How to Monitor AI Access to Electronic Health Records

Healthcare organizations increasingly use AI assistants and agents to summarize clinical notes, retrieve patient information, and automate administrative workflows. When these systems connect to electronic health records (EHRs), security teams need visibility into what they can access, which identities they use, and how patient data moves between connected systems.

Traditional EHR security relies on authentication, access controls, and audit logs. AI introduces additional access paths through service accounts, APIs, automated agents, and third-party tools. An agent may access patient information through an integration rather than a conventional user interface.

Monitoring AI access to electronic health records requires visibility into AI assets, identities, permissions, data flows, and connected systems. Grafyn's AI Security Platform helps security teams discover AI assets, understand their connections, identify exposure paths, and assess potential security risks.

Why Is AI Access Monitoring Important for Healthcare?

EHRs contain sensitive patient information, including medical histories, diagnoses, prescriptions, laboratory results, and clinical notes. Unauthorized access can create privacy, security, and operational risks.

Consider a healthcare organization using AI agents to process prior authorizations and insurance claims. If these agents have broad access to electronic health record (EHR) systems, they may retrieve patient records beyond what is necessary for a specific case. Security teams need to understand which records each agent can access and whether those permissions align with its assigned task. Similarly, unapproved AI scribes may process patient notes outside authorized workflows, while inadequate separation of patient contexts could cause one patient's information to appear in another patient's AI-generated response.

The HIPAA Security Rule includes access-control, authentication, and audit-control requirements for systems that maintain or use electronic protected health information (ePHI). Healthcare organizations should evaluate their AI deployments within their broader security risk management program. See the HHS HIPAA Security Rule guidance.

1. Discover AI Systems That Can Access Patient Data

Healthcare organizations may use AI agents for prior authorizations and claims processing, AI scribes for clinical documentation, third-party language models, research tools, and agents connected to EHR platforms through APIs. Some tools may be introduced by individual clinicians or administrative staff without formal security review, leaving gaps in visibility into how patient information is accessed and processed.

Start by identifying:

• AI applications and agents connected to EHR platforms, including prior-authorization and claims-processing agents.
• Clinical databases and document repositories accessible to AI systems.
• Third-party AI services and unapproved AI scribes that may process patient notes.
• APIs, tools, and service accounts used for automated patient-record access.
• Unapproved AI applications that may receive sensitive patient information.

Grafyn's Discovery & Visibility capabilities help identify AI assets across cloud, SaaS, code, third-party LLMs, and Shadow AI while mapping connections to data, identities, and systems.

This visibility helps healthcare security teams identify AI deployments that may access EHR data, including unapproved tools and agents whose access requires closer review.

2. Review AI Agent Identities and Permissions

AI agents may use service accounts, API keys, access tokens, or delegated identities to connect to healthcare applications. These credentials determine which records and systems an agent can reach.

For example, an agent designed to retrieve selected patient information may not need permission to modify clinical records or access unrelated patient accounts. Excessive permissions can increase the potential impact of misuse or compromise.

Healthcare security teams should:

• Map each agent to its identity and accountable owner.
• Review service-account permissions and API access.
• Identify inherited or delegated privileges.
• Separate read-only access from permissions that allow changes.
• Review credentials and access after system changes.
• Investigate unexpected access to sensitive records.

Grafyn's Agentic Identity Blast Radius Analysis helps teams understand agent identities, permissions, tokens, API keys, connected tools, and downstream system reach. This can help identify excessive access and prioritize permissions that need review.

3. Review EHR Audit Logs and AI Activity

EHR audit logs help investigators understand how patient information is accessed. Depending on the system, logs may record the identity, time, resource accessed, and action performed.

Healthcare organizations should review relevant logs from EHR platforms, identity providers, API gateways, and integration systems.

For AI-related access, investigate:

• Which user or agent identity initiated the request.
• Which patient record or data resource was accessed.
• Which application or integration made the request.
• Whether information was viewed, created, or modified.
• Whether the request succeeded or was denied.

Where possible, correlate logs across systems. An EHR log may show that a service account retrieved a record, while an API gateway log identifies the integration that initiated the request.

Grafyn can complement existing monitoring by helping teams understand AI assets and their relationships with data, identities, and connected systems. It does not replace native EHR audit logging or existing security information and event management (SIEM) tools.

4. Identify Unusual Access and Sensitive Data Exposure

Once AI assets and access paths are known, security teams need to identify activity that may indicate excessive access, policy violations, or unintended patient-data exposure.

