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AI providers operating in the UK should maintain a versioned evidence file that follows the system from design through decommissioning. At its core, that file records the business purpose, security risks and accepted decisions; architecture and dependencies; training-data provenance; model, prompt and configuration changes; testing and approvals; operational monitoring and incidents; supplier hand-offs; and end-of-life actions.
The UK Government’s AI Cyber Security Code of Practice, published on 31 January 2025, is voluntary baseline guidance rather than a standalone legal duty. Binding requirements can still apply through data-protection law, the Network and Information Systems Regulations 2018 (NIS), sector rules and contracts. The practical approach is to use the Code as a common control baseline, then map the evidence to the laws and regulators that apply to your role and service.
What “compliance” means for an AI provider in the UK
The Code covers five lifecycle phases: secure design, secure development, secure deployment, secure maintenance and secure end of life. It is intended for developers, system operators and other supply-chain participants building or deploying systems such as deep-neural-network and generative-AI models. Purely academic systems that will not be deployed are outside its intended scope.
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The documentation set to maintain
1. Purpose, requirements and risk decisions
Before development begins, create a signed scope and risk record containing:
- The business requirement or problem being addressed.
- Intended users, affected people and the operating context.
- System boundaries, environments and dependencies.
- AI-security threats and assumptions, including misuse and abuse cases.
- Mitigations, residual risk, owners and review dates.
- A decision log showing which risks were accepted, transferred or treated, and by whom.
The Code states: “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.” Keep the assessment even if the project is cancelled; it demonstrates how the creation decision was made.
2. Design record and security audit trail
Maintain a design record that an independent reviewer can understand without relying on undocumented staff knowledge. It should show:
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- Model, software and infrastructure dependencies, including versions and owners.
- Identity, access-control, secrets-management and network-segmentation decisions.
- Security requirements, design reviews, threat-model changes and approvals.
- Security tests, red-team exercises, defects, remediation and retesting.
- Information supplied to downstream operators and data custodians.
Keep an audit trail for data preparation, security auditing and incident response. Link each material design change to a ticket or decision record, the person approving it and the date it entered production.
3. Training, fine-tuning and feedback-data provenance
For every training, fine-tuning, retrieval or human-feedback dataset, record the source and how it entered the system. A useful provenance manifest includes:
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- Source owner, collection method and licence or other access basis where relevant.
- For public web material, the exact source URL and the date and time obtained.
- Dataset version, hashes or immutable object identifiers.
- Transformations, labelling, filtering, deduplication and exclusion rules.
- Custodian, permitted uses, retention period and deletion process.
- Known quality, bias, tampering and poisoning risks.
- Validation results and the people who approved inclusion.
The Code’s minimum requirement is explicit: “The documentation of training data should include at a minimum the source of the data, such as the URL of the scraped page, and the date/time the data was obtained.” Recording only a publisher name or a broad website address is not enough to trace a suspected poisoning event.
Preserve the original manifest separately from transformed data. If a poisoning alert arrives, you should be able to identify affected samples, the models trained on them, customers exposed to those models and the corrective action taken.
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Document each model and component in a model card or equivalent record. Include:
- Model identity, version, source and component provenance.
- Intended scope, prohibited uses, known limitations and expected users.
- Guardrails, filters, tool permissions and escalation paths.
- Retention time for inputs, outputs, telemetry and safety logs.
- Suggested review frequency and triggers for an out-of-cycle review.
- Potential failure modes, security assumptions and tested mitigations.
The Code says developers should ensure that documentation includes “the sources of training data (including fine-tuning data and human or other operational feedback), intended scope and limitations, guardrails, retention time, suggested review frequency and potential failure modes.”
Keep a change-controlled log of system prompts, developer messages, policy files, retrieval instructions, tool schemas, sampling settings and other configuration that can alter behaviour. Each entry should identify the old and new value, reason for change, reviewer, test evidence and deployment time. For distributed model components, publish or share cryptographic hashes so operators can verify authenticity and detect substitution.
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5. Deployment, operations and incident evidence
Operational records should allow an auditor to reconstruct what ran, where, under which permissions and with which safeguards. Retain:
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- Accounts, service identities, privileged roles and access reviews.
- Monitoring coverage, alert thresholds, abuse handling and anomaly investigations.
- Vulnerability disclosures, patch decisions, dependency updates and exceptions.
- Model, prompt, data and infrastructure update history.
- Rollback, restoration and disaster-recovery procedures, including test dates.
- Incident reports, notifications, evidence preservation and lessons learned.
Tie incidents and maintenance tasks back to the risk register and maintenance plan. A log entry that says “fixed” without the affected asset, change, test and approval does not provide a reliable audit trail.
6. End of life and supplier-chain records
Decommissioning is a security phase, not an administrative afterthought. Record:
- Criteria that triggered retirement, replacement or suspension.
- Which models, datasets, prompts, software, logs and documentation were removed, retained or archived.
- Deletion verification and any legal or contractual retention exception.
- Revocation of credentials, keys, tokens, certificates and network access.
- Supplier and dependency hand-offs, including who assumes support.
- Notifications to downstream operators, customers and data custodians.
Maintain an inventory of AI-related assets. NCSC guidance identifies models, datasets, prompts, software, documentation, logs and assessments as assets that can be valuable to attackers; an inventory helps you protect, monitor and retire them deliberately.
