AI Evaluation Metrics - Security & Data Protection

Definition:
Measures the effectiveness of safeguards protecting user data and AI model outputs from unauthorized access, breaches, or misuse.

Purpose:
Protect sensitive health and personal information, ensuring confidentiality, integrity, and compliance with security regulations.

For Compliance Team:

Security Policies: Develop and enforce policies aligned with standards such as HIPAA, GDPR, and industry best practices.
Risk Assessments: Conduct regular security risk assessments and vulnerability scans.
Access Controls: Define strict access controls and authentication requirements for data and systems.
Incident Response: Establish protocols for detecting, reporting, and responding to security incidents or breaches.
Training: Ensure all staff are trained on security best practices and compliance obligations.
Audits & Certifications: Maintain certifications (e.g., ISO 27001) and prepare for third-party audits.

For Engineers:

Encryption: Implement strong encryption for data at rest and in transit.
Secure Development: Follow secure coding practices and conduct code reviews to prevent vulnerabilities.
Monitoring: Deploy intrusion detection systems and real-time monitoring of data access.
Data Minimization: Limit data collection and retention to what is strictly necessary for operation.
Regular Updates: Patch software and infrastructure promptly to mitigate known vulnerabilities.
Backup & Recovery: Maintain secure backups and tested recovery plans to ensure data availability and integrity.