Engineering an AI-driven security mesh to detect behavioral anomalies and orchestrate autonomous patching across 50,000+ medical devices without interrupting patient care.
Trusted by Leading Fortune 500 Innovators
Multi-regional hospital network with 50,000+ connected infusion pumps, monitors, and imaging systems.
Cyber-Security Architect + AI Engineer + IoT Specialist embedded within the Bio-Medical Engineering division.
Transitioning from reactive manual patching to autonomous, agentic threat detection and remediation.
Edge-based anomaly detection, Federated Learning for threat signatures, and immutable audit logs.
The hospital network managed an un-inventoryable fleet of 50,000+ IoMT devices, many running legacy firmware with known CVEs. Manual patching was physically impossible and required taking critical devices offline, risking patient outcomes during high-capacity shifts.
The 'Execution Gap' was existential: a single compromised device could allow lateral movement into the EHR system. The enterprise required an autonomous solution that could detect network-level anomalies and deploy patches without human intervention or clinical downtime.
Devices remained vulnerable for months while waiting for manual physical updates.
Agentic automation prioritizes and deploys patches as soon as firmware is validated.
Could only detect known threats, leaving the network blind to zero-day device exploits.
Identifies Peer-Group anomalies (e.g. why is this pump talking to the public web?).
Security updates required clinical downtime, delaying scheduled procedures.
Patches are cached at the edge and deployed during validated idle cycles.
When a threat is detected, the AI Agent 'quarantines' the device at the network level while preserving basic clinical functionality.
Federated Learning patterns ensure that device security metadata is shared for learning without ever moving PHI off-premises.
The system monitors hospital throughput to ensure security workloads never compete with critical device telemetry.
Pre-built device fingerprinting logic for 2,000+ medical device hardware profiles.
Secure multi-node learning templates for HIPAA-compliant distributed AI training.
Real-time dashboards showing fleet security posture and autonomous patch status.
Hardened mTLS templates designed specifically for low-power IoT micro-controllers.
Autonomous patching eliminated the manual bio-medical review backlog entirely.
Behavioral analysis caught lateral-movement attempts that signature-based scanners missed.
Intelligent patch scheduling ensured zero interruptions to active surgical or monitoring sessions.
Client Testimonial
Coretus didn't just build a scanner—they engineered a cyber-physical immune system. For the first time, our 50,000+ medical devices are self-securing, ensuring our patients remain safe while our clinical operations remain uninterrupted.
Chief Information Security Officer