We built an AI security platform for a hospital network's connected medical devices. It learns normal device behaviour, isolates threats and applies patches automatically. Mean time to patch fell 92%, with zero clinical downtime.
Industry: HealthcareService: AI & AutomationUpdated
Overview
The challenge
The hospital network had 50,000+ connected medical devices and no full record of them. Many ran older firmware with known CVEs. Patching by hand was not practical, and it meant taking critical devices offline during busy shifts, which put patients at risk.
Unpatched devices left the network open to ransomware, manual security audits were expensive, and gaps in device security created HIPAA liability.
Unpatched devices gave attackers a way in and a path to move across the network.
Manual security audits cost the health system millions in biomedical labour and devices taken out of service.
Weak control over device access to patient health information fell short of HIPAA and OCR technical safeguard requirements.
The solution
Lightweight listeners record each device's traffic and learn a normal baseline for everything from MRI scanners to heart monitors.
An AI engine scores unusual behaviour as it happens and automatically isolates devices that show signs of scanning or data theft.
An automated firmware workflow deploys validated security patches during quiet periods and keeps a full audit trail.
Before and after
Before
180+ days
Devices stayed vulnerable for months while they waited for a manual update.
After
Under 24 hours
Automation prioritises patches and deploys them as soon as the firmware is validated.
Before
Signature-based
Only known threats were caught, so zero-day device exploits went unseen.
After
Behavioural AI
Spots devices acting unlike their peers, such as an infusion pump talking to the public internet.
Before
Service disruption
Security updates needed clinical downtime and delayed scheduled procedures.
After
Zero downtime
Patches are cached at the edge and installed when the device is confirmed idle.
Key features
When a threat is found, the device is quarantined at network level while its basic clinical functions keep working.
Business impact
Zero lateral movement
Federated learning shares what each site learns about device security without moving patient health information off the premises.
Business impact
Built to HIPAA requirements
The system watches hospital activity so security work never competes with critical device monitoring.
Business impact
92% MTTP reduction
Results
92% MTTP Reduction
Automated patching cleared the manual biomedical review backlog entirely.
Before: 180 DaysAfter: 14 Hours
34% Visibility Lift
Behavioural analysis caught lateral-movement attempts that signature-based scanners missed.
Before: 65%After: 99.9%
100% Reliable
Patch scheduling caused zero interruptions to active surgery or patient monitoring.
Before: 99.9%After: 100%
Faster delivery
Tested components saved about 10 weeks of setup work. We spent that time on device baselines and the patching workflow.
Our tested identity module gave each connected device a verified identity on the hospital network.
A reusable federated learning module let sites share device-behaviour models without moving data off the premises.
An existing monitoring platform gave the biomedical team one view of device behaviour, alerts and patch status.
Pre-built access controls limited who could isolate devices or release patches.
Trust and control
Monitoring captures device network behaviour only. No patient health information enters the security platform.
HIPAA-GATEDEvery patch and isolation event is logged to a secure, auditable blockchain ledger.
VERIFIABLE DATACloud-managed edge nodes keep detection under 40ms however many devices are added.
READY TO GROWThe health system owns 100% of the custom threat models and patching logic from completion.
100% OWNEDWe placed lightweight listeners at the edge to learn each device's normal network behaviour. An AI engine scores anything unusual and quarantines a suspicious device while its basic clinical functions keep working. Validated firmware patches then install automatically during quiet periods, and every action is logged.
Mean time to patch (MTTP) fell by 92%, from 180 days to 14 hours. Before, devices stayed vulnerable for months while biomedical staff updated them by hand. Now automation prioritises patches and deploys them once the firmware is validated, which cleared the manual review backlog entirely.
The platform watches device network behaviour only, so no patient health information enters it. Federated learning lets hospital sites share what they learn about device threats without moving patient data off the premises. Every patch and isolation event is recorded on a secure, auditable ledger.
If unpatched devices are a risk on your hospital network, we can help you add monitoring and patching that works around clinical schedules.