Industry Perspectives

A BAA is required before sharing PHI, but it's not enough—pair it with a standard contract to cover SLAs, security, and AI model risks.

Automate PHI key creation, storage, rotation, recovery, and auditing to close lifecycle gaps and protect patient data.

Practical webinar guidance on governing AI in health systems: vendor risk, board oversight, security controls, and lifecycle monitoring.

How healthcare security teams must inventory, limit access, enforce human approval, and monitor agentic AI to protect PHI and compliance.

AI agents acting in EHRs can expose PHI and cause safety issues—use least-privilege, vendor review, logging, and kill switches.

Autonomous systems can speed detection, containment, and vendor-risk review — but they introduce device, safety, and compliance risks that need strict governance.

How agentic AI in triage, documentation, messaging, and care coordination increases PHI, safety, and vendor risk—and which controls prevent harm.

Guidance for healthcare leaders to inventory, restrict, and monitor agentic AI to prevent PHI leaks and prompt-injection attacks.

AI, devices, machine identities, and vendors can turn small flaws into patient harm or PHI exposure; inventory, segment, and monitor.

AI agents speed docs and billing but raise PHI leakage, prompt-injection, unsafe actions, and vendor risk—strict governance required.

How prompt injection and agent hijacking can leak PHI, alter records, and trigger unsafe actions — and the controls to prevent them.

AI agents already expose PHI, enable prompt injections, and can disrupt care—enforce least-privilege, vendor checks, and AI incident response.

Agentic AI lets machine accounts act across EHRs and billing, creating identity, API, and autonomous-action risks that outpace controls.

Map the five stages of AI attacks in healthcare to protect PHI, patient safety, and revenue with inventories, logging, and playbooks.

Clinical AI tools are already vulnerable to prompt injection, data poisoning, and vendor model swaps—an immediate patient safety and privacy risk.

How AI accelerates attacks on hospitals—targeted phishing, rapid lateral movement, model tampering, and expanded vendor risk.

AI can harm patients without hacks—prompt injection, poisoned data, drift, and vendor chains can alter care; treat AI failures as patient safety events.

Behavior-focused strategies to detect and stop AI-driven phishing, deepfakes, adaptive malware, and secure vendor/AI workflows in healthcare.

Healthcare AI risk stems from silent behavior change across training, deployment, and vendor supply chains.

Traditional kill chains blindside healthcare AI: attacks target data, prompts, models, and vendors—not just servers or endpoints.

Reconnaissance enables prompt manipulation and PHI leakage—inventory models, log prompts, and enforce guardrails to prevent clinical AI harm.

How data poisoning, prompt injection, and weak integrations turn healthcare AI into safety risks — governance and monitoring reduce exposure.

Health systems confuse visible AI oversight with real control, producing artifacts instead of measurable risk-reduction.

Map AI workflows, identify kill-chain stages (recon, poisoning, prompt abuse), and apply controls to protect patients, PHI, and uptime.