NIST AI Risk Management Framework (AI RMF)

NIST AI Risk Management Framework (AI RMF) | CDA.Wiki | CDA.Wiki

# NIST AI Risk Management Framework (AI RMF)

Definition

The NIST AI Risk Management Framework (AI RMF 1.0), published in January 2023, is a voluntary framework designed to help organizations manage risks associated with artificial intelligence systems throughout their lifecycle. It provides a structured approach to identifying, assessing, and mitigating AI-specific risks including bias, lack of transparency, security vulnerabilities, privacy concerns, and unintended consequences. The framework complements existing risk management standards (like NIST CSF and ISO 31000) by addressing the unique characteristics of AI systems.

How It Works

The AI RMF is organized around two main components:

Part 1: Foundational Information describes the characteristics of trustworthy AI:

Valid and Reliable: AI systems perform as intended under expected and unexpected conditions
Safe: Systems do not endanger human life, health, property, or the environment
Secure and Resilient: Systems resist attacks, recover from failures, and maintain integrity
Accountable and Transparent: Decision-making processes are understandable and explainable
Explainable and Interpretable: Outputs can be understood by stakeholders
Privacy-Enhanced: Systems protect individual privacy throughout the data lifecycle
Fair with Harmful Bias Managed: Systems do not discriminate or produce inequitable outcomes

Part 2: Core Framework defines four functions:

GOVERN: Establish organizational structures, policies, and processes for AI risk management. This is the cross-cutting function that informs all others. Includes culture, accountability structures, risk tolerance definition, and third-party AI governance.

MAP: Contextualize AI systems within their operational environment. Identify intended purposes, known limitations, stakeholders, potential impacts, and interdependencies. Mapping occurs before deployment and is continuously updated.

MEASURE: Assess and quantify AI risks using appropriate metrics, methods, and benchmarks. Includes testing for bias, robustness, accuracy, security, and privacy. Measurement must occur across the entire AI lifecycle, not just at deployment.

MANAGE: Prioritize and act on identified risks. Allocate resources for risk treatment, implement controls, monitor effectiveness, and communicate residual risks to stakeholders.

Each function includes categories and subcategories with suggested actions and outcomes.

The companion AI RMF Playbook provides practical implementation guidance for each subcategory, including suggested actions, documentation requirements, and assessment criteria.

Why It Matters

AI regulation is accelerating globally. The EU AI Act is enforceable, China has AI governance requirements, and US federal agencies face executive orders on AI safety. Organizations need a structured approach to AI risk management that is flexible enough to apply across different regulatory environments.

The NIST AI RMF fills this role. As a voluntary, consensus-based framework from a trusted standards body, it provides a common language and methodology for AI risk management without prescribing specific technical solutions. Organizations that adopt it can demonstrate due diligence to regulators, customers, and board members.

The framework is particularly valuable because AI risks are fundamentally different from traditional IT risks. AI systems can fail in ways that are difficult to predict, detect, or explain. They can produce biased outcomes that cause real harm. They can be manipulated through adversarial inputs. Traditional risk frameworks do not address these characteristics.

Real-World Applications

Federal Agencies: US government agencies use the AI RMF to comply with Executive Order 14110 on Safe, Secure, and Trustworthy AI.
Financial Services: Banks apply the framework to algorithmic lending, fraud detection, and trading systems to manage fairness, accuracy, and transparency risks.
Healthcare: Health systems use it to assess AI diagnostic tools for bias, reliability, and patient safety implications.
Technology Companies: AI product teams use the framework to structure red-teaming, bias testing, and safety evaluations before deployment.
Defense Contractors: Apply the framework alongside DoD's Responsible AI principles for military AI systems.

CDA Perspective

The NIST AI RMF integrates into CDA's Risk Governance & Assurance (RGA) domain under the Perpetual Compliance Assurance (PCA) methodology. As organizations deploy AI across their operations, AI risk management becomes inseparable from overall cybersecurity risk management.

CDA's operational approach:

M-RGA-R01 assesses the organization's AI inventory, existing governance structures, and regulatory obligations during reconnaissance
M-RGA-B01 designs the AI governance framework, mapping GOVERN, MAP, MEASURE, and MANAGE functions to organizational roles and processes
M-RGA-H01 implements continuous AI risk monitoring, including bias testing, adversarial robustness assessment, and model performance tracking
M-RGA-D01 conducts AI-specific tabletop exercises to test governance and incident response for AI failures

CDA's position: AI governance is not a separate discipline from cybersecurity governance. It is an extension of it. Organizations that treat AI risk as a standalone problem will fail to connect AI security incidents to their broader risk posture.

Key Takeaways

NIST AI RMF 1.0 is a voluntary framework for managing AI-specific risks across the lifecycle
Four core functions: GOVERN, MAP, MEASURE, MANAGE (with GOVERN as the cross-cutting foundation)
Seven characteristics of trustworthy AI provide the assessment framework
Complements existing standards (NIST CSF, ISO 31000) with AI-specific guidance
Critical for demonstrating due diligence as AI regulation accelerates globally
AI risk management must integrate with, not operate separately from, cybersecurity risk management

Table of Contents

Definition

How It Works

Why It Matters

Real-World Applications

CDA Perspective

Key Takeaways

Related CDA Missions

Discussion

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