safety, reliability, fairness, robustness, and compliance. It encompasses a suite of technical and procedural methods that help validate AI performance across its lifecycle — from development and training to deployment and post-market monitoring. The goal of AI assurance is to build justified confidence that the system will function as intended under real-world conditions.
AI assurance bridges the gap between abstract ethical principles and concrete operational safeguards. It is not a single method but a discipline that draws from multiple fields, including software testing, risk management, auditing, governance, and machine learning evaluation. The assurance process often includes testing for bias, explainability, robustness, auditability, and compliance with applicable laws or standards (e.g., ISO/IEC, NIST, EU AI Act).
A core aspect of assurance is the collection and validation of evidence. This evidence may include model test results, system logs, decision rationales, audit trails, training data documentation, and human oversight protocols. The assurance process typically includes independent validation or third-party review to ensure objectivity and credibility.
In high-risk applications — such as defence, autonomous systems, healthcare, and critical infrastructure — AI assurance is essential to prevent harm, reduce liability, and enable regulatory approval. Assurance practices ensure that systems are not only performant in controlled settings but also reliable and secure in unpredictable or adversarial environments.
Key components of AI assurance include:
Pre-deployment testing: Functional, performance, and robustness tests conducted in simulation or lab conditions.
Post-deployment monitoring: Ongoing checks to identify performance degradation, model drift, or unexpected behaviour.
Governance alignment: Ensuring the AI system is used within its intended scope and follows organisational risk policies.
Transparency and documentation: Capturing decisions, limitations, and assumptions for internal review or external audit.
AI assurance also plays a critical role in public trust. By demonstrating that systems have been independently evaluated and verified against clear benchmarks, assurance helps organisations deploy AI responsibly and respond to growing regulatory and societal expectations. As AI continues to be embedded into essential services and infrastructures, assurance becomes a foundational element of responsible innovation.
Whether performed by internal risk teams or external assurance providers, AI assurance offers a structured, evidence-based approach to verifying that AI systems are fit for purpose, aligned with governance requirements, and resilient under operational stress.