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NIST AI RMF Core Functions Reference for ERP AI

The NIST AI Risk Management Framework is voluntary but widely used as a common language for AI governance. This table condenses its four functions and seven trustworthiness traits into what an ERP assistant or agent team would actually do.

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Function	Category	Intent in plain words	What it looks like for ERP AI
GOVERN	Govern 1	Policies, processes and practices for AI risk are in place	Written AI policy covering which ERP data models may see and who approves new use cases
GOVERN	Govern 2	Accountability structures so teams are empowered and trained	Named owner for each assistant or agent, with escalation path
GOVERN	Govern 3	Workforce diversity, equity and inclusion considered in AI risk work	Include planners, buyers and shop-floor users, not only IT, in reviews
GOVERN	Govern 4	Culture that considers and communicates AI risk	Blameless review of wrong answers; users know how to report them
GOVERN	Govern 5	Engagement with relevant AI actors and affected parties	Feedback loop with users and with customers if outputs reach them
GOVERN	Govern 6	Policies for third-party software, data and supply chain risk	Vendor questionnaire, model license check and data flow review
MAP	Map 1	Context is established and understood	Document purpose, users, ERP modules and data touched
MAP	Map 2	The AI system is categorized	Classify as read-only assistant, recommender or agent that writes
MAP	Map 3	Capabilities, usage, goals, benefits and costs are understood	Baseline time per task and expected error cost before the pilot
MAP	Map 4	Risks and benefits mapped for all components including third-party	List model, retrieval layer, connectors and any hosted services
MAP	Map 5	Impacts to people and organizations are characterized	Consider wrong purchase, wrong price quote or wrong compliance answer
MEASURE	Measure 1	Appropriate methods and metrics are identified and applied	Golden question set with expected answers and a pass rate
MEASURE	Measure 2	Systems are evaluated for trustworthy characteristics	Test accuracy, security, privacy, bias and robustness to odd inputs
MEASURE	Measure 3	Mechanisms to track identified risks over time are in place	Dashboards for wrong-answer reports, refusals and drift after ERP upgrades
MEASURE	Measure 4	Feedback on measurement efficacy is gathered	Review whether the test set still reflects real user questions
MANAGE	Manage 1	Risks are prioritized, responded to and managed	Risk register with owner, mitigation and residual risk
MANAGE	Manage 2	Strategies to maximize benefits and minimize harm are planned	Human approval for any write-back; narrow tool scopes
MANAGE	Manage 3	Risks from third-party entities are managed	Exit plan if a hosted model or vendor changes terms
MANAGE	Manage 4	Risk treatments, incident response and recovery are documented	Runbook to disable an agent and roll back its actions
Trait	Valid and reliable	Output is accurate and consistent for its purpose	Answers trace back to ERP rows or document passages
Trait	Safe	Does not endanger life, health or property	No control of machines or safety decisions without separate engineering review
Trait	Secure and resilient	Resists attack and recovers from failure	Prompt injection tests, least-privilege roles, fallback when AI is down
Trait	Accountable and transparent	Information about the system is available to users	Show the query or sources used for each answer
Trait	Explainable and interpretable	Outputs can be understood in context	Plain-language reason attached to each recommendation
Trait	Privacy-enhanced	Protects human autonomy and identity	Mask personal data and keep employee data out of prompts
Trait	Fair, with harmful bias managed	Avoids unfair outcomes	Check worker scheduling or supplier scoring for skewed results

Category titles are paraphrased from the NIST AI Risk Management Framework 1.0 (January 2023) for orientation. NIST AI 600-1 adds a Generative AI Profile. Read the official publication for exact subcategory text.

Governance pack walkthrough

An optional review of your AI risk register and test evidence.

Optional. The tool and every export work without this. No spam.

How to use it

Walk the table top to bottom for one AI system at a time, and write the owner, evidence and status next to each row in your own risk register. Govern is organization-wide, so complete it once; Map, Measure and Manage repeat per system. Export the table to Excel and add columns for evidence links and review dates.

The four functions

Govern builds the policies, roles and culture. Map establishes context and identifies what could go wrong. Measure tests and tracks those risks with defined metrics. Manage decides what to do about them, including incident response. The traits at the bottom are the lens: valid and reliable, safe, secure and resilient, accountable and transparent, explainable, privacy-enhanced and fair.

Govern once, then Map, Measure and Manage per system
Use a golden question set to make Measure concrete
Tie Manage to a disable-and-rollback runbook
Revisit after every ERP upgrade or model change

Common mistakes

Teams treat the framework as a certification, but it is guidance, so nobody can be certified as compliant. Others skip Measure and rely on demos. A frequent gap is third-party risk: the model, the vector database and the connector all count. Finally, remember that model behavior can change when you upgrade the model, so evidence should carry version numbers.

Frequently Asked Questions

Is the NIST AI RMF mandatory?

No, it is voluntary guidance. Some customers, regulators and contracts reference it, so aligning with it makes security questionnaires and audits easier to answer.

How does it relate to the EU AI Act?

They are separate. The AI Act is binding law with defined tiers. The RMF is a voluntary process framework that can help you build the risk management and documentation practices the Act expects.

What is the Generative AI Profile?

NIST AI 600-1 is a companion profile that applies the RMF to generative AI risks such as confabulation, data privacy and information security. It is useful for LLM-based ERP assistants.

Where should a small team start?

Start with Map 1 and Measure 1: document the purpose and data, then build a small test set. Those two steps expose most practical problems early.

Netray can help turn this into a working governance pack for a specific ERP assistant, including test sets, logs and approval flows.

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