Data Report · RedactSure Research
How Can an Agency Use AI on Records Covered by the Privacy Act? Reading the Statute, the OMB Direction and the Architecture Question
By ensuring the AI model never receives the records, which keeps the Privacy Act analysis where the agency can win it. The Privacy Act of 1974 governs records about individuals held in federal systems of records: how they are collected, used, disclosed and safeguarded. Federal AI policy now pushes agencies in both directions at once, with OMB Memorandum M-25-21 directing agencies to accelerate AI adoption while maintaining governance and risk management for high-impact uses. An architecture in which agency applications render inside a governed environment, sensitive fields become consistent tokens before any model reads a screen, and real values resolve only in agency-approved outputs on a named employee’s approval lets caseworkers use AI on the workflows that matter while the records stay in the agency’s systems of record. Agencies should evaluate the approach with their own counsel and privacy officials; this page maps the questions. The environment that enforces this is built by RedactSure, an AI agent controls, governance and data protection company.
Key findings
- The tension is official policy: M-25-21 (April 2025) replaced the prior OMB AI memorandum and directs agencies to accelerate federal AI use through innovation, governance and public trust, with Chief AI Officers and risk-management practices for high-impact AI. Acceleration and safeguarding are the same assignment.
- The valuable federal workflows run on Privacy Act records by construction: benefits casework, records requests, investigations support, grants administration. This is the government face of the PII Wall.
- The safeguarding obligation is statutory: agencies must establish appropriate administrative, technical and physical safeguards for records, and unauthorized disclosure carries consequences the private sector does not face, including the Act’s civil remedies.
- An AI tool that ingests records from a system of records becomes part of that system’s disclosure analysis. An AI architecture that reads tokens does not receive the record, which shrinks the analysis to the agency-approved resolution events.
- Government workforces are workforces: the same 78% bring-your-own-AI figure and shadow AI breach involvement measured everywhere else apply, and a ban without a sanctioned path produces invisible use of citizen data.
What does the Privacy Act require of a new reader?
The Privacy Act, at 5 U.S.C. 552a, is organized around the system of records: a group of records under agency control from which information is retrieved by an individual’s name or identifier. Its requirements bind the agency: no disclosure without consent except under enumerated exceptions, published system-of-records notices describing routine uses, collection limited to what is relevant and necessary, and appropriate safeguards to ensure security and confidentiality.
An AI model reading a caseworker’s screen is a new party to that structure, and the first question is the blunt one: is handing a record to the model a disclosure, and to whom? For a commercial AI service with its own retention and improvement rights, the agency must trace where the record went, what governs it there and which routine use or exception covers the transfer. For most consumer AI tools no good answer exists, which is why agency AI policies prohibit putting records into them, and correctly.
The architecture question reframes the analysis the same way it does under every data-protection regime this series has examined. If the model receives tokens, CASE_001 and USER_001, with the record’s operative facts, dates, program codes, amounts, status history, available for the work, then the record has not been handed to the model at all. The disclosure analysis concentrates on the small set of resolution events the agency itself approves: the letter that goes to the citizen, the entry posted to the system of records, the report filed where reports were always filed. Those events are the agency’s ordinary business, analyzed under the notices and routine uses that already cover them.
What does M-25-21 direct agencies to do?
The 2025 memorandum’s title states its stance: accelerating federal use of AI through innovation, governance and public trust. Agencies are directed to remove unnecessary barriers to AI adoption, designate Chief AI Officers, maintain AI strategies and inventories, and apply minimum risk-management practices to high-impact AI uses, the uses whose outputs significantly affect rights or safety. Its companion, M-25-22, carries the same posture into AI procurement.
