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The safer alternative to a public chatbot is not automatically any particular open-source model. For government work, safety depends on whether the agency has approved the exact tool and data use, where prompts and files are processed, how the system is secured and maintained, and how people review its answers. A model that runs on an agency-controlled server can reduce some data-flow risks, but it also makes the agency responsible for patching, access control, logging, monitoring, quality assurance, and incident response.
Use this guide to decide when a locally hosted or privately operated system is appropriate, what information staff may enter, how major government policies differ, and which reviews must happen before deployment. Treat open-source and local hosting as design choices—not as proof of compliance, confidentiality, accuracy, or authorization.
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Can I use a public AI chatbot for government work?
Only when your agency has specifically approved that service, configuration, and category of information. In the absence of approval, keep personal, confidential, classified, health, sensitive, and other protected government information out of publicly available generative-AI tools.
Public tools can process prompts on infrastructure outside your agency’s control. Depending on the service and settings, prompts may be retained, reviewed, used to improve a system, exposed through an account compromise, or become subject to another jurisdiction’s access and disclosure rules. Removing names is not enough: combinations of dates, locations, case details, or rare events can still identify people or reveal protected information.
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Official guidance is consistent on the basic precaution, although each jurisdiction sets its own categories and approval process:
- North Carolina’s Department of Information Technology says not to enter personally identifiable or confidential information into publicly available generative AI. It also says code should not be entered without prior written approval, and that staff must independently review and fact-check generated content.
- For Canadian federal institutions, the Treasury Board guide says public servants must not input personal information into publicly available online tools. It recommends consulting privacy officials about whether a Privacy Impact Assessment is needed and completing a formal risk assessment and acceptance when additional mitigation may be required.
- Australian federal staff guidance says not to put personal information or information classified as OFFICIAL: Sensitive or higher into public generative-AI tools. It tells staff to assume inputs could become public, check outputs for fairness, accuracy, and bias, and never let generative AI make final government decisions.
- New South Wales guidance dated 2023 tells staff to avoid personal, official, sensitive, classified, or health information in public tools, fact-check outputs, disable training, logging, and chat history where those controls are available, and follow the entity’s policy.
- The US Department of Veterans Affairs requires sensitive data to be used only with tools that have a VA Authority to Operate for that purpose. That rule and its tool list apply to the VA environment; they are not a government-wide endorsement.
These are examples of government policy, not a substitute for your own agency’s current rules. A tool approved for one department, data class, or network may be prohibited for another.
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Open-source generally describes software whose source code is available under a license that permits specified forms of inspection, modification, and redistribution. In AI, people also use the term for model weights, training code, data, or a combination of those components. Those permissions differ, so read the license for every component rather than treating the label as a security certification.
Local or privately hosted describes where inference runs. A model may run on a workstation, an agency data center, a government cloud subscription, or a contractor-operated environment isolated from a public service. The hosting location changes who controls network routes, storage, identity, and operations; it does not by itself establish that those controls are correctly configured.
A local deployment can keep prompts inside a controlled network and make it possible to set retention and access rules that a public service does not offer. It also transfers obligations to the agency: acquiring suitable hardware, installing and patching dependencies, restricting administrative access, monitoring for abuse, testing updates, handling vulnerabilities, and supporting users when the system fails.
The UK Government’s Artificial Intelligence Playbook notes that many open-source models can run locally on a single machine, potentially in isolation. It also says this approach is not recommended for most production services and may be suitable for ad hoc or one-off work when model performance is not paramount. That is a government playbook judgment, not a universal technical law; workload, threat model, staffing, and service requirements determine whether it fits.
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Are open-source AI models safer for government data?
They can reduce exposure to an external provider when the agency controls the complete deployment, but open-source status alone does not make a system safe, compliant, accurate, or approved. Compare the entire service—model, runtime, user interface, connectors, logs, identity system, storage, and support process—not just the model file.
What local control can improve
- Data-flow visibility: administrators can map where prompts, uploaded files, embeddings, and outputs travel and set network boundaries.
- Retention choices: the agency can define whether conversations are stored, for how long, and who can retrieve them, subject to records and legal obligations.
- Access management: internal identity, role-based permissions, and separate environments can limit who can submit or view sensitive material.
- Change control: the agency can approve model versions, extensions, and updates instead of accepting an automatic vendor change.