Assigning evidence to roles
| Role or exposure | Primary records to own | Questions to resolve |
|---|---|---|
| Developer | Requirements, threat model, architecture, provenance, model card, security tests and release approvals | What was built, from which components, with which limitations and tests? |
| System operator | Environment, access controls, monitoring, abuse response, vulnerability management, incidents and recovery tests | What is running now, who can change it and how is misuse detected? |
| Data custodian | Collection basis, lineage, transformations, quality controls, retention and deletion evidence | Can each dataset and its permitted use be traced? |
| Controller or processor | Processing description, technical and organisational measures, access logs, resilience, backup and recovery evidence | Which personal data is processed, for whom and under what instructions? |
| Supplier or integrator | Component inventory, assurance reports, contractual controls, notification duties and hand-off records | What can a supplier change, and how quickly will you be told about a compromise? |
Data Protection Act 2018 overlay
If the provider is a controller or processor, the AI file should cross-reference the organisation’s data-protection security evidence. The Data Protection Act 2018 framework expects risk-appropriate technical and organisational measures, including controls to prevent unauthorised processing or interference, establish precise processing details, restore systems after interruption and prevent corruption when systems malfunction.
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In practice, link the AI record to access-control reviews, authentication and segregation, audit logging, backup and restore tests, resilience plans, retention and deletion decisions, and breach-response procedures. Record the processing role for each data flow; a provider may be a processor for one customer deployment and a controller for another.
When NIS Regulations 2018 may apply
NIS is a separate, binding overlay for qualifying operators. Relevant digital service providers include online marketplaces, online search engines and cloud-computing services. The current text excludes micro and small enterprises from the relevant digital service provider definition, so record the organisation-size and service-scope analysis rather than assuming that every AI platform is covered.
For an in-scope service, preserve:
- A written determination of service type, size, territorial reach and regulator.
- Risk assessments covering availability, authenticity, integrity and confidentiality.
- Proportionate technical and organisational measures for network and information systems.
- Supplier and dependency assessments, vulnerability treatment and change control.
- Incident records, notification decisions, regulator communications and follow-up actions.
An AI provider can be in scope because of the surrounding cloud or digital service even when the model itself is not regulated as a separate category. Keep the NIS analysis current after acquisitions, major customer changes, new hosting arrangements or expansion into another service type.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Cyber Security and Resilience Bill: track it separately
Government materials state that the Cyber Security and Resilience Bill was introduced for first reading on 12 November 2025 and has progressed through second reading and committee stage. It proposes reforms to NIS and would bring qualifying medium and large managed service providers into scope. It is not a settled duty until enacted and commenced.
Maintain a legislative watch, but do not label proposed requirements as current compliance. Keep a separate mapping that identifies likely future evidence, responsible owners and implementation lead time. Update the mapping when the Bill, commencement orders, NIS secondary legislation or regulator guidance changes.
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Build a versioned evidence pack
Use a controlled repository with immutable or access-logged versions, named owners and review dates. A practical index is:
- Scope statement, role assignments and service-scope determinations.
- Asset and dependency inventory.
- Business requirements, threat model and risk register.
- Architecture, trust-boundary and security-control records.
- Data cards, model cards and provenance manifests.
- Prompt and configuration changelog.
- Security-test, red-team and remediation reports.
- Deployment, access and release approvals.
- Monitoring, abuse, vulnerability and incident logs.
- Supplier assurances, contracts and notification records.
- Retention, deletion, restoration and end-of-life decisions.
- Control-to-evidence index showing owner, status, last review and next review.
Use consistent identifiers so a reviewer can move from a risk to its mitigation, test result, production change and incident history. Restrict access to sensitive logs and datasets, but make the documentation available to downstream operators and data custodians when they need it to run the service safely.
A practical implementation sequence
- Classify the service. Identify every role, data flow, customer deployment, supplier and possible NIS category. Record why each classification was chosen.
- Inventory assets. List models, datasets, prompts, code, tools, environments, credentials, logs and documentation, with owners and locations.
- Set the baseline. Apply the five lifecycle phases of the AI Code and map each control to existing security, privacy, resilience and supplier processes.
- Capture provenance. Freeze a manifest for each dataset and component, including URL and acquisition time for public web data, versions, transformations and hashes.
- Approve the design. Complete the requirements, threat model, architecture review and residual-risk decision before production access is granted.
- Test before release. Record security testing, adversarial evaluation, guardrail checks, defect decisions and independent approval where appropriate.
- Operate with change control. Log model, prompt, data, dependency and infrastructure changes; connect alerts and incidents to the affected release.
- Review on triggers. Reassess after a serious incident, new vulnerability, material model or prompt change, new data source, supplier change, regulatory update or significant expansion of use.
- Close the lifecycle. When retiring a system, revoke access, preserve required evidence, delete or archive data as decided, and notify downstream parties.
Common documentation failures and fixes
- Undated screenshots instead of controlled records: store structured entries with timestamps, version IDs, approvers and links to the deployed artifact.
- “Open web” as the data source: retain exact URLs and acquisition times, then preserve filtering and transformation logs.
- One model card for many variants: create records for each materially different model, adapter, prompt or deployment configuration.
- Security testing detached from releases: identify the exact build, environment, test scope, findings and retest result.
- Risk register abandoned after launch: schedule reviews and connect incidents, vulnerabilities and updates to the relevant risk entry.
- Assuming the voluntary Code is the whole legal answer: complete separate data-protection, NIS, sector and contractual assessments.
- No retirement evidence: document deletion, key revocation, supplier hand-off and customer notification, not merely the shutdown date.
How to keep the record defensible
Evidence is strongest when it is contemporaneous, attributable and reproducible. Use named owners, controlled versions, approval timestamps, cryptographic hashes where authenticity matters, and retention rules that match legal and operational needs. Test restoration and rollback instead of describing them only in a policy. Review the Code, NCSC guidance, NIS scope, data-protection requirements and legislative status periodically because official guidance and commencement dates can change.
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The result should be more than a compliance folder: it should let an operator answer, quickly and precisely, what the AI system is, what it can affect, which data and components it uses, who changed it, which safeguards were tested, what happened during an incident and how the service was safely retired.
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