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There is no universal winner. For a government agency, the safer and more useful choice depends on the task, information classification, deployment architecture, operating capacity, contract, and exit plan. “Open-source” and “commercial” are not opposites: a commercial supplier may offer an open model, and an open model may be hosted and supported by a commercial provider.
Make the comparison precise before buying. Decide whether you are evaluating the model and its license, the hosting arrangement, or the service contract. Then compare data exposure, security assurance, capability, total lifecycle cost, staffing, legal rights, accountability, resilience, and the ability to leave. The UK Government AI Playbook states that “Neither open-source or closed-source AI models are inherently less secure than the other.” That is the useful starting point: assess the actual system and controls rather than relying on a label.
Define the choice before comparing it
Government teams often use “open-source AI” to mean several different things. A supplier might release source code, model weights, documentation, training-data information, or only an interface. These artifacts can have different licenses and may not all be available. “Commercial AI” can mean a public consumer chatbot, a private managed service, a hosted model deployed in a government tenant, or a consultancy-built application. Those arrangements expose data and allocate responsibility differently.
Write the procurement question in three layers:
- Model and artifacts: Which code, weights, datasets, evaluation materials, and documentation are available, and under what licenses?
- Deployment: Where does inference run, where are prompts and outputs stored, and which organization operates the infrastructure?
- Service contract: What are the supplier’s commitments for data use, security, support, changes, liability, audit, and exit?
An agency can therefore choose an open-weight model through a commercial managed service, or run a commercially licensed model on its own infrastructure. Comparing “open” and “commercial” without specifying these layers can produce a confident but meaningless answer.
What government guidance says about security
The current UK Government AI Playbook rejects an inherent security advantage for either open or closed models. Openness can make code, architecture, or weights available for inspection, which may help independent review and allow an agency to test or modify a deployment. Inspection is not a security proof: the agency still has to understand the threat model, verify the artifact it obtained, secure the surrounding application, and maintain the system.
Publicly exposed artifacts can also help an attacker study weaknesses or develop targeted attacks. A closed service may conceal implementation details while providing security documentation, monitoring, patching, and incident support, but those benefits exist only to the extent the supplier actually provides them and the contract makes them enforceable. A proprietary label is not evidence that a service is secure.
Running a model locally changes who operates the infrastructure and may reduce the need to send prompts to an external service. It does not remove model, application, identity, access-control, supply-chain, or operational risks. A local installation still needs hardened hosts, network segmentation, secrets management, logging, vulnerability response, model evaluation, user training, and a process for safely updating or rolling back components. This is an architectural consequence, not a blanket property of every open model.
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How data handling differs by deployment
The decisive question for sensitive information is not whether a model is called open or commercial. It is what data leaves the agency boundary, where it is processed, who can access it, how long it is retained, and whether it is used to train or improve a model. The UK Government AI Playbook specifically warns that public AI applications can limit control over inputs and outputs and that terms and privacy statements govern their use. Unpublished official information should not be entered into a public service unless it has been cleared or is covered by a specific arrangement.
Rank #2
| Deployment pattern | Typical data path | Controls and questions |
|---|---|---|
| Public interface | Users send prompts and files to a generally available service. | Assume the least control until the provider’s current terms, processing location, retention, logging, training use, access, and deletion commitments are verified. Do not treat a free or public interface as an approved environment for official information. |
| Private managed service | A supplier operates the model in a contracted tenant or private endpoint. | Check isolation, subprocessors, jurisdiction, administrator access, logging, retention, training restrictions, incident notification, audit rights, and export or deletion procedures. |
| Agency-hosted model | Inference runs on government-controlled infrastructure or a dedicated facility. | The agency assumes more responsibility for hardware, patching, model updates, identity, monitoring, abuse prevention, continuity, and specialist staff. Verify artifact provenance and license conditions. |
| Hybrid or retrieval system | A model accesses agency documents through a search or retrieval layer while generation may run locally or remotely. | Map every data flow, including embeddings, caches, logs, evaluation copies, and connectors. Apply least privilege and prevent retrieval from crossing classification or departmental boundaries. |
Using organizational data for fine-tuning or retrieval-augmented generation can improve relevance, but it increases the data-security burden. The Playbook states: “If you use your own data with an AI model, you immediately increase the data security risk and you need to apply additional security controls to stop data leakage and privacy violations.” Treat indexes, embeddings, prompt histories, evaluation sets, and backups as potentially sensitive copies of the source material.
