AI Security Risk Assessment Checklist: Protect PHI and Meet HIPAA Standards

Breach Notification and Compliance Audit

Establish a breach notification program that anticipates AI-specific risks to PHI, from prompt injection to data leakage. Your plan should define decision paths, roles, and evidence collection so you can act decisively and demonstrate HIPAA compliance.

Breach notification workflow

• Detect and triage: use alerts on anomalous model behavior, data exfiltration, or unauthorized PHI access; immediately contain affected systems and revoke tokens.
• Investigate: preserve forensic images and audit logs, identify compromised data elements, and determine whether ePHI was accessed, acquired, or disclosed.
• Risk assessment: evaluate likelihood of misuse and potential harm; document rationale behind the determination that an incident is or is not a reportable breach.
• Notification: prepare notices to affected individuals and required authorities without unreasonable delay, aligned with HIPAA Breach Notification Rule timelines.
• Remediation: close root causes, rotate keys, strengthen access controls, and verify fixes through retesting and monitoring.
• Post-incident review: update playbooks and training; track corrective and preventive actions to completion.

Compliance audit cadence and scope

• Define an annual and event-driven audit schedule that covers administrative, physical, and technical safeguards relevant to AI systems processing PHI.
• Map controls to HIPAA requirements and your internal policies; test sampling from data ingestion to model inference and output filtering.
• Validate vendor obligations in Business Associate Agreements (BAAs) and review evidence, including security reports and incident metrics.
• Document findings, assign owners, set due dates, and track remediation until closure; retain evidence for future audits.

Security Risk Assessment Procedures

Conduct a formal risk analysis as required by HIPAA Security Rule 164.308(a)(1)(ii)(A). Center the assessment on how PHI flows through your AI lifecycle and where threats could compromise confidentiality, integrity, or availability.

Scope and inventory

• Build a current inventory of AI assets: datasets, feature stores, models, agents, prompts, vector databases, APIs, and endpoints touching ePHI.
• Diagram data flows for ingestion, training, fine-tuning, inference, logging, and archival; identify trust boundaries and external dependencies.

ePHI vulnerability analysis and threat modeling

• Assess threats unique to AI: prompt injection, data leakage via outputs, model inversion, membership inference, and training data poisoning.
• Evaluate pipeline exposures: misconfigured storage, secret sprawl, inadequate network segmentation, and insecure third-party integrations.

Risk scoring and prioritization

• Score risks by likelihood and impact on PHI; prioritize high-risk items that enable privilege escalation, bypass auditing, or expose large data sets.
• Define risk acceptance thresholds and escalation paths to leadership and compliance.

Remediation planning and validation

• Create action plans with control owners, milestones, and success metrics; address gaps with encryption, RBAC, MFA, network controls, and logging.
• Validate fixes through penetration tests, adversarial evaluations, tabletop exercises, and continuous monitoring.

Governance and cadence

• Establish RACI for risk analysis, sign-off, and evidence retention; repeat assessments at least annually and after major system changes.

Access Control and Authentication Measures

Apply least privilege and zero-trust principles across identities—human and service. Strong access control prevents lateral movement and reduces PHI exposure.

Role-based access control (RBAC)

• Define roles for developers, data scientists, analysts, and operations; grant only the permissions necessary for each function.
• Separate duties for model training, deployment, and key management; enforce just-in-time, time-bound elevation for rare “break-glass” access.
• Harden service accounts: use scoped tokens, short-lived credentials, and secret rotation with automated revocation.

Multi-factor authentication (MFA)

• Require MFA for all privileged users and any access path to PHI repositories, admin consoles, and model management platforms.
• Prefer phishing-resistant factors (for example, FIDO2 security keys) and conditional access based on device health and location.

Session and identity management

• Centralize identities via SSO; enforce session timeouts, IP restrictions, and device posture checks for tools that handle PHI.
• Run periodic access recertifications; alert on anomalous access patterns and excessive privilege accumulation.

Data Encryption and Protection Strategies

Protect PHI at every stage—collection, processing, transmission, storage, and deletion. Align cryptographic choices with your threat model and platform capabilities.

Encrypted PHI transmission

• Enforce TLS 1.2+ end to end, including mutual TLS for service-to-service calls; restrict egress and use private connectivity where available.
• Use message-level encryption for queues and event buses; apply email encryption for any permitted PHI notifications.

