AI SECURITY FOR HEALTHCARE
AI Security Assessment for Healthcare
Protect patient data and clinical workflows as AI transforms healthcare delivery.
AISS · HEALTHCARE BUNDLE
Vertical-specific AISS application
AISS, applied to clinical AI — threat profile, cyber-insurance underwriting crosswalk, and the eight priority sub-controls that matter most for HIPAA-regulated AI surface.
- Healthcare AI Threat Profile (10 MITRE ATLAS techniques)
- Healthcare Cyber-Insurance Underwriting Crosswalk (CC-BY-4.0)
- AISS Spec + 8 healthcare-priority sub-controls
AI Is Reshaping Healthcare — But Who Is Securing It?
Clinicians paste patient notes into AI tools for faster documentation. Diagnostic algorithms influence treatment decisions with minimal oversight. Telehealth platforms integrate AI chatbots that handle sensitive intake data. In each case, protected health information (PHI) flows into systems that most security teams have never evaluated — creating compliance gaps that traditional assessments miss entirely.
HIPAA Business Associate Agreements (BAAs) were written for SaaS vendors processing PHI, not for AI providers training on prompts. Most off-the-shelf AI services either decline to sign healthcare-grade BAAs entirely or sign them with narrow scope that excludes the actual risk surface. When that happens, every AI prompt containing PHI becomes a potential reportable breach. The FDA's evolving Software-as-a-Medical-Device (SaMD) framework adds another layer: AI systems that influence diagnosis or treatment decisions may fall under medical-device regulation regardless of how the vendor labels them.
Shadow AI is already the norm in healthcare. Staff adopt consumer AI tools for scheduling, summarization, and even preliminary diagnosis without IT approval. The IBM 2025 Cost of a Data Breach Report found that one in five organizations experienced breaches linked to shadow AI, costing $670,000 more per incident than standard breaches. In a sector where the average breach already costs $7.42 million, that exposure is untenable.
The Health Sector Coordinating Council (HSCC) recognized this urgency by establishing an AI Cybersecurity Task Force in October 2024, with guidance publications rolling out through Q1 2026 covering governance, secure-by-design principles, and third-party AI supply chain transparency. Organizations that wait for final mandates to act will find themselves remediating rather than preventing.
Regulatory & Compliance Landscape
HIPAA
The Health Insurance Portability and Accountability Act sets baseline safeguards for PHI — but its rules predate AI. Assessments must evaluate how AI tools handle, store, and transmit protected health information beyond what traditional HIPAA audits cover.
NIST AI RMF
The NIST AI Risk Management Framework provides a structured approach to identifying, measuring, and mitigating risks specific to AI systems — from data bias in clinical algorithms to transparency in automated decision-making.
HSCC AI Cybersecurity Guidelines
The Health Sector Coordinating Council's 2026 AI cybersecurity guidance addresses governance maturity, secure-by-design principles, incident response playbooks, and third-party AI supply chain transparency tailored to healthcare organizations.
HITRUST CSF
HITRUST integrates HIPAA, NIST, and ISO requirements into a certifiable framework. Its AI-related control objectives help healthcare organizations demonstrate due diligence to regulators and business associates.
What We Assess in Healthcare
PHI Exposure in AI Tools
Identify where protected health information enters AI systems — from clinical documentation assistants to AI-powered search — and evaluate data handling, retention, and access controls.
Clinical Workflow AI
Assess AI tools embedded in clinical workflows for documentation, triage, and care coordination, including validation processes and clinician override safeguards.
Medical Device AI Vendors
Evaluate third-party AI components in connected medical devices and diagnostic equipment, covering supply chain transparency, update mechanisms, and vulnerability disclosure.
AI-Driven Diagnostics Oversight
Review governance over AI systems that inform diagnostic or treatment decisions, including bias testing, explainability requirements, and human-in-the-loop controls.
Telehealth AI Security
Assess AI integrations in telehealth platforms — chatbots, symptom checkers, and intake automation — for data encryption, consent management, and PHI boundary controls.
AI Training Data Governance
Evaluate how AI models used in your environment were trained, whether patient data contributed to training sets, and what de-identification and consent controls are in place.
THE ENGAGEMENT
An independent HIPAA AI risk assessment — signed and stood behind
We don't hand you a tool and wish you luck. A named assessor evaluates how your AI handles patient data, scores it against HIPAA and NIST, and signs a report you can put in front of an auditor, insurer, or hospital partner — then keeps it current.
HIPAA AI Risk Assessment (fixed scope)
We map every place AI touches patient data — sanctioned tools, shadow AI, and the AI features buried in your existing SaaS — and assess them against the HIPAA Security Rule and NIST AI RMF. Fixed scope, fixed price, a clear start and end.
A signed, defensible report
You get a scored, evidence-backed report a named assessor signs and stands behind — the AI inventory, where data flows, the gaps, and a prioritized remediation plan. The kind of artifact an auditor, cyber-insurer, or hospital partner accepts as proof.
Refresh & Vendor Watch (recurring)
AI surfaces change weekly. Optional recurring refreshes re-prove your posture as tools, vendors, and standards shift — and Vendor Watch flags when an AI provider you depend on changes its terms or controls. Your evidence stays current, not stale.
Fixed-scope engagements — from a focused assessment for a small team and one framework, to a comprehensive review across your AI tooling and multiple frameworks with technical reporting and follow-up advisory.
Transparent, fixed-scope engagements
No hidden quotes and no surprise overages. We scope the assessment to your AI surface and the requirements that gate you, and map a fixed price to it on a short call.
Glass-Box scoring
Every category score is fully derivable from your answers and the published AISS methodology. Your auditor can reproduce the math from the public spec alone.
Open standard
AISS is published under CC-BY-4.0 at github.com/Ayliea/aiss. Fork it, audit it, or propose changes via the public RFC process — the standard belongs to the practitioner community.
Expert-led, not black-box
Every assessment is delivered by a named assessor who signs the output. You know who ran the engagement, what they assessed, and why the score is what it is.
Fixed-scope engagements. We map a price to your AI surface on the call.
Related Reading
Let's Assess Your Healthcare AI Security Posture
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