Open-source and commercial options compared
The following table is a decision checklist, not a measured ranking. Actual results vary by model, architecture, contract, use case, and jurisdiction.
| Decision axis | Open model or self-hosted option | Commercial service option | What the agency must verify |
|---|---|---|---|
| Data control | Can permit local operation and direct deployment control, while shifting more infrastructure and model-operation work to the agency. | Controls vary widely; a managed private service can differ materially from a public consumer interface. | Classification, processing location, subprocessors, retention, logs, training reuse, access, deletion, and audit rights. |
| Security and inspectability | Released code, architecture, weights, or data information may be inspectable, depending on what is actually provided; inspection is not proof of safety. | May include security documentation, controls, monitoring, and support, subject to the supplier’s evidence and commitments. | Threat model, vulnerability disclosure, patch cadence, evaluation access, independent assessment, incident notice, and configuration. |
| Capability | Can allow adaptation and hosting choices, but quality, context limits, latency, and compute needs depend on the particular model and task. | May provide an integrated product, support, and upgrades, while feature availability and changes remain subject to provider terms. | Test representative agency tasks and failure modes with controlled data; do not infer performance from licensing category. |
| Cost and staffing | May avoid some per-use fees but requires compute, engineering, security, updates, support, and assurance capacity. No general cost advantage is established. | Can shift some operating work to a supplier but normally creates recurring charges and service dependency. No general cost advantage is established. | Total lifecycle cost: infrastructure, integration, assurance, personnel, training, migration, and exit. |
| Rights and accountability | Reuse is governed by the applicable licenses; rights in model artifacts, datasets, code, and generated content still require review. | Supplier terms may define use, ownership, indemnity, service levels, liability, and access to records. | IP provenance, permitted uses, generated-content terms, data rights, audit, liability, change, and termination clauses. |
| Resilience and exit | May improve portability in some deployments, but specialized hardware, staff, model files, and integrations can remain dependencies. | Supplier support may improve continuity, while service changes, outages, or termination can create dependency. | Fallbacks, manual processing, export and migration, version-change notice, incident response, and exit assistance. |
Is open-source AI more secure for government?
Not by default. Openness can support reproducible evaluation, code review, and local control, but it can also expose implementation details and leave an agency responsible for discovering and fixing weaknesses. Security depends on the complete system: model artifact, tokenizer and dependencies, serving stack, plugins, data connectors, identity controls, network, hardware, monitoring, and operating procedures.
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- Security teams can examine code or configuration instead of relying solely on a supplier’s description.
- An agency can run tests in its own environment and retain a known version for repeatable evaluation.
- Local deployment can reduce a particular external data transfer, if prompts, logs, telemetry, backups, and support access are also kept inside the approved boundary.
Where openness does not solve the problem
- A released model may not include complete training-data provenance, security fixes, or reproducible build instructions.
- Public weights can be modified or repackaged; verify hashes, provenance, signatures, and the exact source of every artifact.
- The agency must operate patching, vulnerability response, access control, abuse monitoring, capacity planning, and continuity.
- Model behavior can remain unsafe or inaccurate even when every component is visible.
Use a threat model and evidence, not a category label. Require a controlled evaluation against the agency’s abuse cases, privacy risks, reliability requirements, and human-oversight process.
Rank #3
Can government agencies use commercial AI with sensitive data?
They can in some circumstances, but “commercial” does not answer the data question. Approval depends on the information classification, the service architecture, the supplier’s terms, and the agency’s risk acceptance. A private managed service with contractual restrictions, regional processing, customer-controlled keys, limited retention, and audit rights is materially different from a public interface whose terms permit broad logging or reuse.
Before sending sensitive data, obtain written answers to these questions:
- In which countries and facilities are prompts, files, outputs, embeddings, backups, and support records processed?
- Which supplier personnel, subprocessors, administrators, and automated systems can access them?
