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AI providers serving the UK should maintain a versioned evidence file that follows the system from design through decommissioning. At minimum, that file should show the business purpose and accepted risks, architecture and controls, training-data provenance, model and prompt versions, deployment approvals, monitoring and incidents, supplier dependencies, maintenance actions, and end-of-life decisions.
The UK Government’s AI Cyber Security Code of Practice, published on 31 January 2025, is voluntary baseline guidance rather than a standalone law. Binding duties can still apply under the Data Protection Act 2018, the Network and Information Systems Regulations 2018 (NIS), sector rules and contracts. Treat the Code as the common security baseline, then document which legal overlays apply to your role and service.
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What the UK AI Cyber Security Code covers
The Code is aimed at organisations that develop or deploy AI, including deep-neural-network and generative-AI systems. It also addresses system operators, data custodians and other supply-chain participants. A purely academic system that will not be deployed is outside the Code’s intended scope, but a research model moving into a product or operational service should enter the same documentation process before release.
Its lifecycle has five phases:
| Lifecycle phase | Documentation a provider should maintain |
|---|---|
| Secure design | Business purpose, intended users, operating context, threat assumptions, security requirements, risk decisions and planned mitigations |
| Secure development | Architecture, trust boundaries, dependencies, data and model provenance, security testing, approvals and configuration history |
| Secure deployment | Environment, identity and access decisions, deployment approval, monitoring plan, rollback capability and operator hand-off |
| Secure maintenance | Vulnerability handling, updates, prompt and configuration changes, incident records, review decisions and restoration tests |
| Secure end of life | Decommissioning criteria, deletion or retention decisions, credential and key revocation, supplier hand-offs and downstream notification |
Documentation should be usable by the people who operate the system after launch. Developers are expected to provide security-relevant information to downstream operators and data custodians, not retain it as an internal-only file.
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1. Record the purpose, requirements and risk decisions
Before building an AI system, create a scope statement that answers what problem the system solves, who will use it, where it will run and what decisions it may influence. Record the assumptions behind the threat model: for example, whether users are trusted, whether prompts can contain untrusted content, which interfaces are internet-facing and which suppliers control infrastructure or model components.
The risk assessment should identify AI-specific threats and the controls selected to address them. Depending on the system, that can include training-data poisoning, malicious or indirect prompt injection, model extraction, supply-chain compromise, insecure tool use, unauthorised access, data leakage, denial of service and unsafe updates. Keep the decision record showing which risks were accepted, transferred or mitigated, who approved that treatment and the date of approval.
“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.”
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Link each requirement to an owner and an acceptance test. A requirement such as “only authorised staff can change system prompts” should point to an access-control design, an approval record and a test or log demonstrating that the control works.
2. Keep a design record and security audit trail
The design file should let an independent reviewer reconstruct how the service is assembled. Include the model and application architecture, trust boundaries, data flows, external APIs, software libraries, infrastructure services, secrets-management approach and security controls. Identify where responsibility changes between your organisation and a hosted model, cloud provider, managed service or customer-controlled component.
Preserve evidence of design reviews, threat modelling, security testing, red-team exercises, remediation and release approval. The audit trail should show who made a material decision, when it was made, what evidence supported it and which version was approved. Keep links between the risk register, test results and the deployed release so a later incident investigator can distinguish a planned exception from an undocumented drift.
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3. Prove where training and feedback data came from
For training, fine-tuning, retrieval and human or operational feedback data, maintain a provenance manifest. Record the source, collection method, licence or other access basis where relevant, transformations, filtering, custodians, quality checks and known poisoning risks. Preserve enough detail to identify the affected data if a source is later found to be malicious or compromised.
When public web data is collected, the Code sets a minimum traceability requirement: record the source URL and the date and time the data was obtained. Store those fields with the dataset version, not only in a temporary crawler log. A suspected poisoning event can then be scoped to the exact collection window and dataset build.
“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.”
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For each dataset release, retain checksums or immutable identifiers, preprocessing code versions, inclusion and exclusion rules, labelling or feedback procedures and the approval that allowed the data into training. Document how you would quarantine, roll back or rebuild a model if poisoning is suspected.
