Harming users’ data privacy

Mitigation strategy

1. **Implement Rigorous Data Minimization and Purpose Limitation** Establish and enforce a principle of data minimization, ensuring that all AI models are trained on and process only the strictly necessary, relevant, and adequate personal or sensitive data required for their specified, explicit, and legitimate function. Furthermore, securely dispose of data when its retention period for a defined purpose has been exhausted, thereby reducing the volume of vulnerable information and the associated legal and security risk profile. 2. **Deploy Advanced Privacy-Preserving AI (PPAI) Techniques** Utilize state-of-the-art PPAI technologies to technically safeguard data. This includes the application of Differential Privacy (DP) to introduce quantifiable, controlled noise into datasets, making it mathematically difficult to infer the data of any single individual from the model's output. Alternatively, leverage Homomorphic Encryption (HE) to permit computation on encrypted data, or employ Federated Learning (FL) to train models across decentralized data sources without requiring the sharing of raw, sensitive information. 3. **Establish a Comprehensive AI Data Governance and Compliance Framework** Develop and operationalize a robust governance framework that includes continuous auditing and adherence to global data privacy regulations (e.g., GDPR, CCPA). This framework must mandate the secure design of data pipelines, implement strong access controls, and require regular vulnerability assessments to ensure that the AI system's data-handling practices and retention policies meet all legal and ethical requirements for safeguarding user privacy.