- Operational isolation: a system can be placed on a restricted network or dedicated machine when the workload and support model justify it.
What local control does not solve
- A misconfigured server can expose data to the internet or to users who do not need it.
- Open licenses do not guarantee secure code, trustworthy training data, prompt-injection resistance, or timely vulnerability fixes.
- A model can produce fabricated citations, biased recommendations, unsafe code, or incorrect summaries even when no data leaves the agency.
- Local inference may still call external services through plugins, telemetry, update checks, document connectors, or authentication systems.
- The agency remains responsible for procurement, accessibility, records management, privacy, security accreditation, and human accountability.
What can I safely put into an AI chatbot at work?
Start with the agency’s data classification and authorization, not with the chatbot’s marketing label. If the policy does not clearly authorize a category for that service, do not submit it; ask the privacy, security, records, or information-governance contact for a decision.
| Information or task | Practical default | Review needed before use |
|---|---|---|
| Public information and low-stakes drafting | Often the lowest-risk starting point, provided the agency permits the service and no hidden personal or confidential details are included. | Confirm approved account, retention, and acceptable-use settings; check every output before publication. |
| Internal operational material | Use only in a service explicitly approved for that classification and purpose. | Verify hosting, access, retention, logging, and records requirements with security and privacy officials. |
| Personal, confidential, health, classified, or other protected information | Do not enter it into a public tool. | Use only an agency-authorized environment whose approval explicitly covers the data and task. |
| Source code or scripts | Do not submit code to a public tool unless written agency approval permits it. | Check intellectual-property, vulnerability, secrets-management, and repository policies; scan generated code before use. |
| Advice, eligibility, enforcement, or other high-impact decisions | Do not let the model make the final decision. | Define human review, explainability, appeal, bias testing, and audit requirements before deployment. |
Never paste credentials, access tokens, private keys, unredacted case files, or information that a policy labels sensitive. A sanitized sample can still be identifying or confidential when several harmless-looking fields are combined.
How should an agency compare an open-source tool with a public chatbot?
Use a service-level comparison rather than a simple open-source-versus-closed-source argument. The following questions expose the differences that matter operationally.
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|---|---|---|
| Data authorization | Which classifications and purposes are permitted? Who approved them? Are contractors and temporary staff covered? | Signed approval, policy reference, system boundary, and user terms. |
| Hosting and data handling | Where are prompts, files, logs, backups, and embeddings processed and stored? Who can access them? Are they used for training or service improvement? | Architecture diagram, retention schedule, provider terms, and access list. |
| Security and operations | Can the agency patch, isolate, monitor, and support the deployment? What happens when a model, library, or connector is compromised? | Threat model, patch procedure, monitoring plan, incident runbook, and support ownership. |
| Quality and oversight | How are factual errors, bias, unsafe instructions, and prompt-injection attacks detected? Who corrects an answer? | Evaluation set, review workflow, escalation contacts, and change records. |
| Purpose and audience | Is it staff drafting, sensitive internal analysis, or a public-facing service? Could an answer affect rights, benefits, safety, or government advice? | Use-case statement, impact assessment, human-review design, and accessibility plan. |
| Governance and procurement | Have privacy, security, legal, records, accessibility, procurement, and program owners participated as appropriate? | Meeting decisions, risk acceptance, contract terms, and operating policy. |
What approval steps should happen before deployment?
A short experiment with public material may need less process than a production service connected to case systems, but every deployment should have a named owner and a documented data boundary.
- Define the use case. State who will use the system, what task it performs, what decisions remain human, and whether the output is internal or public.
- Classify the inputs. List every planned source, including uploaded documents, retrieved records, prompts, telemetry, and feedback. Mark personal, confidential, health, classified, and legally restricted content.
- Check agency policy and authorization. Identify the current policy, approved services, network requirements, and any Authority to Operate or equivalent authorization. Do not infer approval from another agency’s list.
- Map the data flow. Document the model host, application server, storage, backups, logs, plugins, update channels, identity provider, and support personnel. Confirm whether any component sends data outside the approved boundary.
- Consult privacy and security officials. Ask whether a Privacy Impact Assessment, security assessment, records review, accessibility review, legal review, or formal risk acceptance is required. Canada’s federal guidance specifically recommends privacy consultation and formal risk assessment where mitigation is needed.
