A high-level, technology-neutral framework published in 2023 to help organizations manage AI risks across four functions: Govern, Map, Measure, and Manage.

NIST Generative AI Profile (AI 600-1)

A specialized profile that extends the AI RMF with generative-AI-specific risk scenarios and outcomes, covering systems like chatbots, code assistants, and media generators.

Profile (in NIST AI RMF)

A tailored selection and prioritization of AI RMF outcomes for a specific context, such as a sector or technology family, describing which outcomes matter most and how they apply.

Risks related to the quality and impact of generated content, including hallucinations, bias, toxic language, harmful advice, and deceptive synthetic media.

When a generative model or its surrounding system exposes sensitive information, such as personal data, trade secrets, or proprietary code, from training data or user prompts.

The NIST Artificial Intelligence Risk Management Framework, released in 2023, which provides a general structure for managing AI risks across four functions: Govern, Map, Measure, and Manage.

What is Hallucination?

A phenomenon where a generative AI system produces outputs that are fluent and confident but factually incorrect or fabricated.

What is Synthetic Media?

Media content (images, audio, video, text) produced or heavily modified by AI, including deepfakes and AI-generated images or voice clones.

Introducing the Generative AI Profile (NIST AI 600-1): Scope and Structure — Operationalizing the NIST AI RMF and Generative AI Profile in Practice

1. From AI RMF to Generative AI Profile: Why a New Document?

From AI RMF to GenAI

NIST released the AI Risk Management Framework (AI RMF 1.0) in 2023 to guide risk management for all kinds of AI systems. Soon after, generative AI raised new, distinctive risks that needed extra guidance.

Why a GenAI Profile?

Generative AI can hallucinate, leak sensitive data, generate synthetic media, and enable misuse at scale. NIST responded with the Generative AI Profile (NIST AI 600-1), a focused companion to the AI RMF.

Map vs. Route

The AI RMF is the general map for AI risk. The Generative AI Profile is a highlighted route on that map for generative models, tailoring the framework to GenAI-specific contexts, risks, and outcomes.

What You Will Learn

You will see how the Profile fits with the AI RMF, understand its scope and structure, recognize main GenAI risk themes, and connect it back to the Govern, Map, Measure, Manage functions you studied earlier.

2. What is a NIST "Profile" in the AI RMF World?

What is a Profile?

In the AI RMF, a Profile is a selection and prioritization of outcomes, tailored to a specific context. It answers: for this AI system, which outcomes matter most and how do they show up in practice?

GenAI Profile as a Technology Profile

The Generative AI Profile is a technology-focused profile. It applies across sectors but focuses on generative models and applications, from chatbots to image and code generators.

Linked to AI RMF Functions

The Profile is organized around the same four AI RMF functions: Govern, Map, Measure, and Manage. Each GenAI outcome is linked back to these core functions.

Extending, Not Replacing

The Generative AI Profile does not replace the AI RMF. It extends it with GenAI-specific details and helps organizations apply the RMF to systems like copilots, media generators, and multimodal tools.

3. Scope: What Systems Does the Generative AI Profile Cover?

System, Not Just Model

The Generative AI Profile covers whole generative AI systems, not just the core model. It applies to foundation models, fine-tuned models, and applications that call GenAI APIs.

Types of GenAI Tools

It includes tools that generate text, code, images, audio, video, 3D content, or multimodal outputs, such as chatbots, code copilots, and image generators.

Lifecycle Coverage

The Profile looks at risks across data and model development, deployment and integration, and use and monitoring, including feedback loops and incident response.

Use-Case Neutral

The Profile is sector-neutral. It describes risk scenarios and outcomes you can adapt to healthcare, finance, education, or other domains where you use GenAI.

4. How the Profile Extends AI RMF: A Simple Mapping

Generic vs. GenAI-Specific

The AI RMF has generic outcomes, like measuring AI performance and harmful impacts. The Generative AI Profile turns these into concrete expectations for generative systems.

Measure: A Chatbot Example

For a GenAI chatbot, Measure outcomes include tracking hallucination rates, testing prompt injection, monitoring content safety, and probing for data leakage of secrets or training data.

Govern: Ownership in GenAI

Govern outcomes become GenAI-specific: clear owners for prompt safety policies, approvals for fine-tuning data and plugins, and escalation paths for deepfake or hallucination incidents.

