AI Risk and Compliance Assessments for Healthcare

A practical guide to the people, data, controls, and evidence a healthcare organization should review before deploying or expanding AI.

Maintain an inventory of every approved, planned, and employee-led AI use case.
Map sensitive data flows before connecting AI tools to healthcare systems.
Match oversight, testing, and monitoring requirements to the risk of each use case.
Verify vendor commitments, configurations, and contracts instead of relying on marketing claims.
Reassess AI tools whenever workflows, data connections, or vendor capabilities change.

Artificial intelligence can help healthcare organizations reduce administrative work, organize information, improve service experiences, and support staff decisions. It can also introduce risks that are easy to miss when a tool is adopted quickly or used outside its original purpose.

An AI risk and compliance assessment gives leadership a structured way to decide whether a proposed AI use is appropriate, what safeguards it needs, and who is accountable for operating it. For healthcare organizations, the assessment should go beyond a basic vendor questionnaire. It should examine how the AI handles sensitive information, affects patients and staff, fits into clinical or business workflows, and can be monitored over time.

The goal is not to block every AI initiative. It is to make informed decisions, document reasonable safeguards, and avoid placing employees in the position of relying on tools they do not understand or control.

Start with an inventory of AI use cases

You cannot assess what you have not identified. Begin by documenting both planned and existing AI use. This should include tools formally purchased by the organization, features embedded in existing software, and employee-led use of public AI tools.

For each use case, record:

The business or clinical problem the tool is intended to address
The departments, users, patients, or customers affected
Whether the tool generates, summarizes, recommends, predicts, classifies, or makes decisions
Whether a person reviews and approves its output before action is taken
The systems and data sources connected to the tool
The vendor, product version, contract owner, and support contacts
Whether the use case is in pilot, production, or being retired

This inventory often reveals shadow AI use, such as staff pasting notes into public chat tools or using unapproved transcription features. Those situations should be addressed constructively. Staff need a clear, usable path to approved tools and guidance, not simply a prohibition that is difficult to follow in practice.

Define the purpose, boundaries, and risk level

Every AI use case needs a documented purpose and clear operating boundaries. A tool that drafts a patient appointment reminder presents a different risk profile than one that summarizes clinical documentation or recommends care-related actions.

The assessment should identify what the AI is allowed to do and what it must not do. Consider questions such as:

Is the output informational, operational, clinical, financial, or patient-facing?
Could an inaccurate output delay care, expose sensitive information, create billing errors, or affect a patient decision?
Does the tool influence a diagnosis, treatment decision, prioritization, eligibility determination, or communication with a patient?
What decisions must remain with qualified staff?
What conditions require the tool to be paused or removed from service?

Organizations often benefit from a simple risk-tiering model. Lower-risk uses may include internal drafting with no sensitive data. Higher-risk uses typically include patient data, clinical workflows, automated decisions, or outputs that could materially affect care, privacy, or financial outcomes. Higher-risk use cases should receive deeper review and more frequent monitoring.

Map data flows and sensitive information

Healthcare AI assessments must clearly map the data entering, moving through, and leaving the AI system. This is especially important when a tool is cloud-hosted, relies on an external model provider, or connects to electronic health records, messaging, document repositories, or identity platforms.

Document:

The types of data used, including protected health information, personally identifiable information, financial information, and employee data
Where the data originates and where it is stored, processed, backed up, and deleted
Whether prompts, uploads, outputs, or usage logs are retained by the vendor
Whether the vendor uses customer data to train or improve its models
Which users and systems can access the data
How data is encrypted in transit and at rest
How long information is retained and how deletion requests are handled

If protected health information may be involved, the organization should confirm that contractual and operational safeguards are appropriate for the intended use. Healthcare organizations should also verify whether the vendor’s stated configurations, privacy settings, and contractual commitments match how the tool is actually deployed.

Review vendor, contractual, and third-party risk

AI vendors may rely on subcontractors, cloud providers, model providers, data labeling services, and analytics platforms. A healthcare organization needs visibility into that chain, especially where sensitive information may be processed.