- Choose the deployment pattern. A local single-machine pilot, an isolated internal service, a government-cloud environment, and a public-facing chatbot have different controls and support costs. Select the least complex pattern that meets the use case.
- Test with representative but non-sensitive data. Measure factual accuracy, refusal behavior, bias, prompt-injection resistance, latency, accessibility, and failure recovery. Keep test data free of live personal or classified information unless the environment is authorized.
- Set human review and records rules. Define who checks outputs, what must be cited or verified, which conversations are records, how corrections are logged, and when a user must escalate to a subject-matter expert.
- Approve a bounded launch. Start with named users, limited functions, rate limits, and monitoring. Record the model and software versions so a later update can be evaluated rather than silently accepted.
- Reassess after changes. Revisit authorization when the model, hosting location, connector, audience, data class, or decision impact changes. A tool approved for drafting may not be approved for a public service.
How do government rules differ by jurisdiction?
The principles overlap, but labels, approval documents, and required reviews are local. Use the table as a starting map, then verify the current policy that governs your agency.
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| Jurisdiction or organization | Key restrictions or expectations | Important qualification |
|---|---|---|
| North Carolina, United States | Do not enter personally identifiable or confidential information into publicly available generative AI; do not enter code without prior written approval; independently review and fact-check outputs; consult agency security or privacy contacts when concerned. | State guidance; it is not a rule for every US government agency. |
| Canadian federal institutions | Do not input personal information into publicly available online tools; consult privacy officials about a Privacy Impact Assessment; perform formal risk assessment and acceptance when further mitigation is needed; provide transparency for public interactions. | The federal guide highlights public chatbots as higher-risk uses. |
| United Kingdom | Many open-source models can run locally on one machine and may suit ad hoc or one-off work where performance is not paramount. | The Government AI Playbook does not recommend local open-source models for most production services. |
| Australian federal staff | Do not put personal information or OFFICIAL: Sensitive or higher classified information into public generative-AI tools; assume inputs could become public; check fairness, accuracy, and bias; AI must not make final government decisions. | Agency guidance should adapt access management to its risk profile and operational requirements. |
| US Department of Veterans Affairs | Sensitive data may be used only with tools that have a VA Authority to Operate for that purpose. | Approved and unapproved tool lists are specific to the VA environment and time-sensitive; do not transfer them to another agency. |
| New South Wales | The 2023 guidance says to avoid personal, official, sensitive, classified, or health information in public tools; fact-check outputs; disable training, logging, and chat history where available; and follow entity policy. | Because the document is dated 2023, verify current local rules before relying on it. |
Can I run an AI chatbot locally instead of using a public one?
Yes, if the agency can operate the complete system within its approved boundary and provide adequate support. A local pilot may be practical for a narrow, low-stakes task, while a production service serving thousands of employees or the public may require stronger infrastructure, redundancy, monitoring, accessibility, and formal authorization.
Questions to answer for a local pilot
- Does the machine have enough memory and processing capacity for the selected model and expected number of users?
- Can administrators apply security updates without breaking the application or model runtime?
- Is the device physically and logically protected, including backups and removable media?
- Are prompts, outputs, crash reports, and diagnostic logs stored, and can administrators delete or export them under records rules?
- Can the system operate without unapproved outbound connections, telemetry, plugins, or automatic downloads?
- Who provides help when the model produces a wrong answer or the service becomes unavailable?
Isolation is useful only when it is real and maintained. A local interface that silently sends prompts to a hosted API, syncs chats to a consumer account, or exposes an administrative port does not provide the same boundary as a deliberately isolated deployment.
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Why are public-facing government chatbots a separate risk category?
A public chatbot interacts with unknown users, may receive malicious prompts, and can turn an incorrect answer into apparent government advice. It also raises accessibility, transparency, language, records, moderation, and service-continuity obligations that do not arise in the same way for an employee’s private drafting assistant.
Canada’s federal guidance identifies public chatbots as higher risk and recommends transparency for public interactions. Before launch, publish the system’s purpose, limitations, escalation path, and contact for corrections; prevent it from presenting generated text as a final eligibility, enforcement, medical, or legal decision; and provide a non-AI route for people who cannot or do not wish to use it.