For example, a prior-authorization or claims-processing agent may have broad EHR permissions but only need records relevant to a specific case. If it queries unrelated patient records, the activity may indicate excessive permissions or an access-control weakness. Similarly, inadequate separation of patient contexts could allow one patient's information to surface in another patient's AI-generated response. Unapproved AI scribes may also introduce exposure risks when patient notes are processed through unauthorized services.

Potential warning signs include:

• A prior-authorization or claims agent accessing patient records outside its approved case scope.
• One patient's sensitive information appearing in another patient's AI-generated response.
• An unapproved AI scribe processing patient notes outside authorized workflows.
• Unexpected API calls or permission changes involving EHR integrations.
• Unusual increases in sensitive-data queries or transfers to unapproved services.
• Access to systems or records unrelated to an agent's assigned purpose.

Alerts should be evaluated in context because legitimate clinical and administrative workflows can also generate high volumes of requests.

Grafyn's Sensitive Data Disclosure helps teams assess sensitive-data exposure across AI tools, prompts, files, agents, and connected workflows. Its policy capabilities support ongoing AI security monitoring through built-in and custom policies applied to agents, MCP servers, and endpoints. Policies are evaluated on a schedule, with violations surfaced through the dashboard and email alerts. Examples include detecting an MCP server without authentication, an unapproved or high-risk AI agent, or a newly discovered asset that has not been reviewed. These capabilities complement EHR audit logs by helping security teams identify AI-related risks and investigate potential policy violations.

5. Understand the Potential AI Blast Radius

Monitoring an unusual request is only part of an investigation. Security teams also need to understand which systems an AI identity can reach and what could be affected if it were compromised.

For example, an agent connected to an EHR, clinical document repository, scheduling system, and messaging platform may have access across several workflows. The potential impact depends on its permissions, credentials, connected tools, and downstream relationships.

Healthcare organizations should assess:

• Which patient data stores an agent can access.
• Which identities and credentials it can use.
• Which clinical or administrative actions it can perform.
• Which downstream systems receive its outputs.
• Which trust boundaries its connections cross.

Grafyn's Blast Radius Detection & Minimization helps security teams understand potential paths of impact across AI-related data, identities, actions, and trust boundaries. This visibility can help prioritize high-risk connections and identify opportunities to reduce unnecessary access.

6. Connect Monitoring With AI Governance

AI access monitoring should be part of an ongoing healthcare security and governance program. Organizations need clear rules for approving AI systems, assigning ownership, granting permissions, and reviewing activity.

A practical program should include an inventory of AI assets, approved use cases, access policies, periodic permission reviews, risk assessments for new integrations, and incident-response procedures.

Grafyn's AI Governance capabilities support AI discovery, governance posture assessment, and oversight across models, agents, data, identities, tools, and workflows. Healthcare teams can also explore the AI-TRUST Framework for its focus on agent lineage, identity, observability, sensitivity, and trusted boundaries.

These capabilities can support AI oversight but do not independently establish HIPAA compliance. Organizations remain responsible for implementing appropriate safeguards and meeting their applicable obligations.

Practical Checklist for Healthcare Security Teams

• Discovery: Identify AI systems that interact with EHRs or ePHI.
• Identity: Map agent identities, service accounts, tokens, and API credentials.
• Permissions: Remove unnecessary access and distinguish read from write permissions.
• Monitoring: Review EHR and integration logs and investigate unusual activity.
• Data protection: Identify sensitive-data exposure and unapproved data transfers.
• Incident response: Determine affected records and systems, preserve evidence, and restrict risky access when appropriate.
• Governance: Assign owners, document approved AI use cases, and reassess risks when systems change.

Grafyn's AI Security Platform can be evaluated alongside existing healthcare security tools to improve visibility into AI assets, access relationships, sensitive-data exposure, and potential incident impact.

Conclusion

Securing AI access to EHRs requires visibility into the agents, identities, permissions, and integrations that handle patient information. Traditional audit logs remain essential, but healthcare organizations also need to understand how AI access extends across connected systems.

Grafyn supports this work through AI discovery, agent identity analysis, sensitive-data exposure assessment, blast-radius analysis, and AI governance. These capabilities help security teams identify risky access paths and prioritize remediation as AI becomes more integrated into clinical workflows.

Explore the Grafyn AI Security Platform  or schedule a demo.