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The practical comparison is therefore three separate decisions: which model and artifacts can be inspected or reused, where the system runs and who operates it, and what the supplier contract promises. Evaluate all three rather than treating a license label as a security or value guarantee.
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Start by defining what you are comparing
“Open-source AI” can mean publicly available code, model weights, training data, documentation or a license that permits particular forms of reuse. Those elements are often released separately, and a model may be open in one respect while its training data, safety tooling or production service remains closed. “Commercial AI” can mean a public consumer interface, an API, a private managed environment, an on-premises installation or professional services around any of those options.
Before comparing suppliers, write down the exact arrangement under consideration:
- Model and artifacts: Which weights, code, datasets, evaluation results and documentation are available, and under what licenses?
- Hosting and operation: Will the agency run the system itself, use a supplier’s dedicated environment or send prompts to a public service?
- Service contract: Who controls data use, updates, logging, support, liability, audit and termination?
This separation prevents a common error: assuming that a self-hosted model is automatically open-source, or that a paid service automatically provides strong confidentiality. The UK Government AI Playbook states that neither open-source nor closed-source models are inherently less secure. The relevant evidence is the architecture and controls for the particular deployment.
Is open-source AI more secure for government?
Not by definition. The UK Government AI Playbook says, “Neither open-source or closed-source AI models are inherently less secure than the other.” Security depends on the threat model, the components exposed, the way the application is configured and the organization’s ability to operate it safely.
What inspection can and cannot tell you
Public code, model weights or documentation can make review easier. Agency specialists may inspect dependencies, reproduce evaluations, test for unwanted behavior and look for weaknesses without waiting for a supplier to disclose details. That visibility can support independent assurance and make it easier to adapt a system to local requirements.
Inspection is not a proof of safety. An agency still needs qualified reviewers, time, testing infrastructure and a process for acting on findings. Publicly exposed artifacts can also help an attacker study the system, search for implementation weaknesses or develop targeted attacks. A component may be called open while important parts of the stack remain unavailable for inspection.
What a self-hosted deployment changes
Running a model in an agency-controlled environment can reduce the number of parties that receive prompts and outputs. It may allow network isolation, local retention policies and direct control of identity and access management. Those are deployment benefits, not automatic properties of every open model.
Self-hosting also transfers responsibility to the agency. It must secure servers and accelerators, maintain operating systems and dependencies, restrict administrator access, patch vulnerabilities, monitor misuse, evaluate model changes and respond to incidents. Model-level risks, insecure applications and excessive user permissions remain possible even when data never leaves the agency’s network.
Rank #2
Security questions for either option
- What threats are in scope: data exfiltration, prompt injection, model theft, abuse of privileged tools, denial of service or harmful outputs?
- Which code, weights, datasets and third-party libraries are present, and how are their vulnerabilities disclosed and fixed?
- Can the agency test representative prompts and adversarial cases before approval and after updates?
- Who receives security reports, how quickly are incidents notified and what evidence is available from independent assessments?
- How are administrator, developer, service-account and end-user permissions separated?
Can government agencies use commercial AI with sensitive data?
Sometimes, but the word commercial does not answer the data-protection question. A public interface with consumer terms may handle information very differently from a contracted private service. The agency must verify the actual product, region, account type and contract before sending any official information.
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The UK Government AI Playbook advises organizations to examine data location and processing, future training use, retention, logging and access when connecting organizational data to an AI model. Ask for a data-flow diagram that shows every place prompts, retrieved documents, outputs, telemetry and backups go.
- Classification: Identify whether the proposed inputs are public, internal, confidential, personal, operationally sensitive or otherwise restricted.
- Processing location: Establish the countries and facilities where data is processed, cached and backed up, including subprocessors.
- Training and improvement: Confirm whether prompts, uploaded files or outputs can be used to train or improve a model, and obtain a binding restriction where required.
- Retention and deletion: Specify default and maximum retention, backup deletion, legal holds and how deletion is verified.
- Logging and access: Determine what is logged, who can view it, how long logs remain and whether support personnel can access content.
- Isolation: Verify tenant separation, encryption, key management and controls that prevent one customer’s data appearing in another customer’s context.
The Playbook cautions that unpublished official information should not be entered into public services unless it has been cleared or is covered by a specific arrangement. A private managed service may satisfy different requirements, but only its documented controls and contract establish that fact.
Using agency data for retrieval or fine-tuning
Retrieval-augmented generation and fine-tuning can tailor a general model to agency knowledge. They also create additional repositories, permissions and leakage paths. 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.”
