Governance & Risk Management

Governance & Risk Management: Prototype

NIST AI Risk Management Framework (AI RMF)

H2A conducted an independent analysis of the NIST AI Risk Management Framework to examine why organizations can follow governance standards yet still experience significant AI failures.

While many compliance programs focus on documentation and process requirements, our analysis evaluated the assumptions underlying the framework and how they translate into real-world operational environments.

About the Framework

The NIST AI RMF is a voluntary governance framework designed to help organizations identify, assess, and manage AI-related risks while supporting responsible innovation.

Its goals include:

• Identifying and managing AI risks

• Strengthening organizational governance

• Supporting ongoing monitoring and oversight

Our Analysis

Rather than evaluating a specific implementation, we examined the assumptions built into the framework itself.

The review focused on:

• Whether AI risks can be fully identified and managed through predefined governance processes

• Whether governance expectations align with real-world engineering and operational constraints

Key Findings

The framework provides valuable guidance, but its assumptions do not always reflect the realities of deployed AI systems.

AI risks are not static. They evolve through interactions between models, users, and changing environments.

The framework also assumes a level of visibility and control that organizations may not have once systems are operating in production.

Finally, governance requirements often exceed what engineering teams can realistically monitor, measure, or enforce in practice.

Why It Matters

Organizations can satisfy governance requirements on paper while remaining exposed to operational, legal, and organizational risks.

Effective AI governance requires more than compliance. It requires continuous evaluation of how systems behave in real-world environments.

Key Takeaway

The NIST AI RMF is a useful starting point, but organizations should not treat governance frameworks as complete solutions.

Strong governance emerges when policies, operational realities, and technical capabilities are aligned, allowing organizations to manage AI risks as they evolve over time.

Read Analysis