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Government agencies can use AI to route, summarize, or organize casework without handing the system decision authority—but only if they define where AI acts, preserve meaningful human review, manage security and privacy throughout the system’s life cycle, and decide how AI-related materials will be captured and retained. For U.S. federal agencies, NIST’s voluntary AI Risk Management Framework (AI RMF) offers a planning structure; NARA’s 2026 guidance addresses how the Federal Records Act applies to AI-related records. Neither source, by itself, settles an agency’s specific controls, privacy obligations, procurement requirements, or retention schedule.
What does secure, auditable AI case management require?
AI workflow automation is safest to treat as assistance within a case process, not as an unexamined substitute for the people and rules responsible for the case. An agency should be able to explain what the AI is permitted to do, what information it receives and produces, who can check or correct its work, how the system is protected, and how relevant materials are managed as records.
Those questions belong to related but distinct disciplines. AI risk governance addresses purpose, accountability, and the risks of the AI use. Cybersecurity and privacy risk management address the system, information, and protections across its life cycle. Records management determines which materials are government records and how they must be retained or disposed of. A technically detailed log does not automatically satisfy records requirements, and a records schedule does not establish that an AI use is safe or appropriate.
| Concern | Question for the agency | Relevant federal guidance |
|---|---|---|
| AI risk governance | Why is AI used in this workflow, who is accountable, and how are risks assessed and managed? | NIST AI RMF 1.0 organizes voluntary guidance into Govern, Map, Measure, and Manage. |
| Cybersecurity and privacy | How are system and information risks handled during development, deployment, operation, and change? | NIST’s Risk Management Framework describes a life-cycle process integrating cybersecurity, privacy, and supply-chain risk management. |
| Records management | Which AI-related materials are federal records, and what approved schedule governs their disposition? | NARA’s AC 11.2026 addresses Federal Records Act treatment of AI-related materials for federal agencies. |
This is a U.S. federal framing, not a jurisdiction-specific compliance determination. State, local, tribal, territorial, and non-U.S. governments may have different laws, records rules, security baselines, and procurement requirements. Even within the federal government, the right requirements depend on the agency, mission, information, system boundary, and deployment.
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Can an agency use AI to route or summarize a case without losing accountability?
Potentially, if the agency limits the use to a defined task and retains accountable human authority where the consequences require it. Routing a file to a work queue and summarizing material for a caseworker are different from deciding eligibility, assigning a legal status, or denying a benefit. The workflow design should make that distinction explicit rather than treating all automation as equivalent.
Define the permitted role
For each workflow, describe the task in plain language: for example, “suggest a queue based on case type” or “draft a summary for a reviewer.” State what the system must not do, including whether it may take an action that affects a person’s case without review. The agency should specify who owns the process, who is responsible for the AI system, and who can pause its use when an issue arises.
Make review operational
Human review is meaningful only when reviewers can see enough context to evaluate the AI output, correct it, and decline to use it. Define the reviewer’s authority, access to underlying case materials, escalation path, and responsibility for recording consequential corrections or overrides. Avoid a workflow in which a person is nominally in the loop but cannot understand or challenge the system’s recommendation.
Check the whole handoff
Map how case information moves between the AI tool, the case-management application, staff, and any records or document systems. A correct summary can still cause harm if it is attached to the wrong case, silently overwrites a source document, or is treated downstream as an approved finding. Specify what happens when the system is unavailable, returns an uncertain result, or encounters a case outside its intended scope.
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How can an agency structure its AI risk review?
NIST AI RMF 1.0 is voluntary, use-case-agnostic guidance—not a universal legal mandate or certification. Its four functions provide a way to organize planning and discussion. NIST says the framework is being revised, so agencies should verify the current version and related guidance when they begin or update an effort.
| AI RMF function | Case-management application |
|---|---|
| Govern | Assign accountable roles; set policy for acceptable use, review, escalation, and change; ensure decision authority is clear. |
| Map | Describe the case task, affected people, operating context, data flows, system dependencies, and likely consequences of error. |
| Measure | Evaluate performance and risks against the agency’s stated purpose and context, including failure cases that could affect case handling. |
| Manage | Prioritize risks, select responses, monitor operation, and revise or stop the use when risks or conditions change. |
NIST’s AI RMF Playbook offers optional suggestions aligned to the four functions. It is not an exhaustive required procedure: “The Playbook is neither a checklist nor set of steps to be followed in its entirety.” Agencies should use it as a source of ideas, not as evidence that completing a fixed list guarantees a responsible deployment.
Document the use in a reviewable form
As a practical synthesis of NIST’s risk guidance and NARA’s records guidance—not a prescribed NIST or NARA checklist—an agency can maintain a use-case record containing:
- The case task, intended benefit, affected population, and boundaries of the AI role.
- Where AI is used in the workflow, what decisions remain with staff, and how a reviewer can correct, override, or escalate an output.
- Information entering and leaving the system, including connections to case files and other applications.
- The process for assessing AI performance and risks in the intended context, and for revisiting that assessment after changes.
- Security, privacy, and supply-chain risk responsibilities across development, deployment, operation, and retirement.
- Potential records created or received through the use, the applicable records authority, and how required material can be captured and retained.
