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Start by defining the case task and who has authority
“AI workflow automation” can mean different things: classifying an incoming request, routing a case to a team, extracting information from documents, drafting a summary, or recommending a next step. These uses do not carry identical risks. Before choosing a platform or enabling a feature, identify the exact task, the affected people, the information involved, and what happens when the system is wrong.
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For each proposed use, document the following. This is a practical planning aid synthesized from federal risk and records guidance, not a required NIST or NARA checklist:
- Purpose and scope: What case task is being assisted, and which cases or populations are in scope?
- AI’s role: What does the system produce—such as a route, extracted field, summary, or recommendation—and what does it not decide?
- Decision authority: Which employee reviews the output, corrects it, overrides it, or escalates an uncertain case? Which actions require a human decision?
- Information flow: What data enters the system, what output leaves it, and where are those materials stored or sent next?
- System context: Which case-management, identity, document, and records systems connect to the AI tool?
- Risk and obligations: Which security, privacy, accessibility, procurement, and records requirements apply to this agency and use?
Make the human role operational, not merely aspirational. Staff need a way to see the output and relevant context, flag or correct errors, and continue the case through an established path when the AI service is unavailable or its result cannot be trusted. The agency should specify when review is required and who owns the case at each handoff.
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Use the NIST AI RMF to organize AI risk work
NIST AI RMF 1.0 is voluntary, use-case-agnostic guidance—not a universal legal mandate or certification. It groups AI risk work into four functions. NIST says the framework is being revised, so confirm its current status when applying it; the version discussed here is 1.0.
| Function | How it can inform case-workflow planning |
|---|---|
| Govern | Set roles, policies, accountability, and oversight for the AI use, including who can approve changes and respond to problems. |
| Map | Describe the task, intended use, affected population, operational context, dependencies, and potential consequences of errors. |
| Measure | Evaluate relevant system performance and risks in the intended context, including whether outputs are useful and where they may fail. |
| Manage | Prioritize identified risks and decide how to respond, monitor, or change the use when conditions or evidence change. |
NIST’s AI RMF Playbook offers optional suggestions aligned with the four functions. NIST cautions: “The Playbook is neither a checklist nor set of steps to be followed in its entirety.” Treat it as a planning resource rather than proof that a system is safe, compliant, or suitable for a particular agency.
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Keep AI governance separate from cybersecurity, privacy, and records management
These disciplines support an auditable workflow, but they answer different questions. AI risk governance addresses how the organization identifies and manages risks from an AI use. Cybersecurity and privacy risk management concern protection and handling of systems and information over their life cycle. Records management determines whether materials are government records and how those records must be managed. Satisfying one discipline does not automatically satisfy the others.
| Area | Question for the agency | Federal guidance in scope here |
|---|---|---|
| AI risk governance | Who is accountable for the AI use, what are its boundaries, and how are risks evaluated and managed? | NIST AI RMF 1.0 provides a voluntary structure through Govern, Map, Measure, and Manage. |
| Cybersecurity and privacy | How are system and information risks addressed through development, deployment, and operation? | NIST describes its Risk Management Framework as a flexible seven-step process integrating cybersecurity, privacy, and supply-chain risk management into the system development life cycle. The appropriate controls depend on agency and system context. |
| Records management | Which AI-related materials qualify as federal records, and how must they be retained or disposed of? | NARA’s AC 11.2026 addresses Federal Records Act treatment of AI-related materials for federal departments and agencies. |
NIST’s Risk Management Framework links to standards and guidance supporting FISMA risk-management programs, including selecting, implementing, assessing, and continuously monitoring controls. That general description does not determine an agency’s authorization boundary, impact level, required control set, privacy assessment, or procurement eligibility. Those conclusions require the relevant agency, system, data, and deployment details.
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Plan an audit trail that can explain a case later
An audit trail should let authorized reviewers reconstruct how AI participated in a case and what people did with its output. A technical log by itself is not a records-management plan. NARA identifies inputs, outputs, data, audit trails, software, and other AI materials as potentially subject to federal records analysis. Which materials meet the Federal Records Act definition depends on the circumstances; the guidance does not prescribe one universal logging design.
