Healthcare AI Security

HIPAA requires specific security controls for AI systems processing PHI: (1) Access controls: role-based access to AI systems. Clinicians, administrators, and patients see different data. Enforce at the API layer, not just the UI. (2) Audit logging: every AI inference, every data access, every model query must be logged with timestamp, user identity, action, and outcome. Logs must be retained for 6 years. (3) Encryption: AES-256 at rest, TLS 1.3 in transit. PHI must be encrypted at the field level for sensitive data. (4) De-identification: AI models must be trained on de-identified data. PHI must be stripped before data enters any ML pipeline. Use Safe Harbor or Expert Determination methods. (5) Business Associate Agreements (BAAs): any third-party service processing PHI (cloud providers, API services, SaaS tools) must have a BAA in place. (6) Incident response: documented procedures for security incidents involving PHI.

Healthcare AI has security risks that standard healthcare software does not: (1) Model extraction: attackers may attempt to extract the trained model to reverse-engineer clinical decision logic. Protect with model encryption and access controls. (2) Data poisoning: if attackers can influence training data, they can manipulate AI outputs. Implement data provenance and integrity verification. (3) Prompt injection: for LLM-based healthcare assistants, prompt injection attacks could extract PHI or generate incorrect clinical recommendations. Implement input sanitization and output validation. (4) Model inversion: attackers may attempt to reconstruct training data from model outputs. Use differential privacy for models trained on patient data. (5) Inference attacks: attackers may determine whether specific patients are in the training dataset by querying the model. Implement access controls and rate limiting.

The security architecture should include: (1) Network isolation: healthcare AI systems should run in isolated network segments with strict ingress/egress controls. PHI should never traverse public networks without encryption. (2) Identity and access management: integrate with hospital identity providers (SAML, OIDC). Support multi-factor authentication for clinician access. (3) API security: every API call must be authenticated, authorized, and logged. Use short-lived tokens (JWT with 15-minute expiry). (4) Data loss prevention: implement DLP rules that detect and block PHI in AI inputs and outputs. Prevent PHI from being logged in analytics or error tracking systems. (5) Model serving: serve models from HIPAA-compliant infrastructure (AWS GovCloud, Azure Healthcare, on-premise). Never serve clinical models from public inference APIs without a BAA. (6) Penetration testing: conduct regular penetration testing of AI systems, including adversarial testing of model inputs and outputs.

Healthcare AI compliance requires: (1) Risk assessment: conduct a HIPAA security risk assessment before deploying any AI system that processes PHI. (2) Documentation: maintain documentation of security controls, data flows, and access patterns. (3) Monitoring: continuous monitoring of AI system access patterns, data flows, and security events. (4) Incident response: documented procedures for AI-specific security incidents (model compromise, data poisoning, prompt injection). (5) Regular review: quarterly security reviews of AI systems, including access control audits and penetration test results. (6) Vendor management: ensure all third-party AI services have BAAs, SOC 2 Type II reports, and documented security controls.