The NIST AI RMF Generative AI Profile contains more than 200 suggested actions for managing generative AI risks. These cover everything from governance and data privacy to testing, security, and incident response.

 

Working through every action without considering its relevance to your AI systems can waste time and resources. Some actions will address risks your organization faces directly, while others may be less relevant to your use cases.

 

The solution is to treat the profile as a risk management resource, not a checklist to complete from beginning to end. Start by identifying the generative AI systems your organization uses, assessing their risks, selecting relevant actions, and assigning clear ownership for implementation.

 

 

How to implement the NIST AI RMF Generative AI Profile

 

Here’s how to put the NIST AI RMF Generative AI Profile into practice.

 

1. Inventory your generative AI systems and use cases

 

Identify where your organization develops, deploys, acquires, or uses generative AI. Include standalone AI tools, internally developed applications, third-party models, and AI features embedded in existing software.

 

Do not rely on your official AI register alone. Employees may use unapproved tools, creating shadow AI that exposes company or customer information without appropriate safeguards. Look for it through procurement records, available network or application logs, and a short employee survey.

 

For each system, document:

 

• Purpose: What business task does the system support?
• Data: What information does it process, store, or generate?
• Access: Which systems, documents, and resources can it access?
• Dependencies: Which models, vendors, APIs, and other components does it rely on?
• Ownership: Who is responsible for the system and its business use?

 

For a customer-support chatbot, the inventory might show that it answers customer questions using an external large language model and retrieves information from the company’s product documentation. It may also receive order details or other personal information through customer conversations.

 

2. Identify the risks that apply

 

Use the profile’s 12 generative AI risk categories to identify what could go wrong in each context. You don’t need to assume every category applies equally to every system.

 

For the customer-support chatbot, the initial assessment could include:

 

• Data Privacy: Customer details could be exposed through prompts, logs, or the provider’s data-handling practices.

 

• Confabulation: The chatbot could invent a refund policy or provide an incorrect answer about an order.

 

• Information Security: Weak access controls or prompt-based attacks could expose information the chatbot should not retrieve.

 

• Human-AI Configuration: Customers or support agents could trust an incorrect answer without checking it.

 

• Value Chain and Component Integration: The organization may have limited visibility into the model provider’s controls, subcontractors, or incident response.

 

Record the potential impact, existing safeguards, and gaps that need further investigation. This assessment will guide your selection of profile actions.

 

3. Filter the profile’s suggested actions

 

The NIST AI RMF Generative AI Profile contains more than 200 suggested actions. You don’t need to work through all of them. Start with the risks identified in Step 2 and use the profile’s risk tags to find relevant actions.

 

Follow this process:

 

• Filter by risk. Find actions tagged with the risks identified in your assessment, such as Data Privacy or Confabulation.

 

• Check the function and subcategory. Each action maps to an AI RMF function and subcategory, helping you understand what it addresses.

 

• Confirm relevance. Check whether the action applies to your role as an AI developer, deployer, or user.

 

• Shortlist actions. Keep actions that address a risk or control gap your organization needs to resolve.

 

For example, an organization using a third-party chatbot could start by filtering actions tagged for Data Privacy and Value Chain and Component Integration. Two relevant actions are GV-6.1-009, which extends vendor due diligence for generative AI procurement to cover intellectual property, data privacy, security, and other risks, and GV-6.2-003, which addresses incident response planning for third-party generative AI technologies. Review each action’s full set of risk tags and applicability before adding it to your shortlist. Record its ID, the risks it addresses, and why it applies to your system.

 

4. Prioritize the selected actions

 

Rank shortlisted actions according to potential harm, likelihood, existing control gaps, and the number of people or processes affected. Consider implementation effort, but don’t let ease of completion outweigh risk severity. Also address applicable legal and contractual obligations as required.

 

For the customer-support chatbot example, an initial priority table might look like this:

 

NIST risk category	Identified gap	Planned measure	Example action	Priority
Data privacy	Provider’s data-retention terms are unclear	Review data-handling terms and restrict unnecessary personal data in prompts	GV-6.1-009	High
Confabulation	Chatbot may invent refund policies	Test responses against approved support content	MS-2.5-003	High
Value Chain and Component Integration	Supplier controls are not fully assessed	Complete vendor due diligence and clarify incident responsibilities	GV-6.2-003	High
Human-AI Configuration	Customers may overtrust chatbot responses	Explain the chatbot’s role and provide access to human support	MP-5.1-003	Medium

 

These priorities are illustrative, not ratings prescribed by NIST. Assign your own based on the evidence and risk criteria for each system.

