How would you approach a SecAI+ risk assessment for a new AI-enabled healthcare product?

• A. Identify assets, data flows, threats (data privacy, safety), regulatory requirements, controls (privacy, safety), incident response planning, and ongoing monitoring
• B. Only consider marketing risks
• C. Focus solely on model accuracy
• D. Ignore incident response planning

Answer: (A)

To perform a SecAI+ risk assessment for a new AI-enabled healthcare product, you should identify assets, data flows, threats to privacy and safety, regulatory requirements, controls, incident response planning, and ongoing monitoring. Start by listing all assets—data types (PHI, clinical data), models, infrastructure, and stakeholders. Map how data moves through the system—from collection to processing, storage, sharing, and disposal—so you can see where exposure or misuse could occur. Identify threats across privacy and patient safety, plus potential adversarial or operational risks such as data breaches, data leakage, or unsafe AI behavior, and align them with applicable regulations like HIPAA, consent rules, breach notification, and any medical-device software guidance.

Define controls to mitigate those threats: data minimization and privacy-preserving techniques, strong access controls and encryption, robust auditing, model validation and safety constraints, and governance processes. Develop an incident response plan that covers detection, containment, eradication, recovery, and communication with patients and regulators, followed by post-incident reviews. Establish ongoing monitoring to catch model drift, data quality issues, security events, and changing regulatory or threat landscapes, keeping the risk posture up to date throughout the product lifecycle.

Other options miss critical aspects: focusing only on marketing risks ignores security and privacy; concentrating solely on model accuracy neglects safety and regulatory obligations; and ignoring incident response planning leaves you unprepared for real-world incidents.