HITRUST AI Security Assessment and Certification Specification

ID and evaluate compliance & legal obligations for AI system development and deployment

HITRUST CSF requirement statement [?] (19.06aAISecOrganizational.1)

The organization 
(1) performs an assessment to identify and evaluate its compliance with applicable legal 
and regulatory requirements addressing the development and deployment of AI systems, 
including potential liability for harmful, infringing, or damaging outputs or behaviors. 
This assessment is performed 
(2) prior to deployment of the AI system and
(3) regularly (at least annually) thereafter.

Evaluative elements in this requirement statement [?]
1. The organization performs an assessment to identify and evaluate its compliance 
with applicable legal and regulatory requirements addressing the development and 
deployment of AI systems, including potential liability for harmful, infringing, or damaging 
outputs or behaviors.
2. This assessment is performed prior to the deployment of the AI system.
3. This assessment is performed regularly (at least annually).


Illustrative procedures for use during assessments [?]

  • Policy: Examine policies related to each evaluative element within the requirement statement. Validate the existence of a written or undocumented policy as defined in the HITRUST scoring rubric.

  • Procedure: Examine evidence that written or undocumented procedures exist as defined in the HITRUST scoring rubric. Determine if the procedures and address the operational aspects of how to perform each evaluative element within the requirement statement.

  • Implemented: Examine evidence that all evaluative elements within the requirement statement have been implemented as defined in the HITRUST scoring rubric, using a sample based test where possible for each evaluative element. Example test(s):
    • For example, review the organizations assessment documentation to evaluate its compliance with applicable legal and regulatory requirements addressing the development and deployment of AI systems, including potential liability for harmful, infringing, or damaging outputs or behaviors. Further, confirm that the assessment was performed prior to the deployment of the AI system (if within the past year) and that one is performed regularly (at least annually).

  • Measured: Examine measurements that formally evaluate and communicate the operation and/or performance of each evaluative element within the requirement statement. Determine the percentage of evaluative elements addressed by the organization’s operational and/or independent measure(s) or metric(s) as defined in the HITRUST scoring rubric. Determine if the measurements include independent and/or operational measure(s) or metric(s) as defined in the HITRUST scoring rubric. Example test(s):
    • For example, measures indicate completeness of the organization’s assessment to identify and evaluate its compliance with applicable legal and regulatory requirements addressing the development and deployment of AI systems. Reviews, tests, or audits are completed by the organization to measure the effectiveness of the implemented controls and to confirm that an assessment is performed regularly (at least annually).

  • Managed: Examine evidence that a written or undocumented risk treatment process exists, as defined in the HITRUST scoring rubric. Determine the frequency that the risk treatment process was applied to issues identified for each evaluative element within the requirement statement.

Placement of this requirement in the HITRUST CSF [?]

  • Assessment domain: 19 Data Protection & Privacy
  • Control category: 06 Compliance
  • Control reference: 06.a – Identification of Applicable Legislation

Specific to which parts of the overall AI system? [?]
N/A, not AI component-specific

Discussed in which authoritative AI security sources? [?]
  • LLM AI Cybersecurity & Governance Checklist
    Feb. 2024, © The OWASP Foundation
    • Where:
      • 3. Checklist > 3.7. Legal > Bullet #7
      • 3. Checklist > 3.7. Legal > Bullet #8
      • 3. Checklist > 3.8. Regulatory > Bullet #1
      • 3. Checklist > 3.8. Regulatory > Bullet #10

  • Generative AI framework for HM Government
    2023, Central Digital and Data Office, UK Government
    • Where:
      • Using generative AI safely and responsibly > Ethics > Accountability and responsibility > Practical recommendations > Bullet 1
      • Using generative AI safely and responsibly > Legal considerations > Paragraph 1

  • Securing Machine Learning Algorithms
    2021, © European Union Agency for Cybersecurity (ENISA)
    • Where:
      • 4.1- Security Controls > Organizational > Assess the regulations and laws the ML application must comply with

Discussed in which commercial AI security sources? [?]
  • Databricks AI Security Framework
    Sept. 2024, © Databricks
    • Where:
      • DASF 12: Delete records from datasets and retrain models to forget data subjects
      • DASF 29: Build MLOps workflows to track models and trace data sources and lineage to retrain models with the updated dataset by following legal constraints
      • DASF 27: Pretrain a large language model (LLM) to only use the data that is allowed with LLMs for inference

Control functions against which AI security threats? [?]
Additional information
  • Q: When will this requirement included in an assessment? [?]
    • This requirement will always be added to HITRUST assessments which include the
      Security for AI systems regulatory factor.
    • No other assessment tailoring factors affect this requirement.

  • Q: Will this requirement be externally inheritable? [?] [?]
    • No (dual responsibility). The AI application provider and its AI service providers (if used) are responsible for independently performing this requirement outside of the AI system’s technology stack.