Every day, I’m inspired by how AI is transforming healthcare—spotting rare conditions earlier, personalizing treatments, helping clinicians reclaim time with their patients. The pace of change is incredible. But with that momentum comes a growing responsibility to ask the tougher questions.

How do we protect deeply personal health data as it flows through complex, AI-powered systems? Can we trust a model’s output if we can’t fully explain how it got there? Are we ready for threats like data poisoning or model manipulation that didn’t exist a decade ago?

These aren’t just technical challenges. They go straight to the heart of trust—and in healthcare, trust is non-negotiable. A misstep doesn’t just trigger a security incident; it shakes a patient’s confidence, and sometimes, impacts their care.

So what should we be thinking about?

– Privacy: Ensure data is de-identified where possible and governed by strict access controls.
– Model Integrity: Monitor for drift, adversarial inputs, and training data vulnerabilities.
– Governance: Align models with regulatory frameworks (like HIPAA or GDPR) and set up transparent audit trails.
– Incident Response: Prepare for security events specific to AI systems—detection, containment, and recovery may look different than in traditional environments.
– Cross-functional Collaboration: Work closely with clinicians, developers, and policy teams from the beginning—not just at deployment.

As security professionals, we have a unique opportunity to shape these systems with intention. That means building guardrails that evolve with the tech, embedding privacy and resilience into the foundation—not layering it on later.