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AI Governance Frameworks

NIST AI RMF and CLEAR:
complementary approaches to AI governance

NIST AI RMF provides organisations with a structured approach to managing AI risk. Yunique AI's CLEAR methodology focuses on how governance is understood and operationalised across visibility, risk alignment, enablement, accountability and responsible execution.

CLEAR does not replace NIST AI RMF. It can be used alongside established frameworks and standards to help organisations examine how governance operates in practice.

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Understanding NIST AI RMF

The NIST AI Risk Management Framework (AI RMF 1.0) was developed by the US National Institute of Standards and Technology to help organisations manage risks associated with artificial intelligence. It is intended to be applied across the design, development, deployment and use of AI systems.

According to NIST, the framework is:

  • voluntary
  • rights-preserving
  • non-sector-specific
  • use-case agnostic
  • intended to support organisations designing, developing, deploying or using AI systems
  • designed for flexible implementation according to organisational context, resources and capabilities

It is structured around four Core functions. NIST presents these as iterative and cross-cutting activities, not as a fixed sequence or checklist.

GOVERN
Cultivates and implements the organisational culture, processes, and structures needed to manage AI risk across the enterprise.
MAP
Establishes the context to identify and frame risks related to an AI system, its intended use, and its impacts on people, groups, and communities.
MEASURE
Assesses, analyses, and monitors identified AI risks and impacts through quantitative, qualitative, or mixed approaches.
MANAGE
Prioritises and acts on identified risks based on impact, likelihood, and available resources.

Source: NIST AI Risk Management Framework and NIST AI 100-1, AI RMF 1.0.

From framework to organisational practice

Every organisation needs to translate governance frameworks and standards into its own operating environment. That means taking account of its AI use, people, decisions, risks, controls and obligations.

This is the normal challenge of operationalising governance. Frameworks such as NIST AI RMF provide a recognised structure and shared vocabulary. Organisations still need to apply that structure in a way that reflects their size, sector, maturity, risk appetite and regulatory context.

The question is not whether a framework is too complex or too simple. It is whether the organisation has turned governance intent into operational behaviour: visible use, connected risk thinking, approved ways of working, named ownership and ongoing oversight.

Where CLEAR fits

CLEAR is Yunique AI's proprietary methodology for examining operational AI governance. It looks at how governance behaves in practice across five areas:

Clarity
Visibility into where and how AI is being used across the organisation.
Legal & Risk Alignment
Connecting AI use to relevant legal, regulatory, contractual, privacy, security and organisational risk considerations.
Enablement
Providing approved pathways, guidance, capability and training so people can use AI within established expectations.
Accountability
Establishing ownership, responsibility and clear decision rights around AI use.
Responsible Execution
Embedding oversight, evidence, monitoring, review and response into operational AI use.

CLEAR is designed to complement, not compete with, recognised frameworks, standards and regulatory requirements. It is not a certification, regulatory framework or recognised standard, and it does not replace NIST AI RMF, ISO/IEC 42001, ISO/IEC 42005, applicable law or sector-specific regulatory requirements.

Areas of alignment

NIST functions and CLEAR areas are cross-cutting. Rather than mapping one directly to the other, it is more useful to look at themes where both approaches give attention to the same governance concern.

Visibility and context
NIST AI RMF: MAP establishes context around AI systems, intended purposes, affected parties, risks and impacts.
CLEAR: Clarity focuses on organisational visibility into where and how AI is being used.
Relationship: CLEAR's visibility emphasis can support the organisational understanding needed for effective AI risk management, while NIST MAP considers a broader system and use-context perspective.
Governance and accountability
NIST AI RMF: GOVERN addresses organisational risk-management structures, policies, roles, responsibilities, culture and legal/regulatory considerations.
CLEAR: Accountability focuses on identifiable ownership, responsibility and decision rights, supported by Legal & Risk Alignment.
Relationship: The approaches overlap around governance responsibility, while CLEAR places particular emphasis on connecting AI use to named ownership and operational accountability.
Risk and obligations
NIST AI RMF: Risk considerations appear across GOVERN, MAP, MEASURE and MANAGE.
CLEAR: Legal & Risk Alignment connects AI use to relevant legal, privacy, security, contractual, regulatory and organisational risk considerations.
Relationship: CLEAR provides Yunique AI's methodology for bringing these considerations into governance decision-making; it does not replace the broader NIST risk-management process.
Operation and monitoring
NIST AI RMF: MEASURE and MANAGE address risk measurement, monitoring, prioritisation and treatment.
CLEAR: Responsible Execution examines how oversight, controls, evidence, monitoring, review and response operate in practice.
Relationship: Both recognise that AI governance must continue after initial approval and throughout ongoing use.
Organisational capability
NIST AI RMF: GOVERN includes organisational practices, competencies and risk-management culture.
CLEAR: Enablement focuses on approved pathways, guidance, training and capability that allow people to use AI within established governance expectations.
Relationship: Both recognise that effective AI governance depends on organisational capability, not policy alone.

This comparison illustrates areas of conceptual alignment. It is not an official NIST crosswalk, conformity assessment or claim of equivalence.

Building governance that works in practice

Established frameworks and proprietary methodologies do not need to compete. NIST AI RMF provides a flexible structure for managing AI risk. CLEAR provides Yunique AI's methodology for examining how governance operates across visibility, risk alignment, enablement, accountability and responsible execution.

Organisations already using NIST AI RMF can use CLEAR as a practical lens to examine how their governance arrangements operate day to day. Organisations starting with CLEAR can adopt recognised frameworks and standards such as NIST AI RMF where additional structure, external expectations or conformity requirements call for it.

Explore the CLEAR methodologyStart the CLEAR AssessmentRead the NIST AI RMF

This guide is provided for general information only and does not constitute legal advice. NIST AI RMF is published by the US National Institute of Standards and Technology; Yunique AI is not affiliated with or endorsed by NIST. Seek independent legal advice where appropriate.