AI-driven medical device security lifecycle management

Hospitals run thousands of connected devices (infusion pumps, monitors, imaging systems) from many vendors with differing telemetry and patch processes. Manual triage of advisories and ad-hoc patching leads to long vulnerability windows, frequent service interruptions, and stretched clinical engineering teams.

Combine device inventory, network telemetry, vulnerability feeds, manufacturer advisories, and clinical schedules into an AI-driven risk engine that produces prioritized, actionable remediation plans subject to human approval and change control. The system emphasizes explainability, constrained automation for high-risk devices, and audit trails for regulators.

• Collect: normalize asset inventory (discovery, serial, firmware), network flows, EDR/OT telemetry, and external CVE/advisory feeds into a unified dataset.
• Analyze: use graph analytics and supervised/unsupervised models to detect anomalous device behavior and to compute a contextual risk score (exposure, exploitability, clinical impact).
• Summarize: apply safe, prompt-engineered language models to distill vendor advisories and map recommended actions to internal playbooks (with confidence and source links).
• Automate: trigger guarded remediation actions (patch scheduling windows, VLAN quarantine, change-ticket creation) with mandatory clinical engineering approval for high-impact devices.
• Govern: log decisions, track metrics, and surface explainable reasons for prioritization to support audits and regulator inquiries.

Illustrative impact: teams typically shorten time-to-detect and time-to-prioritize device issues by 40-70%, reduce the count of unmitigated critical device vulnerabilities by 30-60%, and cut emergency device downtime and clinician disruption by 20-40%. The predictable workflows and audit trails also reduce the effort needed for compliance reviews and internal reporting, improving confidence for boards and regulators.