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Understanding NIST AI 100-1: A Roadmap for Trustworthy AI

Updated: Dec 4, 2025

Artificial Intelligence is transforming industries, companies, individuals, and ultimately the entire world. When we look under the cover of AI, we discover that the main requirement is data—massive amounts of data. AI uses this data to learn and develop into a more accurate system. However, the volume and types of data it accesses can lead to ethical and societal risks.


AI isn’t inherently good or bad—it’s a mirror. It reflects the values, biases, and intentions of the people who build and deploy it. So, the real question isn’t “should we fear AI?” but “how do we ensure it serves humanity rather than undermines it?” To address this, NIST (National Institute of Standards and Technology) released AI 100-1, formally known as the Artificial Intelligence Risk Management Framework (AI RMF 1.0).



What Is NIST AI 100-1?

AI 100-1 is a globally recognized voluntary framework designed to help industries and organizations manage the risks associated with the adoption of artificial intelligence. AI poses a risk only when organizations fail to operate, train users, or establish "guardrails" for their implementations. By adopting this framework, organizations can align with industry best practices, improve the reliability of their AI systems, and foster trust among stakeholders, including customers, employees, regulators, and investors (Modi, 2025).


Companies should use this framework to ensure they are protecting their employees and customers. AI risk management offers a path to minimize the potential negative impact of AI systems, such as threats to civil liberties and rights, while also providing opportunities to maximize positive impacts. Addressing, documenting, and managing AI risks effectively can lead to more trustworthy AI systems (Tabassi, 2023).



The Four Core Functions

The framework consists of four core functions to guide organizations through the AI risk management lifecycle:


  1. Govern: This function establishes a comprehensive, organization-wide approach to AI risk management. It embeds governance structures, aligns technical and ethical principles, ensures legal compliance, and fosters a transparent, risk-aware culture throughout the AI system lifecycle.


  2. Map: This function focuses on understanding the full context in which an AI system operates. It enables organizations to identify and plan for risks more effectively. When deploying AI systems, it is essential to ask the right questions before fully implementing them.


  3. Measure: This involves using quantitative, qualitative, or mixed-method tools and methodologies to analyze, assess, benchmark, and monitor AI risks thoroughly. Measuring AI risk and documenting system functionality and trustworthiness is imperative.


  4. Manage: This function emphasizes the use of risk resources to map and measure risks identified in the Governance function. Here, plans are created to respond to, recover from, and communicate incidents or events.


Why It Matters

Traditional governance, risk, and compliance (GRC) and cybersecurity challenges are addressed through a wide array of frameworks and methodologies. While comprehensive, this abundance often leads to overlapping requirements and complex implementations that can overwhelm organizations.


In contrast, AI systems present a newer and limited set of risks. The lack of established, unified guidance makes it difficult to ensure effective governance, risk management, and compliance. The NIST AI Risk Management Framework (AI RMF 1.0) provides a structured and adaptable methodology specifically designed to address these challenges. It offers organizations a clear path toward the responsible and trustworthy deployment of AI. Both AI and traditional software technologies are subject to rapid innovation. Technology advances should be monitored and deployed to take advantage of those developments and work towards a future of AI that is both trustworthy and responsible (Tabassi, 2023).



Looking Ahead

According to NIST, they are committed to updating the framework as AI technologies evolve. The next formal review is scheduled for 2028. However, the GRC, cyber, and technology communities that advocate for AI innovation can influence updates before this review. I believe that organizations adopting this framework now will be better prepared for the future growth and changes that artificial intelligence brings to the world.


AI implementation can lead to positive outcomes as long as we prioritize Governance, Risk, and Compliance when implementing AI systems. The NIST AI 100 covers a broader scope and provides more detailed implementation guidance for the framework. I encourage everyone to read this for themselves and form their own opinions on the framework. Keeping GRC and Cyber mindset at the forefront is key to ensuring we keep data, people, and employees safe.


Conclusion

In summary, the NIST AI 100-1 framework is essential for organizations looking to navigate the complexities of AI adoption. By understanding its core functions and the importance of governance, mapping, measuring, and managing risks, we can create a safer and more responsible AI landscape. Let's embrace this opportunity to enhance our systems and ensure that AI serves humanity positively!


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