A vendor review should address:

Security controls, independent assurance reports when available, and incident response practices
Data ownership, permitted uses, retention, deletion, and portability terms
Subprocessors and locations where information may be processed
Access controls, logging, encryption, vulnerability management, and secure development practices
The vendor’s approach to model updates, feature changes, and notifications
Support for audit requests, investigations, and evidence collection
Contract terms for breach notification, cooperation, liability, and termination

Do not assume that a vendor’s claim of being “compliant” answers every question. Compliance responsibilities are shared. Your organization remains responsible for configuring access appropriately, training users, monitoring usage, and ensuring the tool is used within approved boundaries.

Evaluate security and identity controls

An AI system should be assessed like any other system that handles sensitive data, but its integration points deserve particular attention. Weak identity controls, overly broad permissions, or unsecured application programming interfaces can expose large amounts of data quickly.

Review whether the implementation includes:

Single sign-on and multi-factor authentication where available
Role-based access that limits users to the data and functions they need
Separate administrative accounts and controlled privileged access
Audit logs for user activity, data access, configuration changes, and exports
Secure integration design, including managed credentials and restricted API access
Device, browser, and network protections appropriate to the environment
A tested process to remove access when employees change roles or leave

Access controls should apply not only to the AI interface but also to connected data sources. An AI assistant should not become a convenient way for a user to retrieve records they would otherwise be unable to access.

Test output quality, safety, and human oversight

AI can produce plausible but incorrect information. In healthcare, an assessment should evaluate how errors could affect patients, clinical staff, operations, and trust.

Before deployment, test the tool using representative scenarios, including ambiguous inputs, incomplete records, unusual cases, and attempts to prompt the system outside its intended function. Evaluate whether outputs are accurate enough for the stated use, clearly labeled when uncertain, and easy for staff to review.

The assessment should define:

Who is responsible for reviewing and approving outputs
When staff must verify source information rather than rely on an AI summary
Escalation steps for suspected harmful, inaccurate, biased, or unsafe output
Whether users can correct errors and how corrections are captured
Training requirements for staff who use or supervise the tool
Patient communication expectations when AI is involved in a workflow

For clinical or care-adjacent uses, qualified human oversight should be explicit and practical. A policy that requires review is not enough if users lack the time, authority, source data, or training needed to perform that review.

Assess fairness, bias, and population impact

Healthcare organizations serve diverse patient populations. AI outputs can be affected by incomplete data, historical inequities, language differences, or assumptions embedded in the model and its training data.

Assess whether the use case could create uneven outcomes across patient populations, locations, languages, disability status, or other relevant groups. Ask whether the tool has been evaluated for the population it will serve and whether its recommendations could reinforce existing gaps in access or care.

This review should be proportionate to the use case. A scheduling draft tool may require a lighter review than a system that prioritizes patients, predicts risk, or influences resource allocation. In all cases, staff should have a clear way to report concerning patterns and override inappropriate outputs.

Establish governance, policies, and evidence

An assessment has limited value if nobody owns the resulting controls. Assign a business owner, technical owner, privacy or compliance reviewer, and security contact for each approved AI use case. For higher-risk uses, include clinical leadership and legal counsel as appropriate.

Your governance process should maintain evidence such as:

The use-case inventory and risk classification
Approval decisions, conditions, and responsible owners
Data-flow diagrams and vendor review documentation
Security configuration records and access reviews
Test results, known limitations, and user training materials
Incident reports, corrective actions, and periodic review outcomes

Create policies employees can follow. At a minimum, address approved and prohibited AI use, data handling, required review of AI output, reporting channels, and consequences of bypassing safeguards.

Monitor continuously and reassess changes

AI risk is not static. Vendors update models, add integrations, change terms, and modify retention settings. Your own workflows, data sources, and staff roles also change.

Set a review schedule based on the risk level of each use case. Reassess sooner when there is a significant vendor update, new data connection, security incident, material workflow change, concerning output pattern, or change in applicable requirements.

A well-run AI assessment program gives healthcare leaders a repeatable decision process. It helps the organization use promising technology while maintaining the privacy, security, accountability, and patient trust that healthcare depends on.