AI SECURITY FOR HEALTHCARE
HIPAA AI Risk Assessment for Healthcare
Protect patient data and clinical workflows as AI transforms healthcare delivery.
FRAMEWORK CROSSWALK · HEALTHCARE
Vertical-specific framework crosswalk
Our framework crosswalk, applied to clinical AI — threat profile and the eight priority sub-controls that matter most for HIPAA-regulated AI surface.
- Healthcare AI Threat Profile (10 MITRE ATLAS techniques)
- Control-domain crosswalk + 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
Independent AI security evidence for healthcare review
Ayliea does the assessment work rather than handing you another compliance tool. The engagement turns your AI systems, data flows, safeguards, and gaps into a signed package a reviewer can evaluate.
Scope the review
We identify the AI systems, sensitive-data flows, entities, and review requirements that belong in scope before the assessment starts.
Assess the evidence
A named assessor evaluates the in-scope AI environment against HIPAA Security Rule safeguards and NIST AI RMF, documenting findings and remediation priorities.
Sign the evidence package
You receive an evidence-backed assessment package with a dated assessor sign-off that you can provide during healthcare security, vendor-risk, and procurement review.
Fixed-scope engagements can support a healthcare vendor or business associate preparing for external diligence, as well as a covered entity documenting AI risk internally.
Engagements start at $6,500 (founding rate), priced up front — see full pricing.
Transparent, fixed-scope engagements
Focused and Comprehensive engagements have published starting prices. We scope the actual AI surface, entities, and review requirements before confirming the engagement.
Reproducible scoring
The score is derived from documented assessment evidence and a published methodology rather than an unexplained proprietary number.
Open methodology
Findings map to established frameworks — NIST AI RMF, ISO/IEC 42001, HIPAA Security Rule — using a published scoring methodology customers and reviewers can inspect directly.
Named-assessor delivery
A named assessor performs the engagement, documents limitations and findings, and signs the final assessment package.
Final scope is confirmed on a 20-minute scoping call.
Related Reading
Let's Assess Your Healthcare AI Security Posture
Book a free 30-minute scoping call for a guided assessment of your AI security posture.