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The safest choice is not the assistant with the best demo or the strongest general benchmark. It is the service, tenant, feature set and workflow that your agency has explicitly approved for the exact information and task involved. Start by classifying the data, then verify the assistant’s authorization boundary, deployment location, enabled features, access controls, retention and human-review process. A government-branded plan or certification is not blanket permission: approval can differ by agency, jurisdiction, tenant, model and feature.
This guide gives you a practical way to make that decision without exposing classified, personal or otherwise restricted information to an unsuitable public tool.
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Write down the task, the information it will process and the decision a person will make from the output. “Draft a public, non-sensitive briefing” is a different risk from “summarize case files containing personal health information” or “search a restricted code repository.” The same product can be acceptable for the first task and prohibited for the others.
Classify the information first
Use your agency’s classification and handling rules, not a vendor’s marketing terminology. Identify whether the material includes personal information, health information, controlled unclassified information (CUI), law-enforcement data, procurement-sensitive material, classified information or confidential third-party data. Record the highest classification in the proposed workflow, including information that may appear in uploads, retrieved documents, prompts, conversation history, logs and generated files.
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Rules vary materially by jurisdiction. UK Cabinet Office guidance says, “Never put sensitive information or personal data into these tools.” Australian Government staff guidance permits OFFICIAL information only when agency policy allows it, while prohibiting information classified OFFICIAL: Sensitive or above and personal information in public generative-AI tools. The Government of Canada’s guidance makes use conditional on the context and whether the tool is suitable for the information class; its answer to using generative AI for public communications is simply, “Use caution.”
Define the consequence of an error
Note whether an incorrect answer could affect benefits, an investigation, a safety decision, legal advice, a personnel action, a public statement or a security control. High-consequence uses need stronger source traceability, review and approval than low-risk drafting. The employee using the system remains accountable for official advice and decisions.
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What does an authorization boundary tell you?
An authorization applies to a named service in a defined deployment, for specified data and controls. It does not automatically cover every model, connector, tenant or optional feature offered under the same brand.
Check what the service actually processes
FedRAMP’s 2026 scope guidance distinguishes a public chatbot used only with public, non-sensitive information from services that search internal agency data or let an AI coding assistant access strictly controlled private code. The latter process agency information and fall within the kind of use that requires authorization analysis. Ask your security or authorizing official to confirm whether the proposed processing is inside the approved boundary.
- Record the exact product name, edition, cloud environment and tenant.
- Identify the authorization level or equivalent designation and the authorizing agency.
- Confirm that your agency accepts that authorization for your intended use; a federal authorization is not automatically accepted by every agency.
- List every enabled function: file upload, retrieval, connectors, web grounding, agents, plug-ins, model switching, code execution, API access, export and administrator analytics.
- Check whether each function is covered separately or requires additional review.
Do not treat a certification as universal permission
FedRAMP reported that its 2025 AI Prioritization Initiative ran from August 2025 through April 2026 and listed ChatGPT Enterprise and API Platform, Gemini for Government, and Perplexity Enterprise Pro for Government as receiving FedRAMP certification in early 2026. That dated program status does not establish that every feature, agency, tenant or classification is authorized. Verify the current authorization record and your agency’s acceptance before use.
How do government cloud environments differ?
Government environments can differ in isolation, residency, personnel access, compliance scope and available features. Compare the actual environment rather than relying on the word “government” in a product name.
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| Environment or offering | What provider documentation describes | What you still must verify |
|---|---|---|
| Microsoft GCC | Dedicated government environment with US data residency and government-aligned compliance. | Whether the specific AI service and features are authorized for your agency, data class and tenant. |
| Microsoft GCC High | More isolated environment intended for elevated compliance and sovereignty needs, including certain CUI contexts. | Exact workload eligibility, model and connector availability, authorization level and agency deployment approval. |
| Microsoft DoD | Restricted environment for Department of Defense workloads. | Whether your organization and mission qualify and whether the required assistant features are enabled in that environment. |
| Gemini for Government in an Assured Workloads folder | Google Cloud documentation requires deployment in an Assured Workloads folder configured for the applicable FedRAMP High or DoD IL4 regime. | The feature authorization shown for your regime; some IL4 features are marked for DISA review, and agency acceptance is still required. |
Microsoft notes that workloads are broadly consistent across its government clouds but that features may differ. Google’s feature table separates authorization for FedRAMP High and DoD IL4. Those differences are practical boundaries: a connector or model available in a commercial tenant may be absent, restricted or separately reviewed in a government tenant.