For each workflow, decide what evidence the agency needs to preserve to explain the AI-assisted step. Depending on the use, that may include the input and output, the relevant data and software, and human review, correction, or override actions. Identify where each artifact is captured, who can access it, how it can be exported, and how it connects to the case record. Make these decisions with records, program, privacy, security, and system owners rather than assuming the vendor’s default logs are sufficient.
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NARA’s AC 11.2026, dated August 21, 2026 and reviewed September 16, 2026, guides federal departments and agencies in applying the Federal Records Act to existing AI uses. It says: “Agencies may only dispose of AI-related federal records in accordance with a NARA-approved records schedule.” The memorandum concerns records-management requirements only; it does not establish AI governance, e-discovery, privacy, security, or ethical-use policy. Agencies should determine which materials are federal records and apply the relevant approved schedule before setting retention or disposition practices.
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Build a deployment process around review, testing, and change
A workflow that works in a demonstration may behave differently with real cases, changing data, or connected systems. Use a staged process that makes ownership and evidence visible at each decision point.
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- Specify the use. Define the task, intended users, affected population, information types, boundaries, and cases that must be handled without AI assistance.
- Map the workflow and data. Trace inputs and outputs through connected applications. Identify where human review occurs and how the case proceeds if an output is missing, delayed, or unsuitable.
- Assign accountable owners. Name the program owner, system owner, staff responsible for reviewing cases, and the records, security, and privacy roles that must participate.
- Assess risks and controls. Use the AI RMF to organize AI risk work and the agency’s applicable cybersecurity and privacy processes to address system and information risks. Determine requirements for the specific agency and deployment rather than assuming a generic baseline applies.
- Test against the intended task. Define what an acceptable output means for the actual workflow, examine errors and edge cases, and establish how staff report problems. Do not treat a vendor demonstration or a general framework as evidence of performance for the agency’s cases.
- Set records capture and retention practices. Identify materials that may be records, determine how they will be captured and related to the case, and apply the agency’s approved schedule and other relevant requirements.
- Monitor and revise. Assign responsibility for reviewing performance, incidents, workflow changes, and system updates. Reassess risks when the use, data, software, integrations, or operating context changes.
Evaluate platforms against agency requirements
No platform is suitable merely because it offers AI features or describes itself as government-ready. Use the agency’s documented use case and required approvals to compare candidates. These are evaluation dimensions inferred from federal risk and records guidance, not ratings of products.
- Records handling: Can the agency capture, export, retain, and dispose of relevant materials under its requirements?
- Auditability: Can reviewers understand the inputs, outputs, data and software involved, and human actions taken?
- Security, privacy, and supply chain: Can the agency assess the system across its life cycle using the applicable control and privacy processes?
- Human oversight: Can authorized staff review, correct, override, or escalate outputs in the actual workflow?
- Interoperability: Does the platform work with the agency’s case, identity, and records systems without losing needed context or evidence?
- Mission fit and accessibility: Does the workflow serve the agency’s purpose and remain usable for the people who need to interact with it?
- Procurement and authorization: Has the agency established the applicable procurement, security authorization, and privacy requirements for this deployment?
Use public examples as references, not proof
NIST’s AI Resource Center lists the City of San Jose, California, and PEAT (Partnership on Employment & Accessible Technology), funded by the Department of Labor’s Office of Disability Employment Policy, among government-related examples of the AI RMF in use. NIST explicitly does not validate or endorse an organization’s approach. These listings can help identify public examples to examine, but they do not establish outcomes, prove that a case-management system is effective, or show that a particular approach will meet another agency’s needs.
The Federal Reserve Board provides a separate example of publishing an AI use-case inventory. Its page says the inventory is published pursuant to the AI in Government Act of 2020 and OMB Memorandum M-25-21, and that the memorandum directs annual inventories. The page offers 2025 and 2024 materials and lists February 6, 2026 as its last update. This is a dated example of inventory practice, not a substitute for checking current government-wide directives or the rules that apply to another agency.
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The federal materials offer a useful starting structure for governance, system risk, and records analysis, but they do not decide whether a specific AI-assisted case workflow is compliant, secure, appropriate, or authorized. That depends on the jurisdiction and agency, the task and information involved, system architecture, and applicable agency requirements. State and local governments or organizations outside the U.S. should identify their own governing rules rather than treating federal guidance as binding on them.
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