- How long are content, metadata, logs, and abuse-detection records retained, and can the agency set or verify deletion?
- Are inputs or outputs used to train, fine-tune, evaluate, or improve any shared model?
- Can the provider prove tenant isolation and enforce agency identity, role, network, and key-management controls?
- What notice, cooperation, evidence preservation, and remedy apply after a security or privacy incident?
If any answer is unclear, treat the service as unapproved for that data class. Start with synthetic, anonymized, or already-public material while the assessment continues; do not assume that removing obvious names eliminates re-identification or confidentiality risk.
Capability, cost, and operating capacity
Capability must be tested on the real task
Licensing category does not predict accuracy, latency, multilingual performance, tool use, explainability, or resistance to prompt injection. Build a representative test set from the intended workflow, with sensitive fields removed or handled in an approved environment. Measure task success, unacceptable outputs, citation or record-linking errors, latency under expected load, accessibility, and the amount of human correction required. Include adversarial and out-of-distribution cases, not only polished demonstrations.
Total cost includes the work around the model
A self-hosted option may replace usage charges with infrastructure, power, storage, observability, security engineering, model evaluation, and on-call work. A managed service can shift some of those tasks to a provider while adding recurring fees, minimum commitments, integration charges, data-transfer costs, and migration risk. The available evidence does not establish a general price or total-cost winner, so calculate a lifecycle estimate for the actual workload.
Match responsibility to capacity
Choose self-hosting only when the agency can staff secure operations for the full service life, including weekends and incident periods. A supplier cannot remove the agency’s accountability for lawful use, human decisions, records management, or user training. Conversely, a managed service is not “hands-off”: the agency still needs an owner for configuration, access reviews, evaluations, vendor management, and release approval.
A procurement process that works for either model
- Define the public benefit and task. State the decision or service outcome, affected people, measurable success criteria, and why AI is appropriate. The UK AI procurement guidance recommends beginning with a specific public-benefit goal rather than buying a technology first.
- Assess the data. Inventory inputs, outputs, reference documents, telemetry, and derived artifacts. Assign information classifications, identify personal or legally protected information, and specify what may leave agency systems.
- Map the architecture. Draw every data flow and trust boundary for public, private, self-hosted, and hybrid designs. Identify model files, dependencies, connectors, indexes, logs, backups, support channels, and administrator paths.
- Run multidisciplinary assurance. Include the service owner, procurement, information assurance, security, privacy, legal, records management, accessibility, operational staff, and subject-matter experts. Test the human workflow, not only the API.
- Evaluate suppliers and artifacts. Request provenance, licenses, security assessments, vulnerability handling, update history, subcontractors, data-processing details, performance evidence, and continuity plans. Apply the same diligence to proprietary and open-source components.
- Pilot under controlled conditions. Use representative but approved data, defined user roles, rate limits, logging, human review, and stop conditions. Record failure modes, workarounds, and the cost of correction.
- Contract for operation, not just access. Specify data use, location, retention, deletion, audit, incident notification, service levels, support, training, version changes, liability, portability, and exit assistance before production access.
- Approve, monitor, and revisit. Set release gates, access reviews, drift and quality monitoring, incident exercises, periodic re-evaluation, and a process for suspending the system when controls or assumptions change.
Contract and governance provisions to insist on
- Purpose and data rights: Define permitted processing, prohibit unapproved training or secondary use, and state who owns or may reuse inputs, outputs, prompts, fine-tuning data, and evaluation records.
- Location and access: List processing regions and subprocessors; require advance notice of changes; restrict privileged access; and support agency identity, key, and network controls.
- Retention and deletion: Set retention periods for content, logs, backups, abuse records, and support copies. Require verifiable deletion or return at termination.
- Security evidence: Require current assessments, vulnerability disclosure, patch timelines, secure-development information, penetration-test summaries where appropriate, and cooperation with agency testing.
- Incidents: Set notification deadlines, investigation support, evidence preservation, communications responsibilities, remediation, and recovery obligations.
- Model and service changes: Define notice and approval for model replacement, weight or policy changes, new subprocessors, altered data use, degraded features, and material performance changes.