Data at rest and key management

• Encrypt at rest with strong ciphers (for example, AES-256) and customer-managed keys; implement envelope encryption and scheduled key rotation.
• Store secrets in a hardened vault; tokenize or pseudonymize identifiers and minimize PHI retention to the shortest operational need.

Model and pipeline safeguards

• Isolate training environments; avoid training on raw PHI unless strictly necessary with formal approvals and compensating controls.
• Deploy input/output filters to block sensitive data exfiltration; consider differential privacy or redaction for datasets and embeddings.
• Continuously test models for leakage and unintended memorization; verify that logging excludes PHI or uses safe redaction.

Documentation and Record Retention Policies

Strong documentation proves due diligence and accelerates audits. Maintain clear, current records that show how your AI systems meet HIPAA standards.

Policy library and version control

• Publish policies and procedures governing AI use of PHI; assign owners, review cycles, and approval workflows with tracked revisions.

Audit log maintenance

• Collect tamper-evident logs for access, administrative actions, data flows, and model events; synchronize time sources for reliable forensics.
• Centralize logs in a monitored SIEM; restrict log access, and document retention and disposal steps.

Retention schedules

• Retain required HIPAA documentation for at least six years from creation or last effective date, and define longer periods where laws or contracts apply.
• Apply defensible deletion for expired records and backups, ensuring PHI is irretrievable after disposal.

Employee Training and Awareness Programs

Your workforce is a critical control. Equip people to handle PHI safely and to recognize AI-specific threats that traditional training may miss.

Core curriculum

• Teach PHI classification, minimum necessary use, secure prompt practices, and approved tools for AI-assisted workflows.
• Cover incident reporting, data labeling hygiene, and common attack patterns like prompt injection and data poisoning.

Role-based training

• For engineers and data scientists: secure coding, dataset curation, de-identification, secret management, and supply-chain risk controls.
• For analysts and support staff: proper redaction, safe sharing, and handling of outputs that may contain sensitive context.

Ongoing awareness

• Run periodic phishing and social engineering drills; deliver micro-learnings on new AI risks and policy changes.
• Measure program effectiveness with completion rates, assessment scores, and reductions in repeat violations.

Business Associate Agreements and Vendor Evaluation

Most AI ecosystems rely on vendors. Ensure every vendor that handles PHI signs appropriate Business Associate Agreements (BAAs) and meets your security bar.

Vendor due diligence

• Use structured questionnaires and evidence reviews covering architecture, encryption, RBAC, MFA, data residency, and incident response SLAs.
• Assess audit reports and testing attestations; verify subprocessor disclosures and change notification practices.

BAA essentials

• Define permitted uses and disclosures, required safeguards, breach notification duties, subcontractor flow-down, and right to audit.
• Specify data return or destruction, termination rights, and liability provisions appropriate to PHI risk.

Continuous monitoring

• Track vendor performance, issues, and remediation; reassess on major changes, incidents, or new AI features that touch PHI.

Summary and next steps

Implement this AI security risk assessment checklist to control PHI across your AI lifecycle. Prioritize a defensible risk analysis, strong access controls, encrypted PHI transmission, audit log maintenance, and enforceable BAAs—then test and improve continuously.

FAQs.

What are the key components of an AI security risk assessment?

Core components include asset inventory and data flow mapping, ePHI vulnerability analysis and threat modeling, risk scoring and remediation plans, access controls such as role-based access control (RBAC) and multi-factor authentication (MFA), encrypted PHI transmission and robust at-rest protection, audit log maintenance, incident response with breach notification, and governance that aligns owners, cadence, and evidence.

How does HIPAA regulate AI systems handling PHI?

HIPAA applies through covered entities and business associates, requiring administrative, physical, and technical safeguards across AI workflows. You must perform a risk analysis under HIPAA Security Rule 164.308(a)(1)(ii)(A), implement minimum necessary access, secure transmission and storage of PHI, maintain documentation, and ensure vendors sign and honor Business Associate Agreements (BAAs). Breach notification and audit readiness are mandatory components.

What procedures should be in place for HIPAA breach notification?

Prepare a documented workflow to detect, contain, and investigate incidents; conduct a risk assessment to determine if a breach occurred; and, if so, notify affected individuals and required authorities without unreasonable delay according to HIPAA timelines. Preserve evidence, coordinate with vendors under BAAs, maintain detailed records of actions taken, and update controls and training based on lessons learned.