MedSecure — Medical Device Breach Response Automation

Stop wiper attacks on surgical robots before they reach the operating room

MedSecure detects and quarantines compromised medical devices in under 3 minutes using ML-powered network anomaly detection trained on HL7/FHIR traffic patterns. When Iran-backed threat actors target medtech infrastructure like the recent Stryker attack, our automated orchestrator isolates infected surgical robots, imaging systems, and infusion pumps while capturing forensic evidence for FDA incident reporting. Healthcare IT teams get real-time SIEM integration and pre-built HIPAA compliance reports without manual threat hunting.

Key Benefits:

- 3-minute automated quarantine of compromised devices using Kubernetes-orchestrated network segmentation, preventing lateral movement to critical surgical equipment

- Zero-touch forensic data capture with TensorFlow anomaly detection across HL7/FHIR medical protocols, eliminating 40+ hours of manual incident investigation

- Pre-generated FDA MedWatch and HIPAA breach notification reports with Kafka-streamed audit logs, reducing compliance response time from weeks to hours

MVP Scope: Automated detection and isolation of wiper malware attacks on medical devices with real-time network anomaly detection, surgical quarantine of compromised devices, forensic data capture, and compliance report generation for healthcare organizations.

Tech Stack: Python, TensorFlow, Kafka, PostgreSQL, Docker, Kubernetes, SIEM Integration APIs, HL7/FHIR

Components:

- Network Anomaly Engine

- Automated Quarantine Orchestrator

- Forensic Data Capture System

- Compliance Report Generator

- Alert & Escalation Dashboard

Quality assessment: Strong domain-specific concept with credible technical architecture (ML anomaly detection on HL7/FHIR, sub-3-minute response) and clear market need (surgical device security), but lacks depth on novel ML approach, competitive differentiation from existing SIEM/IDS solutions, and concrete validation metrics beyond the MVP scope.