 

5. Assign owners and define evidence

 

Every selected action needs an accountable owner and a clear completion requirement. For the chatbot, engineering might own technical safeguards, the privacy team might review data handling, and procurement might assess the model provider. Customer support can define when the chatbot must escalate a query to a human.

 

For each action, record:

 

• The responsible owner and due date.
• The expected outcome.
• The evidence needed to confirm completion.
• The status and any outstanding issues.

 

For example, completing a privacy review should produce evidence of the provider assessment and the safeguards implemented to limit unnecessary customer data.

 

 

6. Test the safeguards before deployment

 

Before releasing a generative AI system, test whether its safeguards address the risks identified during assessment. Tests should reflect the system’s intended use and the potential consequences of failure. A general-purpose writing assistant doesn’t need the same evaluation process as an AI application that can retrieve sensitive records or execute actions in connected systems.

 

Depending on the use case, testing could include:

 

• Output reliability: Check whether generated answers are accurate and supported by reliable sources.

 

• Privacy: Test whether the system exposes sensitive information through inputs, outputs, or retrieval.

 

• Security: Evaluate access restrictions and the system’s response to attempts to bypass safeguards.

 

• Harmful outputs: Assess whether safeguards work for relevant harmful or inappropriate requests.

 

• Human oversight: Check whether users understand the system’s limitations and know when to verify its outputs.

 

The profile specifically recommends AI red-teaming, including tests for prompt injection (MS-2.7-007) and privacy leakage (MS-2.10-001). For the chatbot, that means testing whether crafted prompts, or malicious content in retrieved documents, can make it reveal other customers’ order details.

 

Also, document the results, fix significant failures, and retest. If your AI application cannot meet your acceptance criteria, restrict its use or delay deployment until you address the risks.

 

7. Address content provenance and incident disclosure

 

The profile identifies four cross-cutting themes: Governance, Content Provenance, Pre-deployment Testing, and Incident Disclosure. Use them to check whether your implementation plan overlooks important areas.

 

For instance:

 

• Content provenance: For the chatbot, retain enough information to trace answers to relevant knowledge-base sources where feasible. Make the chatbot’s role clear to users, and establish how you will review source or model changes.

 

• Incident disclosure: Define how staff report AI incidents, who investigates them, and who needs to be notified. Agree with the model provider on incident notifications and the information it must share. Follow applicable reporting requirements.

 

These measures should fit into your existing policies and procedures, not become separate paperwork exercises.

 

8. Integrate AI risk management into existing workflows

 

You don’t need a separate process for every action in the profile. Where appropriate, incorporate AI risk management into the workflows your organization already uses.

 

For example:

 

• Add AI-related questions to existing vendor risk assessments.
• Include AI use cases in the enterprise risk register.
• Incorporate AI access and data-handling requirements into security reviews.
• Add generative AI testing to software release and change management processes.
• Connect AI incidents to existing incident response procedures.
• Include AI-related controls and evidence in internal audit planning.

 

This approach reduces duplicate work and makes AI risk management part of normal business operations.

 

9. Track progress without creating unnecessary paperwork

 

Use a tracker to connect each action to its risk, owner, status, and evidence. Reassess when the model or provider changes, you add new data sources, the chatbot’s purpose expands, or an incident reveals a new risk.

 

A completed tracker entry might look like this:

 

Field	Example entry
AI system	Customer-support chatbot
NIST risk category	Data Privacy
Profile action ID	GV-6.1-009
Identified gap	Provider data handling terms need review
Planned measure	Review privacy requirements and provider terms; limit unnecessary personal data
Owner	Privacy lead
Priority	High
Status	In progress
Evidence	Provider review, data-flow assessment, and data-minimization test results
Residual risk	Assess after implementation and testing

 

This is an illustrative record. Completing the listed action alone does not resolve every privacy risk. Review the evidence and assess the remaining risk before closing the item.

 

How CyberArrow can support AI risk management

 

Implementing the NIST AI RMF Generative AI Profile involves more than identifying risks. Teams also need to assign responsibility, track remediation, maintain evidence, and monitor control effectiveness.

 

CyberArrow supports GRC activities such as risk assessments, control tracking, evidence collection, and audit workflows. These capabilities can help organizations connect AI-related risks with their broader governance, risk, and compliance processes.

 

With CyberArrow, organizations can:

 

• Document AI-related risks and assign risk owners.
• Track mitigation activities and outstanding remediation work.
• Centralize evidence supporting risk and control assessments.
• Connect AI risk oversight with existing compliance and audit processes.

 

Looking to make AI risk management more structured and manageable? Schedule a free demo with CyberArrow to explore how its GRC capabilities can support your organization’s governance processes.

 

 

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