Same Functions, More Detail

In each case the Profile keeps the AI RMF functions but adds detailed GenAI risk scenarios and outcomes, making the framework more actionable for generative systems.

5. Structure: How the Generative AI Profile is Organized

Four Familiar Functions

The Generative AI Profile is structured around the same four AI RMF functions: Govern, Map, Measure, and Manage, each with GenAI-focused content.

Inside Each Function

Within each function, the Profile includes subcategories, GenAI-specific outcomes, and risk scenarios such as synthetic media misuse or code leakage.

Outcome Style

Outcomes are written as statements, for example: "Potential for synthetic media misuse is assessed for each deployment context."

Skeleton and Muscles

If you understand the AI RMF functions, you know the skeleton. The Generative AI Profile adds GenAI-specific muscles and nerves: hallucinations, jailbreaks, data poisoning, and more.

6. Core GenAI Risk Themes in the Profile

Content Harms and Quality

GenAI can hallucinate facts, produce toxic or biased language, give harmful advice, and create synthetic media that supports harassment or disinformation.

Data Leakage and Privacy

Risks include models revealing personal data, leaking confidential business information from prompts, and exposing sensitive data through logs or fine-tuning.

IP and Copyright

GenAI may reproduce copyrighted training material, generate content similar to protected works, or output code that conflicts with open-source licenses.

Security and Misuse

Attackers can use GenAI for prompt injection, jailbreaks, phishing, malware, and scaling cyberattacks far beyond what manual methods allow.

Systemic and Societal Impacts

Deepfakes can erode trust in media, GenAI can shift labor markets, and biased outputs can reinforce stereotypes or widen access gaps.

7. Thought Exercise: Mapping a GenAI Use Case to Risk Themes

Activity: Take a common GenAI use case and connect it to the risk themes from the Profile.

Step 1: Pick one use case (choose one):

A. A university uses a GenAI chatbot to answer student questions about courses and financial aid.
B. A bank uses a GenAI assistant to help staff draft emails to customers.
C. A media company uses a GenAI image generator to create marketing visuals.

Step 2: For your chosen use case, answer these prompts (mentally or in writing):

Which content harms and quality issues are most likely? (e.g., hallucinations, bias)
Where could data leakage happen? Consider both training data and user prompts.
What IP or copyright concerns might arise?
How could the system be misused at scale? By insiders? By attackers?

Step 3: Connect to AI RMF functions

Govern: Who should own policies and approvals for this GenAI use?
Map: What context do you need to document (users, stakes, environment)?
Measure: What metrics or tests would you run first?
Manage: What is one control or safeguard you would implement immediately?

Try to be concrete. For example, if you chose the university chatbot, you might note that hallucinations about financial aid rules could have real financial consequences for students, so measurement should include tests against official policy documents.

You do not need to be perfect. The goal is to start thinking like the Generative AI Profile: for each use case, systematically scan for GenAI-specific risks and link them to concrete outcomes and actions.

8. Quick Check: Profile vs. Framework

Answer this question to check your understanding of how the Generative AI Profile relates to the AI RMF.

Which statement best describes how the Generative AI Profile (NIST AI 600-1) relates to the AI RMF 1.0?

It replaces the AI RMF and should be used instead of it for any generative AI system.
It is a specialized profile that extends the AI RMF with GenAI-specific risk scenarios and outcomes.
It is an unrelated guideline that focuses only on copyright law for generative AI.

Show Answer

Answer: B) It is a specialized profile that extends the AI RMF with GenAI-specific risk scenarios and outcomes.

The Generative AI Profile is a specialized profile built on top of the AI RMF. It does not replace the framework; it tailors and extends it with GenAI-specific risk scenarios and outcomes across Govern, Map, Measure, and Manage.

9. Quick Check: GenAI Risk Themes

Test your knowledge of key GenAI risk themes highlighted in the Profile.

Which set lists risk themes that are all clearly emphasized for generative AI in the NIST Generative AI Profile?

Battery efficiency, screen brightness, and device temperature.
Content harms and quality, data leakage and privacy, IP and copyright, security and misuse.
Keyboard layout, mouse sensitivity, and monitor size.