INFO
Governs how personal data is collected, stored, processed, and shared, ensuring AI systems comply with legal standards and embed privacy protections from design to deployment.
Core Dimensions
- Regulatory Alignment: Comply with laws like GDPR, CCPA, and HIPAA
- Data Governance: Define encryption protocols, secure storage, and access controls
- Privacy-by-Design: Embed privacy protections into system architecture from the outset
- Data Minimization: Limit collection to only necessary personal data, retained for minimal duration
- Privacy-Enhancing Technologies (PETs): Encourage use of differential privacy, federated learning, and homomorphic encryption
Strategic Objectives
- Legal Compliance: Meet jurisdictional and industry-specific privacy standards
- Risk Reduction: Minimize exposure to breaches and unauthorized access
- Trust Building: Demonstrate responsible data stewardship to users and regulators
- Supply Chain Accountability: Define third-party data-sharing agreements and oversight
- Incident Preparedness: Establish breach notification protocols and response plans
Implementation Guidance
- Conduct privacy impact assessments during system design and deployment
- Use audit templates to evaluate data handling practices and regulatory compliance
- Maintain versioned governance documents for privacy policies and breach protocols
- Establish cross-functional privacy teams to oversee implementation and review
- Promote user empowerment through accessible consent tools and opt-out mechanisms