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There is no universal UK certificate that makes an AI tool secure or automatically compliant. A defensible decision combines the UK Government’s voluntary AI Cyber Security Code of Practice with the National Cyber Security Centre (NCSC) secure AI lifecycle guidance, then tests the particular tool, supplier, data, integrations and consequences of failure against your organisation’s legal, contractual, procurement and risk requirements.
Assess the whole service rather than a model name or a vendor security statement. Document what the system does, who is responsible, what information leaves your control, how the supplier tests and changes it, and what happens during an incident or at termination. The result should be an evidence-based decision with named owners, mitigations and a date for reassessment.
What “meets UK security requirements” actually means
For most buyers, the phrase covers several different tests that should not be confused:
- UK cyber-security baseline: the Government’s AI Cyber Security Code of Practice and NCSC guidance describe expected practices across design, development, deployment, maintenance and end of life.
- Legal duties: data-protection, confidentiality, sector regulation, employment, safety or other obligations may apply to your use. The cyber code does not replace them.
- Contractual and procurement rules: your customer contracts, public-sector terms, insurance conditions or internal policies may impose stricter controls or evidence requirements.
- Risk acceptance: even when a supplier follows good practice, your organisation must decide whether the remaining risk is acceptable for the proposed use.
The Code is voluntary. Its use of “shall” describes a requirement within that Code, not a universal statutory duty by itself. It applies to deployed AI systems and assigns responsibilities according to roles in the AI supply chain. Academic creation and testing are outside its scope when the system is not intended for deployment.
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The Government page describes submitting the Code and implementation guide to ETSI as the basis for future standards TS 104 223 and TR 104 128. That is a standards-development pathway, not evidence that those standards are already in force or that any tool has been certified.
A practical six-step assessment
Use this sequence for a new purchase, a major change to an existing service or a new use case. Keep the evidence with the decision record.
- Define the use and impact. Record the business problem, intended users, outputs, data types, connected systems, permissions, deployment arrangement and what could happen if the system is compromised, unavailable or wrong.
- Map responsibilities. Decide whether your organisation is acting as a System Operator, Developer, Data Custodian or more than one of these. Put the allocation in governance documents and the contract.
- Review the supplier and supply chain. Ask how the provider assesses models, data, software libraries, APIs and other components, and how it handles a component or supplier that fails your criteria.
- Confirm data boundaries. Establish what prompts, files, outputs, feedback and logs are retained, where they are processed, who can access them, whether they are used for training or reviewed by people, and how deletion works.
- Demand operational evidence. Request threat models, security testing, red-team results where appropriate, incident procedures, logging, monitoring, patching and communication of material model or prompt changes.
- Record and revisit the decision. Capture evidence, gaps, mitigations, owners and accepted residual risk. Set triggers for reassessment, including a new data category, integration, user group or significant model update.
1. Define the use case before judging the tool
Describe the intended function
Write a short system statement that a reviewer can test: what the AI receives, what it produces, who relies on the result and which actions remain subject to human approval. “General productivity assistant” is too vague. “Summarises internal support tickets and proposes replies for trained agents, without sending messages automatically” gives you boundaries to assess.
List every input, including prompts, uploaded documents, images, audio, telemetry, feedback and administrator data. List outputs and downstream destinations, such as a ticketing system, code repository, customer record or decision workflow. Include the deployment arrangement: supplier-hosted service, private environment, on-premises component, API call or a combination.
Model consequences, not just probability
NCSC recommends holistic threat modelling that considers effects on the system, users, organisation and wider society. Consider confidentiality, integrity, availability, safety, financial loss, discrimination, reputational damage and regulatory exposure. A low-probability prompt-injection route may still require strong controls if it could disclose a large personal-data set or trigger a privileged action.
Assess both malicious and accidental failure. Examples include an attacker manipulating retrieved content, a user uploading restricted information, a model producing a confident but false answer, an unavailable API interrupting a critical process, or an update changing output behaviour without operational testing.
2. Identify your role and accountability
The Code’s responsibilities depend on who creates or adapts the model and who embeds or deploys it. Using a third-party model does not automatically remove operator responsibilities.
| Role | Typical responsibility | Questions to settle |
|---|---|---|
| System Operator | Embeds, deploys or runs an AI system in a real process. | Who controls configuration, users, permissions, monitoring, incident response and the decision to keep operating? |
| Developer | Creates or adapts a model or AI system. | Who owns secure design, training or fine-tuning controls, evaluation, documentation and release decisions? |
| Data Custodian | Controls or supplies data used by the system. | What classification, quality, access, retention and lawful-use rules govern the data? |
One organisation can hold multiple roles. For example, a company may operate a supplier’s model, fine-tune it on internal data and control the records sent through an API. Assign an accountable owner for each control, even where the supplier performs the technical work. The contract should state which evidence the supplier must provide and which controls remain yours.