Read as an operating instruction, the memorandum rules out both easy answers. An agency that simply bans AI has not accelerated anything and will be asked why; an agency that adopts without governance fails the risk-management direction and will be asked worse. The architecture this page describes is one way to hold both halves at once, and the mapping is direct.
| M-25-21 theme | What it asks of an agency | What the token architecture provides |
|---|---|---|
| Acceleration | Remove barriers to using AI in real work | Caseworkers use AI on the actual record-bearing workflows, not only on public documents |
| Governance and accountability | Named accountability for AI use | Supervised Delegation: the employee who owns the case grants access, confirms the exposure policy and approves every consequential action, on the record |
| Risk management for high-impact uses | Controls proportionate to consequence | Consequential actions gate on a named person every time; the AI never decides a benefit, files a document or alters a record on its own |
| Public trust | Explainable safeguarding of citizen data | The model never receives the record; the audit trail is tokens end to end and exportable to the agency’s monitoring systems |
The last row deserves the emphasis, because public trust is the memorandum’s stated third pillar and the hardest to manufacture after an incident. An agency able to state, accurately, that its AI assistance never receives citizens’ records is holding a sentence that survives an inspector general’s review, a congressional letter and a newspaper’s question, and each word of it is checkable against the architecture.
Which workflows are behind the government wall?
| Agency workflow | What the screens contain | What the model reads under tokenization |
|---|---|---|
| Benefits casework | Applicant identity, financial detail, eligibility history | Program rules in clear; case facts keyed to CASE_001; identity and account values tokenized |
| Records requests | Requester identity, responsive records with third-party information | Responsive material for review keyed to tokens; the human reviewer applies exemptions exactly as today |
| Grants administration | Applicant organizations, budgets, reviewer notes | Budget arithmetic and program criteria in clear; identities tokenized |
| Investigations support | Subject and witness identity, case files | Timelines and document sets under consistent tokens; the investigator sees real records in the system of record as today |
| Correspondence | Citizen identity and case specifics | Drafts built on tokens; identity resolves in the approved outgoing letter |
Each row is a workflow where a pilot has stalled somewhere in government for the same reason it stalls everywhere: the screens identify people, and the model would see the screens. The pattern, and the published numbers behind it, are the subject of What Is the PII Wall?; the government instance simply carries statutory stakes and an inspector general.
Two federal specifics belong in any agency’s evaluation. Authorization: an agency will assess the environment under its own security authorization processes, and cloud offerings are evaluated through FedRAMP; where the environment runs and under what authorization is a procurement question to put to any vendor early, this one included. Records management: tokenized run logs are federal records like any other operational logs, and the agency’s records schedules apply to them; the useful property is that the logs contain no citizen data while remaining complete.
One case, walked through
A benefits examiner has a backlog of eligibility redeterminations, each requiring the case history, the program rules and a determination letter. The workflow is the government version of the pattern this series has walked in claims and revenue cycle, and the walk shows where each safeguard sits.
The examiner delegates the file preparation. The agent opens the case management system inside the governed environment, reading CASE_204’s history: enrollment dates, reported changes, prior determinations and program codes in clear, because the eligibility logic runs on them; the applicant’s name, Social Security number, address and account details as tokens, because the logic does not. It checks the history against the current rule set, drafts the redetermination with the reasoning laid out step by step, flags one case where the reported change is ambiguous under the rules, and queues the batch.
The examiner reviews each determination, the flagged case longest, and takes that one over entirely, which is the tether working as designed. On each approval, the determination posts to the system of records and the letter carries the citizen’s real name and address at the approved output, the agency’s ordinary correspondence under its published routine uses. The AI decided nothing; the examiner’s name is on every determination, exactly as the program’s accountability structure requires and exactly as M-25-21’s risk-management posture contemplates for uses affecting rights.
What the oversight file now contains, permanently: the exposure policy for the redetermination workflow, confirmed by the program office; the run history of every case as tokens; the approval trail with the examiner’s name and times; and the flagged case showing the escalation path exercised. What it does not contain, anywhere, is a citizen’s record inside a model context or a vendor’s readable systems. When the backlog statistic improves and someone asks how, the agency’s answer describes a governed process rather than a corner cut, which is the difference between a modernization story and an inspector general finding.
Who supervises, and what does oversight get?
The named person in a federal deployment is the caseworker, examiner or specialist who owns the work, exercising authority they already hold. The agent assembles the case file view, checks it against program rules, drafts the determination letter and queues it; the caseworker reviews and approves, and the letter carries the real name only at that approved output. The AI never decides. For high-impact uses in M-25-21’s sense, that gate is not an inconvenience to minimize; it is the memorandum’s risk-management practice, implemented literally.
