NIST AI Risk Management Framework
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  • NIST AI Risk Management Framework
  • GRN 1: Risk Management Documentation
    • GRN 1.1 - AI Legal and Regulatory Requirements
    • GRN 1.2 - Trustworthy AI Characteristics
    • GRN 1.3 - Transparent Risk Management
    • GRN 1.4 - Risk Management Monitoring
  • GRN 2: AI Organisation Structure
    • GRN 2.1 - Roles and Responsibilities
    • GRN 2.2 - AI Risk Management Training
    • GRN 2.3 - Executive Responsibility
  • GRN 3: AI Internal Stakeholders
    • GRN 3.1 - AI Risk Decisions Making
  • GRN 4: Organisational Commitments
    • GRN 4.1 - AI Risk Organisational Practices
    • GRN 4.2 - AI Organisational Documentation
    • GRN 4.3 - Organisational Information Sharing Mechnism
  • GRN 5: Stakeholder Engagement
    • GRN 5.1 - External Stakeholder Policies
    • GRN 5.2 - Stakeholder Feedback Integration
  • GRN 6: Managing 3rd-Party Risk
    • GRN 6.1 - 3rd Party Risk Policies
    • GRN 6.2 - 3rd Party Contingency
  • MAP 1: AI Application Context
    • MAP 1.1 - Intended Purpose of AI Use
    • MAP 1.2 - Inter-disciplinary AI Stakeholders
    • MAP 1.3 - AI's Business Value
    • MAP 1.4 - Organisations AI Mission
    • MAP 1.5 - Organisations Risk Tolerance
    • MAP 1.6 - Stakeholder Engagements
    • MAP 1.7 - AI System Requirements
  • MAP 2: AI Application Classification
    • MAP 2.1 - AI Classification
    • MAP 2.2 - AI Usage by Humans
    • MAP 2.3 - TEVV Documentation
  • MAP 3: AI Benefits and Costs
    • MAP 3.1 - AI System Benefits
    • MAP 3.2 - AI Potential Costs
    • MAP 3.3 - AI Application Scope
  • MAP 4: 3rd-Party Risks and Benefits
    • MAP 4.1 - Mapping 3rd-Party Risk
    • MAP 4.2 - Internal Risk Controls for 3rd Party Risk
  • MAP 5: AI Impacts
    • MAP 5.1 - AI Positive or Negative Impacts
    • MAP 5.2 - Likelihood and Magnitude of Each Impact
    • MAP 5.3 - Benefits vs Impacts
  • MRE 1: Appropriate Methods and Metrics
    • MRE 1.1 - Approaches and Metrics
    • MRE 1.2 - Metrics Appropriateness and Effectiveness
    • MRE 1.3 - Stakeholder Assessment Consultation
  • MRE 2: Trustworthy Evaluation
    • MRE 2.1 - Tools for TEVV
    • MRE 2.2 - Evaluations of Human Subjects
    • MRE 2.3 - System Performance
    • MRE 2.4 - Deployment Valid and Reliable
    • MRE 2.5 - Regular Evaluation of AI Systems
    • MRE 2.6 - Evaluation of Computational Bias
    • MRE 2.7 - Evaluation of Security and Resilience
    • MRE 2.8 - Evaluation of AI Models
    • MRE 2.9 - Evaluation of AI Privacy Risks
    • MRE 2.10 - Environmental Impact
  • MRE 3: Risk Tracking Mechanism
    • MRE 3.1 - Risk Tracking and Management
    • MRE 3.2 - Risk Tracking Assessments
  • MRE 4: Measurement Feedback
    • MRE 4.1 - Measurement Approaches for Identifying Risk
    • MRE 4.2 - Measurement Approaches for Trustworthiness
    • MRE 4.3 - Measurable Performance Improvements
  • MGE 1: Managing AI Risk
    • MGE 1.1 - Development and Deployment Decision
    • MGE 1.2 - Risk Mitigation Activities
    • MGE 1.3 - Risk Management of Mapped Risks
  • MGE 2: Managing AI Benefits and Impacts
    • MGE 2.1 - Allocated Resources for Risk Management
    • MGE 2.2 - Sustained Value Mechanism
    • MGE 2.3 - AI Deactivation Mechanism
  • MGE 3: Managing 3rd-Party Risk
    • MGE 3.1 - 3rd Party Risk are Managed
  • MGE 4: Reporting Risk Management
    • MGE 4.1 - Post-Deployment Risk Management
    • MGE 4.2 - Measurable Continuous Improvements
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  1. GRN 6: Managing 3rd-Party Risk

GRN 6.1 - 3rd Party Risk Policies

NIST AI RMF (in the playbook companion) states:

GOVERN 6.1

Policies and procedures are in place that address risks associated with third-party entities.

About

Organizations usually engage multiple third parties for external expertise, data, software packages (both open source and commercial), and software and hardware platforms across the AI lifecycle.

The need to rely on external resources or expertise may heighten existing challenges in an already complex undertaking, increasing the difficulty of risk management efforts.

Organizational approaches to managing third-party risk should be tailored to the resources, risk profile, and use case for each system. Organizations should strive to apply governance approaches to third-party AI system and data as they would for internal resources — including open source software, publicly available data, and commercially available models.

Actions
  • Collaboratively establish policies that address third-party AI systems and data.

  • Establish policies related to:

    • Transparency into third-party system functions, including knowledge about training data, training and inference algorithms, and assumptions and limitations.

    • Thorough testing of third-party AI systems.

    • Requirements for clear and complete instructions for third-party system usage.

Transparency and Documentation

Organizations can document the following:

  • Did you establish mechanisms that facilitate the AI system’s auditability (e.g. traceability of the development process, the sourcing of training data and the logging of the AI system’s processes, outcomes, positive and negative impact)?

  • If a third party created the AI, how will you ensure a level of explainability or interpretability?

  • Did you ensure that the AI system can be audited by independent third parties?

  • Did you establish a process for third parties (e.g. suppliers, end-users, subjects, distributors/vendors or workers) to report potential vulnerabilities, risks or biases in the AI system?

  • To what extent does the plan specifically address risks associated with acquisition, procurement of packaged software from vendors, cybersecurity controls, computational infrastructure, data, data science, deployment mechanics, and system failure?

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Last updated 2 years ago