MedSecLab

MedSecLab is a portfolio-grade reference architecture for securely deploying clinical AI applications in a simulated healthcare environment.

The project connects four related GitHub repositories into one clear story: a security-hardened clinical AI gateway, detection engineering for LLM workloads, adversarial testing, and the homelab infrastructure that ties everything together.

Goal: build a realistic clinical AI security lab using only synthetic healthcare data, then document the architecture, controls, detections, red-team findings, and lessons learned.

Demo Video

End-to-end walkthrough of the clinical AI security pipeline (~2 min).

demo-github.webm

The demo covers:

1. Health check and RAG data ingest (synthetic patients → Chroma)
2. Normal clinical queries (including patient lookup by name)
3. Prompt injection blocks → Wazuh rules 100100, 100101, 100102, 100200
4. PHI probing (allowed by gateway) → Wazuh rule 100300
5. Structured audit logging in security.log

Reproducible scripts: clinical-ai-gateway/demo/ (05-run-full-demo.sh)

Why This Exists

Most homelab projects stop at tool installation: Wazuh, Kasm, OpenEMR, Ollama, or Suricata.

MedSecLab is different. The focus is not simply installing tools. The focus is building a defensible security story around clinical AI:

• How should a local clinical AI system be exposed safely?
• How can synthetic patient records be queried without leaking PHI?
• How can prompt injection and abnormal LLM usage be logged and detected?
• How can red-team findings be mapped to real mitigations?
• How can a small lab simulate enterprise-style healthcare AI security?

Portfolio Repositories

MedSecLab is the umbrella repo. The technical work is split into focused repositories so each hiring audience can quickly evaluate the part they care about.

Repository	Purpose	Status
MedSecLab	Portfolio landing page, architecture, roadmap, demo video	Active
clinical-ai-gateway	Secure FastAPI gateway for clinical LLM/RAG workloads	Active
clinical-ai-detections	Wazuh rules, Grafana dashboards, MITRE ATLAS mapping	Active
clinical-ai-redteam	Garak/PyRIT testing methodology, findings, mitigations	Planned

Main Project Tracks

Track 1: Secure Clinical AI Inference Pipeline

Story: I built and secured an end-to-end RAG pipeline that lets clinicians query synthetic patient records using a local LLM, with PHI redaction, audit logging, and OWASP LLM Top 10 controls.

Planned components:

• Data layer: OpenEMR seeded with Synthea synthetic patient data
• Ingestion: Synthea/FHIR records processed through Microsoft Presidio
• Vector database: Qdrant or Chroma
• Inference: Ollama or vLLM serving a local model
• Gateway: FastAPI service with validation, rate limiting, output filtering, and audit logging
• Access layer: Streamlit or React frontend, optionally accessed through Kasm

Primary repository:

clinical-ai-gateway

Main deliverables:

• ✅ Working secure AI gateway with RAG (Chroma, Presidio, Ollama)
• ✅ Demo video and reproducible demo scripts
• Threat model using STRIDE and optionally LINDDUN
• Security controls mapped to OWASP LLM Top 10, NIST AI RMF, and HIPAA Security Rule technical safeguards

Track 2: SOC Detection Engineering for AI Workloads

Story: I developed and tested custom Wazuh rules that detect prompt injection attempts, model exfiltration behavior, and anomalous API usage patterns specific to clinical LLM deployments.

Planned detections:

• Prompt injection signatures
• Role override and jailbreak-style attempts
• Unusual prompt/token volume
• Off-hours access to clinical AI endpoints
• Repeated PHI redaction triggers from one user
• Model file access or tampering anomalies

Primary repository:

clinical-ai-detections

Main deliverables:

• ✅ Wazuh decoders and 7 detection rules (100100–100401)
• ✅ Example logs, logtest notes, and validation samples
• ✅ 3 Grafana dashboards (security overview, prompt injection, RAG ingestion)
• ✅ MITRE ATLAS mapping (clinical-ai-detections/docs/mitre-atlas-mapping.md)
• ✅ Compliance matrix (HIPAA / OWASP / NIST)

Track 3: Adversarial Testing and Hardening

Story: I conducted a structured red-team exercise against my own clinical AI deployment, documented findings using MITRE ATLAS, implemented mitigations, and retested.

Planned testing:

• Garak LLM vulnerability scans
• PyRIT scenarios
• Manual prompt injection tests
• PHI leakage attempts
• Model extraction and abuse-pattern testing against the lab only

Primary repository:

clinical-ai-redteam

Main deliverables:

• Red-team methodology
• Lab-only test scenarios
• Findings report
• MITRE ATLAS mapping
• Mitigations and retest results

Planned Lab Architecture

The final lab simulates a small healthcare provider network. It does not need to run all services at the same time.

Zone	Purpose	Example Services
Clinical	Simulated healthcare user environment	Win11 workstation, OpenEMR, Synthea data
DMZ	Controlled access layer	Kasm, reverse proxy, AI app gateway
SOC	Monitoring and detection	Wazuh, Suricata, Grafana, Loki
AI/ML	Local AI workload	Ollama/vLLM, FastAPI gateway, Presidio, vector DB
Attacker	Red-team testing	Kali Purple, Garak, PyRIT, Atomic Red Team
Mgmt/Infra	Management services	OPNsense, DNS, Vault, Gitea

Repo Layout

MedSecLab/
├── README.md
├── docs/
│   ├── demo.webm          # End-to-end portfolio demo (~2 min)
│   ├── roadmap.md
│   ├── lessons-learned.md
│   ├── compliance-coverage.md
│   └── runbook.md
├── infra/
│   ├── proxmox/
│   ├── ansible/
│   └── networking/
├── diagrams/
│   ├── network.png
│   ├── data-flow.png
│   └── threat-model.png
└── related-repos.md

Important Rules

• No real patient data will be used.
• All healthcare data must be synthetic.
• This is a lab/reference architecture, not a production healthcare system.
• Red-team content targets only the author’s own lab environment.
• Failures and limitations will be documented honestly.

Portfolio Status

Deliverable	Status
Landing repo (MedSecLab)	✅ Active
Secure clinical AI gateway + RAG	✅ clinical-ai-gateway
Wazuh / Grafana detection stack	✅ clinical-ai-detections
End-to-end demo video	✅ README embed · docs/demo.webm
MITRE ATLAS rule mapping	✅ Phase 3.2A complete
Compliance matrix (HIPAA / OWASP / NIST)	✅ compliance-matrix.md
Structured red-team report	⏳ clinical-ai-redteam
Architecture diagrams & threat model	⏳ Planned
Blog-style writeups	⏳ Planned