Keep the chatbot’s retrieval sources current and approved. A locally hosted model can still quote an outdated policy or invent a form. Route consequential questions to trained staff and log enough information to investigate harmful answers without collecting more personal information than the service needs.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should staff verify AI-generated government work?
Human review is a control, not a ceremonial sign-off. The reviewer should have the subject expertise and authority to reject the output, not merely correct spelling.
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- Check primary sources: compare legal, policy, program, and technical claims with the current authoritative document.
- Recalculate important results: independently verify figures, dates, eligibility logic, code behavior, and procedural steps.
- Look for omissions: ask what exception, affected group, language, or accessibility need the draft failed to mention.
- Inspect uncertainty: treat confident wording, invented citations, and unsupported explanations as warning signs.
- Protect personal information: remove unnecessary details from prompts and drafts before sharing them with another reviewer.
- Record the decision: retain the approved version and the responsible official’s review according to applicable records policy.
Australian guidance states, “Generative AI must not make final decisions on government advice, services or outputs.” That principle applies even when the model is internally hosted and the answer looks plausible.
What are the common failure modes and fixes?
A staff member pastes a case file into a public tool
Stop further use, preserve the relevant account and audit information according to incident procedures, notify the agency privacy or security contact, and follow breach-assessment requirements. Do not assume that deleting the chat proves the information was removed from every copy or log.
A local model begins sending telemetry outside the network
Disconnect or block the unauthorized route, identify the component responsible, and determine what data was transmitted. Rebuild from a reviewed package, document the change, and require approval before reconnecting optional telemetry or update services.
Users treat a draft as an official answer
Change the interface and training: label drafts, show the responsible team, require a human approval action, and route high-impact questions to staff. Restrict the model from directly publishing or changing official records until those controls are tested.
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Pin approved versions where feasible, maintain a test set, and evaluate every model or retrieval change before release. Keep a rollback copy and record which version produced each consequential output.
The model produces biased or unequal results
Pause the affected workflow, collect examples across relevant groups and languages, and involve policy, legal, privacy, and subject-matter reviewers. Do not deploy a workaround that hides the disparity without addressing the underlying data, prompt, retrieval, or model behavior.
Which deployment is appropriate for a particular use case?
Use the least risky pattern that still meets the need, then apply controls proportionate to the impact.
- Low-stakes internal drafting: an approved service with public or non-sensitive inputs may be sufficient. Require fact-checking and prohibit automatic publication.
- Sensitive internal analysis: use an environment whose authorization explicitly covers the data. Prefer restricted identity, documented retention, monitored access, and a defined human reviewer.
- One-off technical experiment: a local open-source model may fit when data is non-sensitive, performance requirements are modest, and an owner can maintain the machine. Keep the experiment outside production systems.
- Public information assistant: use curated sources, transparency notices, accessibility testing, escalation to staff, abuse monitoring, and a release process that treats every content change as a service change.
- High-impact decision support: obtain specialized privacy, legal, security, and program approvals before testing with representative data. The model may assist a qualified official, but it must not make the final decision.
No universal open-source tool list can answer these questions for every agency. The decisive evidence is the approved use case, data boundary, hosting design, operating capability, and human oversight documented by the responsible organization.
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Frequently Asked Questions
Does deleting a chatbot conversation remove the government-data risk?
Not necessarily. Copies may exist in application logs, backups, security monitoring, provider systems, or another user’s export, and deletion controls may not cover all of them. Treat deletion as a containment step, not proof that the information was never retained.
Can an agency fine-tune a local model on internal documents?
Only after confirming that the documents may be used for that purpose and that the training process, resulting weights, checkpoints, and evaluation data have the same or stricter protection. Establish version control, access limits, retention rules, and a way to remove or quarantine a problematic training set before starting.
What should a government contract require when a supplier hosts the model?
Require clear statements about processing locations, personnel access, retention, deletion, incident notification, subcontractors, model or software changes, audit evidence, and termination data return. Have procurement, legal, privacy, security, and records officials determine which clauses are mandatory for your jurisdiction.
How can a small agency start without buying a large AI platform?
Begin with a narrowly defined, low-stakes task using public or deliberately synthetic data and a named owner. Document the data flow, test output quality, obtain the required policy approval, and expand only after support, monitoring, and human-review responsibilities are workable.
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