The agency must determine which controls, reviews, and schedules actually apply. The framework descriptions do not decide an agency’s authorization boundary, impact level, privacy assessment, required control set, or procurement eligibility.
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How do agencies keep an audit trail for AI-assisted cases?
Start with the recordkeeping question, not with a vendor’s logging feature. 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 identifies inputs, outputs, data, audit trails, software, and other AI-related materials as items that may require records analysis. Whether a particular item is a federal record depends on the applicable records definition and circumstances; a log entry is not automatically a complete or sufficient record of the case.
The memorandum addresses Federal Records Act requirements only. It does not establish AI governance, e-discovery, privacy, security, or ethical-use policy. Agencies need to handle those questions under the policies and authorities applicable to their systems and missions.
Decide what must be reconstructable
For a case workflow, determine what evidence is needed to understand how an AI output entered the case and what staff did with it. Depending on the use and governing requirements, that may involve the input and output, relevant data, the software or system involved, the point in the workflow where the output was used, and human review or correction. NARA’s guidance identifies categories for analysis; it does not prescribe one universal logging design.
Specify how relevant materials can be associated with the correct case, exported in usable form, protected from unauthorized change or access, and made available to authorized reviewers. Include the system’s versions or changes where needed to understand the materials, and define who is responsible for ensuring capture. The exact design should follow the agency’s use, records obligations, and security and privacy requirements rather than a generic logging template.
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Apply an approved records schedule
For federal agencies, AI-related federal records cannot be disposed of ad hoc. AC 11.2026 states: “Agencies may only dispose of AI-related federal records in accordance with a NARA-approved records schedule.” The agency’s records officials should determine the applicable schedule and how it covers the AI-related materials in the workflow. The memorandum does not supply one retention period for every AI system or case type.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should security and privacy be handled over the system life cycle?
NIST describes its Risk Management Framework as a flexible seven-step process that integrates cybersecurity, privacy, and supply-chain risk management into system development and operation. It connects to standards and guidance supporting FISMA risk-management programs, including the selection, implementation, assessment, and continuous monitoring of controls. The appropriate control baseline and implementation depend on agency and system context.
For an AI-enabled case process, security and privacy planning should cover the full system and its information flows, not just the AI component in isolation. Identify system dependencies and data exchanges; establish how access and change are managed; and determine how risks are assessed and monitored as the system is deployed and operated. These are planning considerations, not a substitute for the agency’s applicable authorization, privacy, and security processes.
Do not infer from the NIST framework page that a particular deployment has an authorization, meets a specified impact level, has completed a privacy assessment, or is eligible for procurement. Those determinations require details about the agency, system, information, and proposed use that are not established by general framework guidance.
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How should an agency evaluate a case-management platform?
No platform can be judged suitable for government casework from an AI feature list alone. Translate the workflow’s requirements into questions for vendors and agency reviewers, and assess the answers against the agency’s mission, security and privacy processes, records obligations, and existing systems. The following are evaluation dimensions inferred from NIST and NARA guidance, not vendor ratings or a formal certification scheme.
- Records handling: Can required materials be captured, exported, retained, and disposed of under the applicable agency schedule and processes?
- Auditability: Can the agency understand relevant inputs, outputs, data, software, and human actions associated with a case?
- Security, privacy, and supply chain: Can the system be assessed and managed within the agency’s life-cycle risk processes?
- Human authority: Can authorized staff review, correct, override, and escalate AI outputs in the actual workflow?
- Interoperability: Does the proposed integration preserve case context and support required records and access processes?
- Mission fit and accessibility: Does the workflow work for the agency’s actual users and affected population?
- Procurement and authorization: Has the agency established what approvals and procurement conditions apply to this specific deployment?
Require evidence for the operational workflow, not merely assurances about model capability. For example, ask to see how an output is presented to a caseworker, how a correction is recorded, what materials can be exported, and how changes to the system are handled. Agency staff responsible for records, security, privacy, procurement, accessibility, and case operations should be involved early enough to identify requirements before a platform choice constrains the design.
What do public-sector examples establish—and what do they not?
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 AI RMF use. NIST explicitly says it does not validate or endorse an individual organization’s approach. These examples show reported use of the framework, not proven case-management outcomes, vendor endorsement, or evidence that a particular system is effective.
The Federal Reserve Board provides a separate example of an agency publishing an AI use-case inventory. Its inventory page says it is published pursuant to the AI in Government Act of 2020 and OMB Memorandum M-25-21, and says the memorandum directs annual inventories. The page provides 2025 and 2024 materials and lists February 6, 2026 as its last update. This is a dated example of inventory practice; agencies should confirm current government-wide directives and their own obligations rather than generalizing from that page.
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What should the first implementation review produce?
Before enabling AI in a live case workflow, the agency should be able to show a coherent account of the proposed use: its purpose and limits, the people accountable for it, how staff retain meaningful authority, how risks will be assessed and managed, and which AI-related materials may need records treatment. It should also identify the specific agency processes that will resolve security, privacy, procurement, and records requirements. If those answers depend on the jurisdiction, agency, information type, or system configuration, resolve that scope before treating general federal guidance as a compliance decision.
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