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3. Examine the supplier and supply chain
An AI service is a chain of models, training or retrieval data, software libraries, APIs, identity systems, hosting, monitoring tools and human support. A weakness in any link can affect your use.
Evidence to request
- An AI-specific threat model covering your proposed configuration and likely abuse cases.
- A component and dependency description: model names and versions, data sources or provenance information where available, APIs, libraries and hosting locations.
- Processes for vulnerability management, patching, dependency updates and handling a supplier or component that no longer meets your criteria.
- Security evaluation reports, limitations, known failure modes and the date or version to which each result applies.
- Incident-notification commitments, audit information, service records and named escalation routes.
- Change-management information for model replacements, prompt or policy changes, new features and infrastructure migrations.
Ask what evidence is independently reviewed, what is self-attested and what is specific to the service you will use. A general statement that a provider “takes security seriously” does not establish how adversarial inputs, data exposure or your integrations were tested.
Check provenance and versioning
Request enough version information to reproduce an assessment: model or endpoint identifier, release date, configuration, safety settings and connected components. Ask how the provider records training, fine-tuning or retrieval-data provenance and what it can disclose without exposing protected information. If the provider cannot identify which model served a material output, define how you will investigate incidents and compare behaviour over time.
4. Set data, access and location boundaries
Trace every data path
For each category of information, answer:
- Is it sent to the provider, a sub-processor, an external model API or a plug-in?
- Is it stored temporarily or retained in prompts, files, outputs, feedback, telemetry or logs?
- Where is it processed and stored, and can support personnel or partners access it?
- Is it used to train or improve a shared model, and can you opt out?
- How are records deleted, including backups, cached copies and account exports?
Do not infer answers from a product label such as “enterprise” or “private.” Obtain the applicable service terms, data-processing information and retention settings for the exact edition and configuration you will deploy.
Apply least privilege
Restrict the system to the data and actions needed for its stated function. Use separate identities for users, service accounts and administrators; limit API keys by scope and rotate them; isolate environments; and require approval before an AI output can change a production record, send an external message or execute code. Treat retrieved documents and model-generated instructions as untrusted input when they can influence tools or workflows.
Handle personal data separately
The Code notes that personal data, including pseudonymised data, may create data-protection obligations. Determine the applicable roles, lawful basis, purpose limitation, transparency, retention and international-transfer requirements, and consult current Information Commissioner’s Office guidance. A cyber-security assessment cannot substitute for a data-protection impact assessment or other legal review where those are required.
5. Test the system in its intended configuration
Ask for the supplier’s evaluation, but also test the workflow you will operate. A model may perform differently when connected to your documents, tools, permissions and prompts.
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Security scenarios to cover
- Prompt injection through user text, retrieved documents, web content or tool results.
- Attempts to extract another user’s prompts, files, system instructions or secrets.
- Unsafe or unauthorised tool calls, including privilege escalation and data exfiltration.
- Malicious files, oversized inputs, denial-of-service patterns and rate-limit bypasses.
- Hallucinated instructions, fabricated citations or unsafe recommendations in the target workflow.
- Failure of the model, API, identity provider, network or monitoring service.
Define expected behaviour for each scenario, record the configuration and keep test results with the version assessed. Where red teaming is appropriate, ask what scope, attack methods, remediation and retesting were used. A supplier’s generic penetration-test summary is not a substitute for AI-specific evaluation.
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Make human oversight real
Specify which outputs require review, what reviewers must verify and when the process must stop. “Human in the loop” is not a control if the reviewer cannot see the source material, model uncertainty, tool actions or relevant audit trail, or if workload makes rubber-stamping inevitable.
6. Verify operations, monitoring and change control
NCSC states: “Security must be a core requirement, not just in the development phase, but throughout the life cycle of the system.” Require operational evidence, not only design documents.
Monitoring and logs
Confirm that you can monitor inputs, outputs, tool calls, authentication events, policy violations, administrative changes and unusual usage. Decide which logs contain personal or confidential data, who may inspect them, how long they are retained and how they are protected from alteration. Monitoring should support detection and investigation without creating an uncontrolled second copy of sensitive information.
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Obtain notification timeframes, severity definitions, contact routes and cooperation commitments. Ask how the provider preserves evidence, contains a compromised model or account, restores service and informs you about affected data. Define your fallback process if the AI service is unavailable or produces unsafe results; critical work should not depend on an untested single path.
Updates and model changes
Require advance notice or a documented assessment process for material model, prompt-policy, infrastructure or sub-processor changes. Test changed behaviour in a staging environment where possible, review permissions and repeat high-impact security scenarios. NCSC guidance treats changes to data, models or prompts as security-relevant. Keep the ability to pin a version, delay an update or withdraw the integration when your risk assessment requires it.
Supplier question checklist
Send questions that match the supplier’s role and architecture. Request evidence or a precise explanation for each answer.
