Governance Gap Leaves Hospital Networks Exposed to AI System Risks
Hospitals are wiring up AI faster than guardrails appear, risking safety, privacy and uptime. Governance means clear owners, limits, live monitoring and a safe off switch.
Healthcare AI systems need real governance, not hope
"Still lacking is a governance structure for systems coming online to consider whether they're acting within their design constraints, what they're doing to networks and the risks they present," said Richard Staynings, chief security strategist at Cylera.
He's right. Hospitals are wiring up AI, apps and connected devices faster than they're setting guardrails. That gap creates patient safety, privacy and uptime risk you can't outsource to vendors.
What governance means in practice
Governance is not a memo. It's clear ownership, pre-deployment checks, live monitoring and the ability to stop the system safely when it misbehaves. It must tie clinical, security and operational decisions together.
Think of it as a lightweight control system wrapped around each AI-enabled tool: define the limits, watch for drift, and act fast.
A simple model you can apply
- Ownership: Assign an executive sponsor, a clinical owner and a technical owner for every AI-enabled system. Publish a RACI so there's no confusion.
- Design constraints: Document intended use, clinical boundaries, prohibited uses and data access needs. Require human-in-the-loop for any high-risk action.
- Pre-deployment review: Run threat modeling, safety impact analysis and privacy assessment. Validate outputs on real clinical scenarios before go-live.
- Network safeguards: Segment devices, restrict egress, rate-limit APIs and monitor east-west traffic. Treat each new connection as untrusted until proven safe.
- Continuous assurance: Track model performance, input/output anomalies and security events. Log everything to your SIEM and set alerts on drift and policy violations.
- Vendor accountability: Bake requirements into contracts: SBOM, vulnerability disclosure, patch SLAs, PHI handling, model update process and audit rights.
- Change control: Use staging, canary releases, feature flags and a clear rollback path. Maintain a kill switch that clinical and on-call teams can trigger.
Minimum controls: quick checklist
- Strong identity and access management with least privilege and MFA
- Data classification, encryption at rest/in transit and retention rules
- Provenance tracking for datasets, prompts and fine-tunes
- Evaluation suite for accuracy, bias, toxicity and safety edge cases
- Network segmentation, egress controls and API gateways
- Comprehensive audit logging with time sync and tamper protection
- Incident runbooks that cover clinical safety, security and downtime
Metrics that matter
- Percent of AI-connected systems with named owners and approved use cases
- Percent on segmented networks with egress controls
- Time from patch release to deployment on covered systems
- Near-miss and incident rate tied to AI features
- Drift alerts resolved within defined SLAs
90-day starter plan
- Weeks 1-2: Inventory AI-enabled systems and connected devices. Map data flows and interfaces.
- Weeks 3-4: Stand up a cross-functional AI governance board (clinical, IT, security, risk, legal, data science, biomed). Approve the RACI and intake process.
- Weeks 5-6: Define design constraints and review criteria. Update procurement templates and BAAs with AI and security clauses.
- Weeks 7-8: Pilot continuous monitoring on one high-value system. Add drift and anomaly alerts to your SIEM.
- Weeks 9-12: Run a failure simulation and tabletop. Close gaps, publish playbooks and scale to the next two systems.
Standards and helpful resources
Use proven guidance to avoid reinventing the wheel and to speed internal alignment.
- NIST AI Risk Management Framework for a common language and control categories
- HHS 405(d) Health Industry Cybersecurity Practices for sector-specific security safeguards
Build skills across your teams
Governance sticks when clinicians, engineers and security share the same playbook. If you need structured upskilling, explore targeted AI courses by role.
The takeaway is simple: every AI-enabled system needs clear limits, ongoing oversight and a safe off switch. Put the structure in place now, before scale makes it harder and riskier to fix later.