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Which data-handling controls should you compare?
Request a data-flow description for the exact configuration. A privacy statement for a consumer product is not enough to evaluate an agency deployment.
Storage, processing and retention
- Where prompts, uploaded files, embeddings, logs and outputs are processed and stored.
- Whether data can leave the approved geographic or sovereign boundary through support, telemetry, backup or subprocessors.
- How long conversation content, administrative logs and safety records are retained, and whether your agency can set shorter periods.
- Whether customer content is used to train or improve models, and what contractual or administrative control disables that use.
- How deletion, export, legal hold and records-management requests work.
People and systems with access
Ask which provider personnel, subcontractors, administrators and agency roles can view inputs or outputs. Confirm role-based access, privileged-access monitoring, separation of duties, multifactor authentication, audit logs and incident notification. The Australian privacy regulator recommends due diligence on intended use, data flows, who can access input and output data, privacy and security risks, safeguards and human oversight. It advises against entering personal, especially sensitive, information into publicly available generative-AI tools as a best practice.
Identity, administration and integration
Prefer an agency identity provider, centralized provisioning and rapid offboarding. Check whether administrators can restrict external sharing, disable public links, block unapproved connectors, enforce approved models and review access logs. Integrations with email, document stores, case-management systems and code repositories should be assessed as separate data paths; enabling a connector can materially change the authorization scope.
How should task performance and safeguards affect the choice?
Evaluate the assistant on the work it must perform, not on a generic model leaderboard. Ask for a controlled demonstration using representative, non-sensitive material.
Useful capabilities to test
- Source-linked answers or quotations that let a reviewer trace claims to an approved document.
- Reliable handling of your required languages, document formats, tables and scanned files.
- Configurable retrieval that limits results to authorized repositories and respects document permissions.
- Predictable refusal and escalation behavior when a request exceeds the user’s authority.
- Administrative controls for model selection, prompt logging, retention and export.
- Accessibility, uptime, support hours and records-management compatibility.
Human review is a control, not a courtesy
UK and Australian staff guidance warns that generative-AI output can be convincing yet inaccurate and calls for checks of accuracy, fairness and bias. Set a written review step: a qualified employee verifies factual claims against authoritative records, checks for omitted context and discriminatory patterns, confirms that the output is suitable for release, and records the approval where policy requires it. Do not allow an assistant to make an unreviewed eligibility, enforcement, employment, procurement or safety decision.
How do you choose an assistant step by step?
- Describe the use case. State the task, users, systems involved, expected volume, output destination and consequence of error. Separate drafting, summarization, search, coding, translation and decision support because their data flows differ.
- Classify every input and output. Include personal, health, CUI, classified, law-enforcement and third-party information. Mark whether the output itself becomes an official record or a new sensitive artifact.
- Apply agency policy. Check your approved-tools list, acceptable-use rules, privacy impact assessment requirements, records obligations and procurement constraints. If policy is silent, stop and obtain a security and privacy determination rather than assuming public data is the only concern.
- Shortlist only approved deployment types. Record the service, tenant, region, authorization level, authorizing body and agency acceptance. Exclude consumer or public instances when policy bars the information class.
- Map feature scope. Make a separate row for uploads, retrieval, connectors, web access, agents, code execution, model choice, API calls, logging and exports. Ask the provider or authorizing official for evidence that each row is covered.
- Review data flows and contracts. Confirm processing locations, retention, training use, subprocessors, administrator access, deletion, incident response and support access. Require written answers for unresolved items.
- Test with controlled material. Use synthetic or already-public examples that resemble the real workload. Measure source traceability, error modes, accessibility and whether permission boundaries are enforced; do not upload live sensitive records merely to test quality.
- Design the operating procedure. Specify who may use the assistant, what they may enter, which repositories can connect, how outputs are reviewed, how incidents are reported and when content is deleted or filed as a record.
- Obtain documented approval. Capture the decision, scope, controls, owner, expiration or review date and approved features. Train users on prohibited inputs and provide a reporting route for suspected exposure.
- Revalidate after changes. Recheck authorization and data flows when the provider adds a model, connector, region, agent capability, retention option or subcontractor, or when your agency changes classification rules.
What comparison scorecard can an agency use?