4. Maintain model, prompt and configuration records
Keep a model card or equivalent record for every production model and materially different variant. It should identify the model and version, component provenance, intended scope, limitations, guardrails, retention time, suggested review frequency and potential failure modes.
“Developers should ensure that the document includes security-relevant information, such as 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.”
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Keep an audit log for system prompts, policy prompts, tool definitions, retrieval settings, safety filters, temperature or sampling settings and other configuration that can alter behaviour. A prompt change can create a new security exposure even when model weights remain unchanged, so record the old and new values, reason for change, reviewer, test evidence and release time.
Where model components are distributed, publish or exchange cryptographic hashes so operators can verify authenticity and detect substitution. Keep the hash with the dependency inventory and the release manifest, and define how a mismatch is investigated.
5. Document deployment, operations and incident evidence
Before production release, retain approval for the target environment, network placement, identity and access controls, secrets handling, logging, monitoring and rate limits. Record which roles can deploy, change prompts, access logs, export data or invoke high-impact tools. Include separation of duties where a single change could bypass review.
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- Monitoring for availability, abuse, anomalous prompts, unusual output patterns, data exfiltration and control failures
- Alert thresholds, triage owners, escalation paths and evidence-preservation steps
- Vulnerability reports, dependency updates, patch decisions and exceptions
- Release history, rollback or restoration procedures and the results of recovery tests
- Incident timelines, containment actions, notifications, root-cause analysis and lessons learned
Tie each operational record back to the risk register and maintenance plan. An auditor or incident responder should be able to see what changed, which risk it affected, who approved it and whether monitoring detected the change.
NCSC secure-AI guidance treats providers as responsible across design, deployment and operation and highlights documentation, supply-chain security and asset management. Maintain an inventory of models, datasets, prompts, software, documentation, logs and assessments; these assets can themselves be valuable to attackers.
6. Plan end of life and supplier hand-offs
Define the conditions that trigger retirement: a replacement model, unacceptable residual risk, unsupported dependency, contract expiry, loss of data rights or inability to maintain security controls. Record whether data, model artefacts, prompts and logs are deleted, retained for a defined period or transferred to another custodian, and state the legal or operational reason.
At decommissioning, document revocation of credentials, tokens, certificates and encryption keys; removal of network access; destruction or archival checks; supplier and dependency hand-offs; and notification to downstream operators and customers. Keep evidence that backups, replicas and cached artefacts were covered by the decision.
How statutory duties overlay the voluntary Code
Data Protection Act 2018
If you are a controller or processor, document risk-appropriate technical and organisational measures for personal data. The legislation points to controls that prevent unauthorised processing or interference, establish precise processing details, restore systems after interruption and prevent corruption when systems malfunction.
Cross-reference the AI file to access-control records, processing instructions, logging, resilience tests, backup and recovery evidence. The AI risk register should identify where personal data enters prompts, training, feedback, telemetry or support workflows and which party is responsible for each processing activity.
Network and Information Systems Regulations 2018
NIS can apply to relevant digital service providers, including online marketplaces, online search engines and cloud-computing services. The current text excludes micro and small enterprises from the relevant digital service provider definition. In-scope services must secure network and information systems against compromise of availability, authenticity, integrity or confidentiality.
Maintain a written service-scope determination, size assessment, risk assessments, proportionate security measures, incident records and communications with the regulator. An AI feature does not automatically make a service subject to NIS; the analysis depends on the service category, organisation size and applicable UK rules.
Cyber Security and Resilience Bill
Government materials state that the 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 current duty until enacted and commenced.
Keep a legislative watch and map proposed requirements separately from evidence of present compliance. Do not label a control “required by the Bill” until the final text and commencement arrangements establish that obligation.
| Overlay | When it matters | Evidence to retain |
|---|---|---|
| AI Cyber Security Code of Practice | Developed or deployed AI systems, excluding purely academic systems not intended for deployment | Lifecycle risk, design, provenance, model, operational and end-of-life records |
| Data Protection Act 2018 | Provider acts as controller or processor of personal data | Processing details, access control, logging, resilience, backup and recovery evidence |
| NIS Regulations 2018 | Relevant digital service provider, including specified online and cloud services, subject to the current scope and size rules | Scope determination, risk assessment, security measures, incidents and regulator communications |
| Cyber Security and Resilience Bill | Potential future expansion, including qualifying medium and large managed service providers | Separate legislative-impact assessment and implementation plan; not proof of a current enacted duty |
Build a practical UK AI security evidence pack
- Define scope and roles. Identify whether you are developer, system operator, data custodian, controller, processor, supplier or a combination. Record the service type, deployment regions, customers and dependencies.
