Model Privacy & Confidentiality

AI systems can unintentionally reveal sensitive information about individuals, organizations, datasets, or proprietary models. As AI adoption grows, privacy and confidentiality risks have become major governance concerns. This lesson explores how organizations protect sensitive information throughout the AI lifecycle by examining privacy threats, confidentiality controls, and governance practices designed to reduce information leakage. Learners will study membership inference attacks, model inversion, differential privacy, synthetic data, encryption technologies, and confidentiality assurance mechanisms. Understanding AI privacy and confidentiality is essential for governance auditors because protecting information is fundamental to trust, compliance, regulatory readiness, and responsible AI operations.