For retrieval, review document indexing, chunking, access filters, search logs and whether a user can induce retrieval of material outside their authorization. For fine-tuning, control the training set, remove unnecessary personal information, record the model version and test whether sensitive examples can be reproduced. Neither technique removes the need for ordinary application security and records-management controls.
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Compare the options across the full lifecycle
The following checklist is a decision framework, not a measured ranking. Actual outcomes vary with the model, deployment architecture, contract, use case and jurisdiction.
Rank #3
| Decision axis | Open model or self-hosted option | Commercial service option | What the agency should verify |
|---|---|---|---|
| Data control | May permit local operation and direct deployment control, while the agency assumes more infrastructure and model-operation work. | Controls vary widely; a dedicated managed environment can differ materially from a public interface. | Classification, processing location, subprocessors, retention, logs, training reuse, access, deletion and audit rights. |
| Security and inspectability | Code, architecture, weights or training data may be inspectable, depending on what is actually released; inspection is not a complete security proof. | A provider may supply security documentation, controls and support, but the customer must validate the specific service and commitments. | Threat model, vulnerability disclosure, patch cadence, evaluation access, independent assessment, incident notice and configuration. |
| Capability | Can offer flexibility to adapt or host, but quality, latency and compute needs depend on the model and task. | Can provide a managed product and upgrades, while feature access and changes follow provider terms. | Test representative agency tasks with controlled data; do not infer quality from licensing category. |
| Cost and staffing | May avoid some per-use fees but requires infrastructure, engineering, security, updates and support capacity. | Can shift operating work to a supplier, with recurring charges and dependency on service terms. | Total lifecycle cost, including compute, integration, assurance, personnel, training, migration and exit. No general cost comparison figure is established. |
| Rights and accountability | Reuse may be allowed under an open license, but model, dataset and code rights still require review. | Supplier terms may define access, ownership, indemnity, service levels and liability. | IP provenance, permitted use, generated-content terms, data rights, audit, liability and change or termination clauses. |
| Resilience and exit | Some deployments support portability, but specialized hardware, staff and model artifacts can remain dependencies. | Supplier support may improve continuity, while service changes or termination can create dependency. | Fallbacks, manual processing, export and migration, version-change notice, incident response and exit assistance. |
A procurement process that reduces avoidable risk
Procurement should begin with the public problem, not a preferred model category. UK AI procurement guidance recommends a specific public-benefit goal, a data assessment, multidisciplinary review, governance and information assurance, integration planning, training and knowledge transfer, and deliberate allocation of risk and liability. That guidance was published on 8 June 2020, so use it as a framework and check newer rules for a live procurement.
- Define the outcome. State the service improvement, affected users, human decision-maker and measurable success criteria. Ask whether a non-AI system would solve the problem with less risk.
- Map the data. List every input, output, document source and telemetry stream. Assign classifications and identify information that must remain inside agency systems.
- Choose an architecture. Compare agency-hosted, dedicated managed and public-service designs. Document trust boundaries, identities, network paths, storage, backups and human review.
- Run a representative evaluation. Use realistic tasks and edge cases, with approved or synthetic data where necessary. Measure accuracy, refusal behavior, latency, accessibility and failure consequences rather than relying on marketing claims.
- Review the supply chain. Inventory the model, datasets, code, libraries, hardware, hosting and support providers. Obtain provenance, license terms, vulnerability processes and update histories.
- Conduct multidisciplinary assurance. Include security, privacy, legal, procurement, records management, accessibility, service operations, subject-matter experts and affected stakeholders.
- Negotiate operational commitments. Put data use, location, retention, audit, incident notification, service levels, update notice, support, training and liability in enforceable terms.
- Approve a fallback and exit. Define how staff continue manually or move to another system if the model is unavailable, unsafe, materially changed or no longer affordable.
- Monitor after launch. Track quality drift, harmful outputs, access anomalies, data leakage, incidents, supplier changes and user feedback. Reassess approval when the model, data or use case changes.
Contract terms that matter more than the sales label
Data-use and confidentiality clauses
State precisely whether the supplier may use prompts, files, outputs, feedback or logs for training, analytics or human review. Cover subcontractors, support access, cross-border transfers, backups and deletion. Require notice and approval for material changes, and ensure confidentiality obligations survive termination.
Model and service change control
Generative systems can change behavior when a provider updates a model, safety layer, retrieval index or infrastructure. Require version identification, advance notice where feasible, testing windows, rollback or pinning options for critical services, and a process for re-approval after material changes.