- Accountability and liability: Allocate responsibilities for unlawful outputs, privacy breaches, IP claims, service failure, records, accessibility, and human review. Avoid clauses that silently transfer all risk to the agency.
- Continuity and exit: Require export in usable formats, documentation, configuration and prompt portability where lawful, migration support, fallback operation, and a defined period of assistance after termination.
Supply-chain and jurisdiction checks
NIST AI 600-1 advises updating acquisition due diligence for intellectual-property, privacy, security, and third-party risks, and it explicitly covers both open-source and proprietary tools and providers. An approved-provider list, continuous monitoring, incident procedures, and tested fallbacks are practical controls rather than substitutes for a one-time questionnaire.
Do not transplant one jurisdiction’s rule into another. UK Procurement Policy Note 017 was published on 17 February 2025, updated from its 2023 issuance, and says its provisions apply from 24 February 2025 to UK central government departments, executive agencies, and non-departmental public bodies. It recommends proportionate due diligence on supplier AI use and safeguards against using confidential authority information to train AI, including clarity about service data such as meeting-record training. Confirm current applicability and later amendments for the specific body.
The cited NIST software-supply-chain guidance is US federal procurement guidance. Its scope distinguishes open-source software freely and directly obtained by an agency from open-source components bundled or integrated into purchased software. That is a scope rule for the cited US context, not a general exemption for every agency, country, or AI acquisition. Ask counsel and procurement officials which rules actually govern the purchase.
Use-case patterns and sensible defaults
Public information search and drafting
A managed service may be reasonable when the corpus is already public, prompts contain no restricted information, outputs are reviewed by staff, and the contract and logging arrangement are understood. Keep a human accountable for publication and prevent the tool from silently becoming a decision-maker.
Internal records assistance
Start with an approved private or agency-hosted design. Enforce document-level permissions in retrieval, segregate indexes by classification and role, and test whether prompts can cause the model to reveal material a user could not otherwise access. Retain source references so reviewers can verify generated summaries.
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Do not choose a model solely because it is cheaper or easier to integrate. Define the legal authority, human decision point, explanation and appeal process, monitoring for disparate error, records obligations, and a manual fallback before any pilot. If those controls cannot be demonstrated, do not automate the decision.
Classified or exceptionally sensitive work
Use the agency’s applicable classified-information rules and approved infrastructure. A local model can reduce one external transfer but does not by itself satisfy classification, supply-chain, personnel, physical-security, or emissions requirements. Obtain formal authorization from the responsible security and information-assurance authorities.
Common failure modes and recovery actions
- “Open means safe.” Replace the assumption with artifact provenance, threat modeling, testing, patch ownership, and operational controls.
- “Commercial means the vendor handles everything.” Read the service and data-processing terms; assign an agency owner for configuration, access, evaluation, and incidents.
- Uploading data before approval. Stop the workflow, preserve relevant logs, notify the privacy and security teams, assess exposure, and move testing to synthetic or cleared data.
- Ignoring hidden data copies. Inventory embeddings, caches, telemetry, backups, support tickets, and evaluation datasets, then apply the same retention and deletion rules as the source data.
- Testing only the happy path. Add prompt injection, data-exfiltration, hallucination, bias, outage, overload, accessibility, and rollback scenarios; define a stop condition.
- Accepting silent model changes. Require version records, change notices, regression tests, and approval before a new model or policy reaches production.
- No exit plan. Maintain a manual process, export essential records and configuration, rehearse provider outage, and budget migration work before signing.
A practical decision rule
Prefer an agency-hosted or tightly controlled deployment when data cannot leave the approved boundary, repeatable versioning is essential, and the agency has the people and infrastructure to operate it. Prefer a managed commercial service when supplier-operated controls and support materially reduce operational risk, the data terms meet the classification, and the contract provides audit, notification, change control, and exit rights. Consider a hybrid when the agency can keep sensitive retrieval and records inside its boundary while using an external model only for a separately approved, minimized payload.
In every case, make the decision reversible enough to manage failure: retain a non-AI procedure, document assumptions, monitor real use, and revisit the risk assessment when the model, data, supplier, law, or mission changes. The strongest procurement is not the one with the most open or most prestigious model; it is the one whose controls, evidence, staffing, contract, and fallback match the public task.
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