- What AI-specific threat model and risk assessment cover this service and our proposed use?
- Which model, data, API and software components are involved, and what provenance and versioning information can you provide?
- What information do you receive, store, review, share or use for training or service improvement?
- What retention, deletion, processing-location and access controls apply to prompts, files, outputs and logs?
- Which controls prevent unnecessary access to our systems and data, and how are identities, roles, credentials and integrations managed?
- What evaluation has been performed for prompt injection, adversarial inputs, data exposure and system failure?
- What known limitations and failure modes remain, and what mitigations are expected from us?
- How are incidents reported and handled, and what audit information can we obtain?
- How are security patches, sub-processor changes and material model or prompt updates communicated?
- How can we export or delete our data and records, and what happens at termination or end of life?
Use the answers as evidence to weigh, not proof by assertion. Record unanswered questions as gaps with an owner and deadline rather than silently treating them as low risk.
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| Dimension | What to compare | Evidence to retain |
|---|---|---|
| Data sensitivity and permitted use | Classifications accepted, training or improvement use, retention and deletion. | Contract terms, configuration and data-flow diagram. |
| Hosting and access boundaries | Processing locations, isolation, identity controls, administrative access and sub-processors. | Architecture description and access-control evidence. |
| Model and component supply chain | Provenance, versions, dependencies and response when a component fails your criteria. | Component inventory and change records. |
| AI-specific testing | Prompt injection, data leakage, unsafe tool use, failure modes and limitations. | Evaluation scope, results, remediation and retest dates. |
| Logging and incident response | Events captured, retention, investigation access, notification and recovery. | Incident plan, sample audit information and service commitments. |
| Updates and model changes | Notice, testing, version pinning, rollback and approval process. | Change policy and release communications. |
| Integration permissions | Least-privilege scopes, approval gates, secret handling and isolation. | Permission matrix and integration design. |
| Exit and end of life | Export, deletion, transition support, backup handling and shutdown procedure. | Exit plan and termination terms. |
Also consider data quality, unintended consequences and whole-of-life support and maintenance. A cheaper or more capable tool may be unsuitable if it cannot provide the evidence, control or exit arrangements required for your impact level.
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The final record should let an independent reviewer understand why the tool was accepted, restricted or rejected. Include:
- Use case, users, outputs, integrations, deployment model and impact assessment.
- Role allocation for System Operator, Developer and Data Custodian responsibilities.
- Data-flow, access and retention decisions, including personal-data analysis where relevant.
- Supplier evidence reviewed, version or configuration assessed and unanswered questions.
- Threat scenarios, test results, known limitations and required human oversight.
- Mitigations, accountable owners, due dates and controls that must exist before launch.
- Accepted residual risks, the person authorised to accept them and any use restrictions.
- Reassessment triggers and the next review date.
The Code’s provision 2.1 says: “As part of deciding whether to create an AI system, a System Operator and/or Developer shall conduct a thorough assessment that includes determining and documenting the business requirements and/or problem they are seeking to address, along with associated AI security risks and mitigation strategies.” Apply that discipline to procurement and deployment decisions as well as in-house development.
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Common mistakes and how to correct them
Looking for a single approval badge
Why it fails: there is no universal UK AI-security certificate that answers your use-case, data and integration questions.
Correction: use the voluntary Code and NCSC lifecycle guidance as a baseline, then complete a documented, risk-specific assessment.
Accepting a supplier’s broad security claim
Why it fails: a claim may cover infrastructure but omit model behaviour, prompt injection, training use, logs or your configured permissions.
Correction: request dated, service-specific evidence and test the deployed configuration.
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Why it fails: APIs, retrieval stores, plug-ins, credentials and workflow automation can create the most consequential attack paths.
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Correction: map and test the complete chain, including human review and fallback procedures.
Ignoring change after approval
Why it fails: model replacements, prompt-policy changes, new data and new integrations can alter the threat profile.
Correction: define reassessment triggers, monitor behaviour and require change notification or approval.
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Treating pseudonymisation as the end of privacy analysis
Why it fails: pseudonymised information can still be personal data and may remain subject to data-protection obligations.
Correction: involve the appropriate privacy and legal reviewers and apply current ICO guidance.
How much confidence can consultation figures provide?
The Department for Science, Innovation and Technology’s 2024 Call for Views reported that 80% of respondents endorsed the proposed intervention, with 83% to 90% support for individual principles in the Code. These figures describe consultation responses. They do not measure the security of an AI tool, prove that the Code is mandatory or replace an assessment of your service.
When specialist help is justified
Internal teams can often assess a low-impact assistant with clear data boundaries. Specialist AI-security or governance expertise becomes more valuable when the system handles sensitive or regulated information, makes or supports high-impact decisions, can invoke privileged tools, is fine-tuned on internal data, or must provide strong audit evidence to customers or regulators.
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