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Specify the records the agency can inspect: access logs, security assessments, subprocessors, vulnerability reports, evaluation results and deletion evidence. Set incident-notification deadlines, cooperation duties, evidence preservation and responsibilities for notifying affected people or regulators.
Rights, outputs and liability
Clarify who may use inputs and outputs, how third-party content is handled, what indemnities apply and where liability caps do or do not apply. Do not assume that an open license grants rights to every dataset or generated output, or that a commercial subscription transfers ownership without conditions.
Portability and termination
Define export formats for prompts, outputs, configurations, evaluation records, indexes and other agency data. Address migration assistance, continued read-only access, key destruction, deletion certification and the time allowed to transition to a fallback. A system that cannot be exited safely is a strategic dependency even if its current price is attractive.
Supply-chain due diligence applies to both categories
NIST AI 600-1 advises updating acquisition due diligence to cover intellectual property, privacy, security and third-party risks for both open-source and proprietary tools and providers. An approved-provider list, continuous monitoring, incident procedures and documented fallbacks can make those controls operational rather than merely contractual.
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Maintain an inventory of direct and transitive components, including model files, tokenizers, evaluation tools, plugins, vector stores, operating-system packages and hosted APIs. Record versions and hashes where practical, scan for known vulnerabilities, review maintainer activity and establish how a critical fix will be obtained when a project or supplier stops responding.
Jurisdiction matters. The NIST software supply-chain guidance cited here is US federal procurement guidance. It distinguishes open-source software obtained freely and directly by an agency from open-source software bundled or integrated into purchased software; the latter falls within its stated scope. Do not extend that rule automatically to another country, agency or AI acquisition.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.UK policy points agencies should check
UK PPN 017 was published on 17 February 2025, updated from its original 2023 issuance, and says its provisions apply from 24 February 2025 to central government departments, executive agencies and non-departmental public bodies. It recommends proportionate due diligence on supplier AI use, safeguards against using confidential authority information to train AI, and clear contract treatment of service data such as meeting-record training.
Confirm that your organization is within the notice’s scope and check for later amendments before relying on it. Local authorities, devolved administrations and overseas agencies may follow different rules. The UK Playbook and procurement guidance are useful reference points, not a substitute for the law, security policy and records requirements that govern the specific buyer.
Match the deployment pattern to the use case
Public, low-sensitivity assistance
For public information, drafting or search where no restricted material is submitted, a commercial service may reduce operational burden. The agency still needs approved accounts, content review, accessibility checks, logging rules and a prohibition on entering unapproved information.
Restricted internal knowledge work
A dedicated managed environment or agency-hosted model can limit data exposure, but retrieval permissions, identity integration, monitoring and human review become central. Start with a narrow corpus and low-consequence tasks, then expand only after testing leakage and incorrect answers.
High-consequence decisions
Benefits, enforcement, health, safety, employment and other high-impact uses require stronger justification and oversight. Keep a named human decision-maker, document limitations, test for disparate impacts and provide a route to challenge or correct an output. A model’s openness does not remove those obligations.
Mission-critical or offline operation
Local deployment may be useful where connectivity, sovereignty or continuity requirements dominate. Budget for redundant hardware, patching, specialist staff, model evaluation and recovery drills. A local system still needs controlled updates and a tested manual procedure.
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Common mistakes and how to correct them
- “Open means secure.” Replace the assumption with component-level inspection, adversarial testing and an operating-responsibility matrix.
- “Paid means confidential.” Verify product-specific data terms, location, retention, training use and support access before approval.
- “Self-hosted means risk-free.” Threat-model the application, identities, integrations, model artifacts and administrators, not just the network boundary.
- “A benchmark proves suitability.” Test representative agency tasks, failure costs, accessibility and update behavior under controlled conditions.
- “The license settles ownership.” Review separate rights for code, weights, datasets, prompts, outputs and third-party content.
- “The first deployment is the final one.” Require change notification, re-evaluation, rollback and an exit plan before production use.
- “One country’s procurement rule applies everywhere.” Identify the governing jurisdiction, agency policy and procurement threshold at the start.
What a defensible decision record contains
Keep a concise record that another team can audit six months later. It should identify the public benefit and rejected alternatives; the data classification and approved data flows; the model and component inventory; evaluation results and known failure modes; security, privacy and legal reviews; the selected architecture; contract protections; accountable owners; monitoring indicators; and fallback and exit procedures.
This record also makes future comparison easier. If a provider changes terms or an open component stops receiving maintenance, the agency can see which assumptions must be retested instead of treating the deployment as a black box.
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