INDEPENDENT AI RISK ASSESSMENT
HIPAA AI Risk Assessment
A fixed-scope, independent assessment of how your AI handles patient data — every endpoint mapped, scored against HIPAA and NIST, and delivered as a signed report a named assessor stands behind for auditors, cyber-insurers, and hospital partners.
AI Adoption Creates Security Gaps That Traditional Assessments Miss
Traditional security assessments evaluate networks, endpoints, and applications. But they don't account for the unique risks introduced by AI adoption — shadow AI tools transmitting sensitive data to unknown third parties, employees pasting customer PII into generative AI prompts, API integrations with no access controls, and AI-generated outputs being used in regulated contexts without human review.
Our AI Security Assessment is purpose-built to evaluate these risks. Every engagement is scored against the Ayliea AI Security Standard (AISS) — a public, CC-BY-4.0 licensed standard of 10 AI-specific control domains with crosswalks to NIST AI RMF, NIST CSF, CIS Controls v8.1, ISO/IEC 27001, OWASP LLM Top 10 2025, MITRE ATLAS, and EU AI Act Article 50. See our full methodology for details on how each domain is assessed and scored.
The result is a clear picture of your AI risk exposure with a prioritized, actionable plan to address it.
COMPREHENSIVE ASSESSMENT
AI Endpoint Security Testing
A hands-on technical evaluation of the interfaces where AI tools connect to your organization.
Endpoint Discovery & Mapping
We identify every AI-related API endpoint, integration, and data flow across your environment — sanctioned tools, shadow AI, and embedded AI features within existing SaaS platforms.
Authentication & Authorization
We verify that AI API keys and tokens are properly scoped, rotated, and stored — checking for over-privileged access, shared credentials, keys exposed in client-side code, and missing revocation procedures.
Data Leakage Analysis
We analyze what data is transmitted to AI endpoints — whether PHI, ePHI, or other sensitive data (PII, credentials, proprietary information) is being sent in prompts, fine-tuning data, or API payloads, and whether disclosures satisfy the HIPAA minimum-necessary standard at the model layer.
Input Validation & Injection
For internally hosted or custom AI endpoints, we test for prompt injection, jailbreak vectors, and malformed input handling — verifying that user-facing AI interfaces enforce appropriate guardrails.
Transport & Configuration Security
We verify that all AI endpoints enforce TLS, validate certificates, and that no API traffic traverses unencrypted channels. We also check for overly permissive CORS policies and missing rate limiting.
Abuse & Cost Controls
We evaluate whether AI API integrations have rate limits, spending caps, and usage monitoring in place — preventing runaway costs from misuse, compromised keys, or automated abuse.
10 DOMAINS
AI Security Control Domains
Every assessment evaluates your organization across these domains.
AI Governance & Policy
Organizational policies, roles, and accountability structures for AI adoption and oversight.
AI Asset Management
Discovery, inventory, and classification of all AI tools and services in use across the organization.
Data Protection in AI
Controls for data flowing to, from, and within AI systems — including DLP, classification, and retention.
Access Control for AI
Authentication, authorization, and least-privilege access to AI tools and the data they process.
AI Supply Chain Security
Vendor risk assessment, third-party AI service evaluation, and supply chain integrity verification.
AI Output Validation
Controls ensuring AI-generated outputs are reviewed, accurate, and appropriate before use in decisions.
AI Incident Response
Procedures for detecting, responding to, and recovering from AI-related security incidents.
AI Monitoring & Logging
Visibility into AI system usage, data flows, anomalies, and audit trail maintenance.
AI Training & Awareness
Employee education on safe AI usage, acceptable use policies, and organizational AI guidelines.
Model Security
Protection of AI models from adversarial attacks, prompt injection, data poisoning, and model theft.
DELIVERABLES
Your Deliverable Package
Executive Summary Report
C-Suite and board-ready overview of findings and risk posture.
Technical Assessment Report
Detailed findings with evidence, analysis, and remediation steps.
AI Asset Inventory
Complete catalog of discovered AI tools with classifications and risk scores.
Compliance Gap Matrix
Regulation-by-regulation gap analysis with severity ratings.
Remediation Roadmap
Phased action plan with priorities, timelines, effort estimates, and ownership.
Risk Register
Trackable finding list with scoring, status, and acceptance/remediation decisions.
WHY NOW
The forcing functions are already here
You don't need a new regulation to act. The requirements that gate healthcare AI are already in force — here's what's driving assessments today.
The HIPAA Security Rule already requires it
A risk analysis covering the systems that touch ePHI is a standing HIPAA Security Rule requirement — and an incomplete or missing risk analysis is among the most frequently cited findings in OCR enforcement. AI tools that process patient data fall squarely inside that obligation.
Cyber-insurance is starting to ask
Cyber-insurance applications and AI riders increasingly condition coverage on an AI inventory and documented controls. A reproducible assessment gives you the inventory and evidence to answer those questions — and to keep your coverage.
Hospitals vet vendors against the HSCC guide
Health-sector buyers increasingly assess their AI vendors against the HSCC's AI third-party risk guidance. If you sell into hospitals, a defensible AI security assessment is becoming part of the procurement conversation — not a nice-to-have.
NIST AI RMF is the recognized safe-harbor framework for AI risk. Ayliea's assessments align to NIST AI RMF alongside HIPAA, NIST CSF 2.0, and ISO 27001 — building the documented, reasonable-care evidence base that auditors, insurers, and hospital partners expect.
Average data breach cost
$4.44M
The average total cost of a data breach globally in 2025 — and breaches involving shadow IT cost 35% more due to delayed detection and response.
IBM Cost of a Data Breach Report, 2025
Shadow AI exposure
75%
of employees report using AI tools at work. In most mid-market organizations, IT has visibility into fewer than half of these tools.
McKinsey Global Survey on AI, 2024
Regulatory penalty ceiling
€15M
Maximum penalty under the EU AI Act for high-risk AI system violations — 3% of global annual turnover (EU AI Act Art. 99(4)). Prohibited AI practices carry up to €35M / 7% (Art. 99(3)). The Colorado AI Act adds per-violation fines starting in 2026.
EU AI Act, Article 99
Mean time to identify a breach
194 days
Organizations without AI security governance take an average of 194 days to identify a breach — nearly 7 months of undetected exposure.
IBM Cost of a Data Breach Report, 2025
You can't govern the AI you can't see
In most organizations, IT has visibility into fewer than half the AI tools employees actually use — each one an ungoverned path that patient or proprietary data can flow down. An assessment surfaces them, maps where the data goes, and turns shadow AI into a governed, documented inventory.
The math
200
employees in a typical mid-market org
60+
actively using AI tools weekly (at 30%)
An Ayliea assessment costs a fraction of a single breach — and covers risks that traditional assessments don't even evaluate. See our published pricing floors.
AFTER YOUR ASSESSMENT
Keep Your Posture Current Between Engagements
Your signed deliverables are owned by your organization — exportable and yours to keep; portal access is provided with the engagement and continues with a Monitoring Retainer.
Let's Scope Your Assessment
Every engagement starts with a free 30-minute scoping call.