- Create the asset inventory. List models, datasets, prompts, software, infrastructure, APIs, secrets, logs, documentation and assessments. Assign an owner and version or immutable identifier to each item.
- Open the risk register. Capture business requirements, threat assumptions, AI-security risks, mitigations, residual risk and approval decisions. Link each risk to a control and evidence location.
- Version provenance. Store dataset manifests with source, collection method, URL and acquisition date/time for public web data, transformations, custodians and poisoning checks.
- Approve the design. Preserve architecture diagrams, trust boundaries, dependency reviews, security requirements, test plans and sign-off.
- Record model and configuration state. Keep model cards, component hashes, limitations, guardrails, retention and review intervals, plus prompt and configuration change logs.
- Gate deployment. Retain environment, access-control, monitoring, rollback and release approvals. Confirm downstream operators received the security-relevant documentation.
- Operate and learn. Store monitoring alerts, abuse investigations, vulnerabilities, updates, incidents, notifications, recovery tests and lessons learned.
- Close the lifecycle. Document retirement criteria, deletion or retention, key revocation, supplier hand-offs and downstream notification. Archive the final evidence index.
Compare obligations by provider role and service
The same AI stack can create different documentation duties depending on who controls each activity. Use these four axes when assigning owners:
| Comparison axis | Questions to answer |
|---|---|
| Role | Are you developer, system operator, data custodian, controller, processor or supplier? Which party approves changes and handles incidents? |
| Service type and NIS status | Is the service an online marketplace, online search engine, cloud-computing service or another category, and does the current size threshold apply? |
| Lifecycle stage and freshness | Is the evidence for design, development, deployment, maintenance or retirement? When was it last reviewed after a model, prompt, dependency or threat change? |
| Data, sector and supply-chain exposure | Does the system process personal data, support a regulated sector, rely on external models or expose high-value tools and interfaces? |
Common documentation failures and corrections
A generic risk assessment with no decision trail
A list of threats is not enough if it does not show the business requirement, chosen mitigation, residual risk and approver. Add dated decisions and link them to tests and release versions.
Training-data records that omit acquisition time
A source name without the URL and date/time cannot reliably identify a poisoned collection window. Store those fields in every public-web dataset manifest.
Prompt changes treated as informal edits
Changing a system prompt, tool instruction or retrieval filter can alter attack surface and output controls. Use the same review and rollback discipline as a software release.
Supplier evidence kept outside the system file
Contracts and assurance reports should be indexed against the dependency inventory, with expiry dates and compensating controls for gaps. Record which supplier owns each control boundary.
Future proposals confused with current law
Keep the Bill’s impact analysis in a separate, clearly dated section. Update the legal mapping when enactment, commencement or regulator guidance changes the position.
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Set review triggers rather than relying only on an annual calendar. Reassess after a model or prompt change, new training or feedback data, dependency or supplier change, security incident, material threat intelligence, architecture change, new processing purpose or retirement decision. Record the review date, reviewer, changed assumptions and resulting action.
The Code, NCSC guidance, NIS scope and UK data-protection requirements can change. Check the latest official UK Government, NCSC and regulator material before relying on an older control mapping, especially for the Cyber Security and Resilience Bill, NIS secondary legislation, regulator guidance or a revised AI Code.
What a defensible file looks like
A defensible UK AI security file is not a single document. It is a versioned repository with a scope statement, asset and dependency inventory, risk register, requirements and threat model, data and model cards, provenance manifest, prompt and configuration changelog, security-test and red-team reports, deployment and access approvals, monitoring and incident log, maintenance and vulnerability records, supplier assurances, retention and deletion decisions, and an index linking every control to an owner and evidence.
This structure gives operators, customers, auditors and regulators a traceable account of what the AI system was intended to do, how it was secured, what changed, how failures were handled and how the service was finally withdrawn.
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