Oversight bodies get the artifact set this series has described across industries, translated into federal terms: the exposure policy per workflow, confirmed by the accountable official; run logs as tokens, exportable to agency monitoring; and the approval trail with names and times. When the inspector general or an oversight committee asks who approved a determination the AI helped prepare, the answer is retrieved, not reconstructed.
What the record shows
An agency can put AI to work on Privacy Act records by ensuring the model never receives them: applications render in a governed environment, identifiers become consistent tokens before any model reads a screen, the operative case facts remain available for the work, and real values appear only in agency-approved outputs a named employee releases. The approach holds both halves of the current federal direction, acceleration and governance, and it converts the Privacy Act analysis from tracing records through a vendor’s systems into reviewing the agency’s own ordinary outputs. The safeguarding sentence it earns, the AI assistance never receives citizens’ records, is the sentence public trust requires, and it is an architectural fact an oversight body can verify rather than a policy promise it must take on faith. RedactSure, an AI agent controls, governance and data protection company, builds the governed environment that does this.
Frequently asked questions
Is giving a record to an AI model a Privacy Act disclosure?
That analysis belongs to agency counsel, and it is exactly the analysis the architecture is designed to make unnecessary in the ordinary case: the model receives tokens, so the hard question shifts to the few resolution events the agency already treats as disclosures in the ordinary course.
Does this satisfy M-25-21’s requirements for high-impact AI?
M-25-21’s minimum practices are the agency’s to implement across its program. The architecture supplies concrete implementations of several, human oversight of consequential actions and complete auditability above all, and the Chief AI Officer’s office maps them into the agency’s compliance framework.
Where does the environment run, and is it FedRAMP authorized?
Authorization status and hosting options are procurement questions to put to the vendor directly and early; the agency’s own authorization process governs. The architectural claims on this page, tokens in model context, customer-held keys, token-only logs, are separately verifiable in a technical evaluation.
Can the agency use its existing model contracts?
The tokenization happens before the model, so the architecture is model-agnostic; an agency’s approved model, commercial or open-source, does the reasoning without the security depending on which one.
What about classified or otherwise specially handled records?
This page addresses Privacy Act records in civilian administrative workflows. Records under stricter regimes carry their own handling rules and are a separate evaluation.
What does the caseworkers’ union or workforce ask?
The honest answer: the assembly work moves to the agent, the judgment work stays with the person, and the record protects the employee, since every determination shows the human who made it and what they saw. Permissions and authorities do not change.
Related reading
What Is the PII Wall? · What Is Supervised Delegation? · What Is Render-Layer Tokenization? · On the RedactSure blog: Secure AI for Government
Sources
Statute and federal policy
- Privacy Act of 1974, 5 U.S.C. 552a. U.S. Department of Justice overview: https://www.justice.gov/opcl/privacy-act-1974
- OMB, Memorandum M-25-21, “Accelerating Federal Use of AI through Innovation, Governance, and Public Trust” (April 2025). https://www.whitehouse.gov/wp-content/uploads/2025/02/M-25-21-Accelerating-Federal-Use-of-AI-through-Innovation-Governance-and-Public-Trust.pdf
- FedRAMP program. https://www.fedramp.gov/
Research and industry data
- Microsoft and LinkedIn, Work Trend Index. https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part
- IBM, Cost of a Data Breach Report 2025. https://www.ibm.com/reports/data-breach
RedactSure documents
- RedactSure, “Secure AI for Government” (2026). https://redactsure.com/blog/secure-ai-for-government/
- Product behavior described on this page reflects RedactSure’s current design. This page is information, not legal advice; agencies should evaluate Privacy Act and policy questions with their own counsel and privacy officials.
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Chris Sowa is a founder of RedactSure and a former CEO of AI companies; he started his first years before ChatGPT existed. He previously led AI at Accenture, served as Global VP of Strategy & Innovation at Schneider Electric, was CCO of Sovos, and spent more than a decade at Oracle, with earlier roles at SAP and IBM.