AI Agents in Healthcare: Security Risks Every Developer Should Know

AI in healthcare is moving past simple chatbots. The real shift now is toward agents that can summarize patient histories, retrieve notes, route tasks, draft responses, and interact with systems across the care workflow. That sounds useful because it is. But it also changes the security model completely. You are no longer securing a text generator. You are securing a semi-autonomous system that can observe data, reason over it, and sometimes act on it. That is a much riskier class of software. LangProtect explains this well in its post on securing AI agents in healthcare. The core issue is simple: once AI systems move from passive chat to active workflows, the PHI exposure surface gets much larger. Instead of just generating text, agents start touching EHR data, APIs, inbox workflows, and decision paths that were never designed for probabilistic systems. If you are building in this space, the biggest mistake is assuming the main risk is model accuracy. It is not. A wrong answer is bad.