Privacy in AI assurance refers to the protection of individuals’ personal information throughout the AI lifecycle — from data collection and model training to inference and deployment. As AI systems increasingly rely on data to function, ensuring privacy is both a legal obligation and a fundamental ethical requirement.

There are two primary dimensions of AI privacy:

• Data privacy during training: ensuring that personal data is used lawfully and ethically, often through anonymisation, minimisation, or differential privacy

• Privacy in model outputs: preventing AI systems from leaking, reconstructing, or unintentionally revealing personal information during inference or response generation

AI privacy assurance involves:

• Conducting privacy impact assessments to identify risks to data subjects

• Implementing technical safeguards like encryption, access controls, and privacy-preserving machine learning

• Verifying compliance with privacy laws such as GDPR, HIPAA, or sector-specific regulations

• Testing for data leakage, re-identification risk, and model inversion vulnerabilities

Assurance also includes evaluating whether the system respects user consent, aligns with intended data use purposes, and maintains transparency about how personal data is handled.

In sectors like defence, intelligence, healthcare, or smart infrastructure, privacy breaches can have severe legal, operational, and reputational consequences. Assurance helps reduce these risks by embedding privacy protections into system design and validating their effectiveness under real-world conditions.