Healthcare AI Governance and Risk Management Services
Build AI Innovation on a Foundation of Trust
What is AI Governance for Healthcare?
AI governance in healthcare is the set of policies, oversight bodies, and controls that decide how an organization selects, deploys, and monitors AI. It protects patient safety, PHI, and compliance while letting teams adopt AI with confidence.
Healthcare organizations are moving quickly to adopt AI to improve outcomes, reduce administrative burden, and control costs. But AI introduces new operational, security, privacy, and ethical risks that traditional governance programs were not designed to manage.
Our services meet you where you are and help you build a program that fits your organization
AI Governance Acceleration
Get a running start with proven foundations.
Starting from a blank page slows teams down. Acceleration gives you the cornerstone artifacts of a successful governance program, so your leaders can focus on decisions instead of drafting documents.
Your foundation includes:
- AI Governance Committee Charter: Defines roles and responsibilities for overseeing AI and aligns them to organizational objectives.
- AI System and Use-Case Intake Questionnaire: A template to document, evaluate, and prioritize AI use cases for review.
- AI Policy: Guiding principles, acceptable and unacceptable uses, and guidance on data protection and IP.
- AI Inventory and Intake Approach: Key attributes for intake, tracking, and risk-tiering AI systems, use cases, and models.
- AI Governance and Risk Management Framework: The approach to managing AI adoption risks and governing AI use in line with your AI strategy and business objectives.
- AI Taxonomy: Standardized definitions that build a common language and keep you connected to technology advancement.
Many healthcare leaders tell us the same thing:
“I want to build some foundational AI governance, but I don’t know where to start.”
AI Governance Implementation & Support
Turn your policy into daily practice.
A policy no one understands isn’t a policy. Implementation is hands-on program development, in which we work alongside your teams to put all ten elements of a complete governance program in place:
- Policies and Standards
- Governance Bodies
- Risk Management Framework
- System, Use Case, and Model Inventory
- Cyber, Privacy, and Data Governance
- Third-Party Risk Management
- Regulatory Compliance
- TEVV, Metrics, and Monitoring
- Technical and Operational Controls
- People Enablement and Change
We help you take five key steps: charter the committee, set your guardrails, see what you have, assign ownership, and enable your people. We also map each governance function to a named owner, so every function has an accountable role and nothing falls through the cracks.
The case for building a front door for AI governance:
“…we want to use this AI product, but we also need to ensure we have the right controls and governance on the agents that are working on that, so we won’t fall victim to unnecessary incidents later.”
Mahesh Chintakunta, CISSP, CPCU, Deputy Chief Information Security Officer, Rush University System for Health
AI Governance Program Management
Keep your program moving, and keep it working.
Governance is not a one-time project. Someone needs to orchestrate it day-to-day. Our services provide best practices and AI governance management until your organization is ready to take over. We assist with the coordination your program needs, including:
- Day-to-day orchestration, intake triage, and reporting, the work of an AI Governance Program Lead or PMO
- Inventory upkeep, so you always know which AI systems, models, and vendors are in use
- Ongoing testing and monitoring, so you know not only that a model works, but that it keeps working
- Clear reporting up to your AI Governance Committee and executive leadership, who set risk appetite and hold accountability
Closing the AI Governance Gap, Clearwater’s AI Benchmark Report 2026
“86% of healthcare organizations cannot demonstrate that their vendors’ AI controls are fully verified.”
What’s at Stake When AI Is Ungoverned
Healthcare leaders are being asked to move quickly on AI while protecting patients, data, and trust. That is a lot to carry. Knowing where the risk sits makes it easier to manage.
Without clear governance, AI can create exposure across your organization.
Patient Safety
Clinical decision-support errors, biased diagnostic models, and unmonitored model drift can directly affect the quality of care.
Privacy and PHI
Sensitive data flowing into prompts, training pipelines, and third-party models creates new HIPAA exposure paths.
Regulatory
Regulatory authorities, from HHS and the Joint Commission to the FDA and ONC, are beginning to ask what governs your AI systems.
Cybersecurity
Prompt injection, model poisoning, and data exfiltration, as well as AI extending the attack surface beyond traditional perimeters.
Whether you are developing new AI capabilities, deploying AI-enabled technologies, or simply trying to understand your organization’s AI risk exposure, a structured governance foundation is essential.
Explore How Clearwater can Accelerate your AI Governance Program
Framework Alignment
Strong AI governance gives you the structure to manage AI risk with confidence.
Clearwater’s AI governance services align with the four core functions of the NIST AI Risk Management Framework: Govern, Map, Measure, and Manage. Governance is the broader discipline. It establishes the oversight, accountability, and shared expectations that guide how your organization adopts and uses AI. NIST AI RMF provides a strong first step, but it is broad, covering cybersecurity and privacy, as well as transparency, responsible use, safety, and bias.
To go deeper on AI security, pair it with security-specific guidance. Options include guidance from the Joint Commission and Coalition for Health AI (CHAI), guidance from the Health Sector Coordinating Council (HSCC), and NIST’s draft Cyber AI Profile. Use the guidance to define dedicated AI security controls, then enforce them with technical guardrails.
This framework-driven approach helps you adopt AI while maintaining consistent governance and risk management across the enterprise, so your leaders can move forward knowing the right guardrails are in place.
AI Governance FAQs for Healthcare Organizations
What is shadow AI, and why does it matter?
Shadow AI is any AI tool your organization uses that it hasn’t approved or cannot see. It matters because AI changes weekly, so an AI inventory has a shelf life. In the benchmark, about 30% of organizations named unapproved employee AI as their greatest risk, and about 42% named sensitive data exposure through AI tools as a leading concern. Few have a way to validate their inventory automatically. Many still track AI in a spreadsheet built from user surveys, procurement records, and tribal knowledge.
Why is third-party AI such a concern?
You inherit it, whether you chose it or not. Software and hardware providers, including medical device makers, are adding AI features to the products you already use. Many respondents named third-party AI a top concern, but few can fully validate vendor AI controls. Questionnaires and point-in-time attestations cannot answer the questions that matter: Will our data be used for training? Can we opt out? How is it secured? Does a human oversee the AI, or can it act on its own?
We have an AI policy. Isn't that enough?
A policy is a necessary start, not proof of control. Most organizations, especially larger ones, now have policies, from acceptable use to data and access. What lags is enforcement. Where guardrails exist, they mostly sit in data protection, such as DLP. Visibility and identity controls matter too, especially when AI agents hold access privileges. The top concerns respondents named, including data leakage, ongoing visibility into employee AI use, and regulatory exposure, can only be addressed with technical controls.
Is the NIST AI Risk Management Framework enough?
It is a strong first step, but it is broad. The NIST AI RMF covers cybersecurity and privacy, and also transparency, responsible use, safety, and bias. To go deeper on AI security, pair it with security-specific guidance. Options include guidance from the Joint Commission and CHI, guidance from the Health Sector Coordinating Council (HSCC), and NIST’s draft Cyber AI Profile, released earlier this year. Use that guidance to define dedicated AI security controls, then enforce them with technical guardrails.
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