Score each candidate with evidence, not impressions. A simple “pass, conditional, fail” result is safer than averaging unlike controls into a single marketing-style number.
| Question | Pass evidence | Red flag |
|---|---|---|
| Permitted information | Policy explicitly allows the identified classification and data types. | Terms say only “business data” or leave personal and health information undefined. |
| Authorization boundary | Named service, tenant and feature are inside an accepted authorization. | Only a parent company or product family is certified. |
| Data location | Processing, backup and support locations match agency requirements. | Routing can change by region or is undisclosed. |
| Retention and training | Retention is configurable and customer content is excluded from model training under enforceable terms. | Deletion, legal hold or training-use controls are vague. |
| Access and audit | Agency identity, least privilege, logs and incident notifications are available. | Shared accounts, weak administrator controls or no exportable audit trail. |
| Feature boundaries | Connectors, agents, web grounding and uploads are individually approved. | Optional features silently expand data access. |
| Output safeguards | Source traceability, review workflow and bias/error checks are defined. | Users are encouraged to accept fluent answers without verification. |
| Operational fit | Accessibility, support, records handling and availability meet mission needs. | Required features exist only in an unapproved commercial environment. |
How do UK, Australian, Canadian and US rules illustrate the differences?
These examples are not interchangeable approvals; they show why geography and agency policy must be part of the selection.
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United Kingdom
UK civil-service guidance prohibits entering classified, sensitive, personal or non-public government intent into public generative-AI tools. A public tool that is acceptable for generic brainstorming therefore cannot be assumed acceptable for internal policy drafts or case material.
Australia
Australian staff guidance allows OFFICIAL information only subject to agency policy and prohibits OFFICIAL: Sensitive or above and personal information in public generative-AI tools. The privacy regulator additionally recommends checking data flows, access, safeguards and human oversight before deployment.
Canada
The Government of Canada frames use around context and tool suitability. Its “Use caution” answer for developing public communications reinforces that even apparently low-risk drafting needs review of the content, audience and service configuration.
United States federal agencies
Use current FedRAMP records and agency requirements. The Department of Veterans Affairs publishes an agency-specific table identifying tools approved for VA sensitive data, including PHI and PII, and distinguishes broadly available tools from those requiring gated access. VA’s list is an example of agency governance, not a general approval for another department.
What are the most common selection mistakes?
“It has a government edition, so all features are approved.”
Fix: Verify the exact tenant, model, connector and authorization record. Feature availability and authorization can differ between commercial, government, high-isolation and defense environments.
“We will remove names, so the data is safe.”
Fix: Treat combinations of dates, job titles, case facts, locations and unique events as potentially identifying. Follow agency de-identification rules and obtain privacy review; do not rely on casual redaction.
“The model cites sources, so no review is needed.”
Fix: Check that citations point to the correct, current and authorized documents and that the answer did not omit contrary evidence. A citation feature supports review; it does not transfer accountability to the software.
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“We can enable connectors later without another assessment.”
Fix: Treat every connector, agent and external action as a scope change. Reassess permissions, records, retention and authorization before activation.
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What should the approval record contain?
Store a short decision package that another reviewer can understand without repeating the evaluation.
- Mission, users, tasks and consequences of error.
- Information classifications and examples of prohibited inputs.
- Service name, edition, tenant, region, models and enabled features.
- Authorization reference, agency acceptance and any conditions.
- Data-flow diagram covering prompts, files, retrieval, logs, support and backups.
- Retention, deletion, training-use, subprocessor and incident-response terms.
- Identity, role, audit, accessibility and records-management controls.
- Human-review procedure, escalation path and output-release authority.
- Owner, review date, change triggers and user-training evidence.
If any item is unknown, mark the use conditional or pause it. An explicit “not approved for this data or feature” decision is safer than an informal assumption.
Frequently Asked Questions
Can I use a public AI chatbot for government information that is already online?
Possibly, but only when agency policy permits the service and the material is genuinely public. Remove hidden metadata, unpublished context and personal information, and remember that prompts and logs may create a new record or data flow.
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Fine-tuning or embedding documents creates additional storage, access and retention obligations. Treat the training corpus, evaluation data and resulting model as a separate approval scope, and verify deletion and administrator-access controls before proceeding.
How often should an approved AI assistant be reassessed?
Reassess whenever the provider changes models, connectors, regions, retention, subprocessors or agent capabilities, and whenever agency classification or authorization rules change. Set a periodic review date even if no change is announced.
What is the safest way to run a pilot?
Use synthetic or already-public material that mirrors the real task, restrict accounts and connectors, log prompts and outputs according to policy, and require a named reviewer for every result. Do not use live sensitive records merely to measure answer quality.
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