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Assess AI security as a continuing business-risk process, not a one-off checklist. Define the system and its purpose, map data and dependencies, model AI-specific and conventional cyber threats, assign controls and owners, record residual risk, then test and monitor the system throughout its life. UK organisations can align this work with the voluntary UK Government Code of Practice for the Cyber Security of AI, the National Cyber Security Centre (NCSC) Guidelines for Secure AI System Development, and existing cyber-risk governance.
What an AI security risk assessment must achieve
An assessment should let accountable leaders answer five practical questions:
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- What is the AI system intended to do, and where are its boundaries?
- What information, models, infrastructure, people and connected services does it depend on?
- How could an attacker, supplier failure, misuse or system error affect confidentiality, integrity, availability, operations and people?
- Which safeguards reduce those risks, who operates them, and what risk remains?
- How will the organisation detect, respond to and reassess changes or failures?
The NCSC states that “Security should be integrated into all AI projects and workflows in your organisation from inception.” That means the assessment starts while a use case is being considered, not after procurement or deployment.
Use the UK guidance in the right way
UK Government Code of Practice for the Cyber Security of AI
The UK Government Code of Practice for the Cyber Security of AI is voluntary. It is intended to help establish a global baseline through work with ETSI standards. It supports risk assessment, due diligence, threat analysis, human responsibility, asset protection and lifecycle security. It is not, on the evidence available here, a general statutory obligation. An organisation must still determine which data-protection, safety, contractual and sector rules apply to its own activities.
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The Code’s 2024 Call for Views recorded 80% endorsement of the proposed intervention, with support for individual principles ranging from 83% to 90%. Those figures describe consultation responses; they do not measure incident frequency, risk severity, implementation or control effectiveness.
NCSC secure-AI guidance
The NCSC Guidelines for Secure AI System Development were published on 27 November 2023, version 1.0. They cover secure design, development, deployment, and operation and maintenance. The NCSC’s manager-and-board guidance, AI and cyber security: what you need to know, is useful for questions about accountability, governance, critical assets, worst-case consequences, incident response, supply chains and skills.
For organisations responsible for essential functions, the NCSC Cyber Assessment Framework (CAF) Principle A2, Risk Management, is particularly relevant. It says: “There is no single blueprint for cyber security and therefore organisations need to take steps to determine security risks that could affect the operation of essential functions and take measures to appropriately manage those risks.” CAF is not presented as a universal requirement for every UK organisation.
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1. Define the purpose, system boundary and accountability
Write the business case before the threat model
Describe the problem the AI is meant to solve, the expected benefit and why AI is appropriate. State the intended users, decisions or actions supported, operating environment, success criteria and unacceptable outcomes. A vague objective such as “use a language model to improve productivity” is not a usable security scope; “draft internal service-desk replies from approved knowledge articles, with a human approving every response” is more precise.
Identify every organisational role
An organisation may be the developer, system operator, data custodian, user, or several of these at once. Name each role and assign a business risk owner before scoring. Also name technical owners for the model, data pipelines, integrations, identity and access management, infrastructure, logging, supplier relationships and incident response. Accountability must remain clear when a third party hosts the model.
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Set the boundary in writing
Record the model and version, prompts and system instructions, retrieval sources, training or fine-tuning data where known, user interfaces, APIs, libraries, hosting, users, downstream systems and suppliers. Include capabilities: what the system can read, change, send, execute or trigger. Document sensitivity and the consequences of losing confidentiality, integrity or availability.
| Boundary item | Questions to record |
|---|---|
| Purpose and use | What task is performed? Which uses are allowed, restricted or prohibited? |
| People and roles | Who operates, administers, reviews, supports and approves outputs? |
| Data | What data enters, leaves or is retained? Where is it processed and stored? |
| Model and prompts | Which model, version, system instructions, guardrails and update path are used? |
| Integrations | Which APIs, retrieval stores, software libraries and connected systems are involved? |
| Actions | Can the system alter records, send messages, make decisions or invoke tools? |
| Impact | What happens to people, operations, customers and connected systems if it fails? |
2. Map assets, data flows and dependencies
Make an inventory that is detailed enough to support access decisions and incident investigation. Include model weights or files, datasets, feedback and evaluation data, prompts, embeddings or retrieval indexes, logs, backups, secrets, software dependencies, model-serving infrastructure, APIs and connected business systems. Logs can contain sensitive prompts, personal information or details of unsafe capabilities, so protect them as assets rather than treating them as harmless diagnostics.
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Trace the flow of information
For each data flow, record its source, destination, purpose, location, retention, access path and protection. Mark whether data is sent to an external model or service. Identify who can read, modify or invoke each component. A useful diagram shows the user interface, identity provider, application, prompt and retrieval layer, model endpoint, tools, storage, monitoring and downstream actions.
Assess third-party models and services
Apply the organisation’s normal software and supplier-risk process, adding AI-specific questions. Ask providers to explain their security responsibilities, data handling and retention, system limits and failure modes, update practices, incident notification, access controls and available assurance evidence. Establish whether the provider can change the model or processing location without notice. Check an alternative service, a degraded mode or a recovery path when the dependency is important to an essential or business-critical function.
Do not treat a model or API name as proof of security. Assurance depends on the service configuration, contract, data flows, operational controls and evidence available for the particular deployment. Guidance supports due diligence but does not prescribe a universal questionnaire or guarantee that a provider will disclose every requested detail.
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3. Threat-model AI and conventional cyber risk together
For each scenario, describe the attacker or failure source, access path, vulnerability, affected assets and people, consequence, existing controls and required treatment. AI-specific threats often amplify ordinary weaknesses in identity, infrastructure, APIs and software supply chains.
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|---|---|---|
| Prompt injection and unsafe inputs | Malicious instructions in user prompts, documents or retrieved content; whether instructions can override boundaries or reach tools | Disclosure, unsafe output, unauthorised downstream action or loss of trust |
| Data poisoning or tampering | Who can alter training, fine-tuning, feedback, retrieval or operational data; integrity checks and approval paths | Changed behaviour, biased or unsafe results, compromised decisions |
| Privacy leakage | Whether queries, outputs, logs or model behaviour can reveal sensitive training or operational information | Confidentiality breach, privacy harm or regulatory exposure |
| Model extraction | Access to model interfaces or weights, query limits, monitoring and protection of proprietary functionality | Reconstructed functionality, intellectual-property loss or an easier path to attack |
| Supply-chain compromise | Provenance of model files, serialized weights, libraries, containers and build pipelines | Malware or arbitrary code execution, data theft or persistent compromise |
| Over-permissioned integration | Whether the system can access or change more data and systems than its task requires | A manipulated or erroneous output becomes a wider incident |
| Availability and behaviour change | Outage, throttling, drift, model replacement, degraded quality or dependency failure | Interrupted operations, unsafe decisions or cascading failures |
Handle untrusted model files as code
The NCSC warns that importing untrusted model weights can expose systems to arbitrary code execution. Scan and isolate third-party models or serialized weights before use, validate provenance where possible, restrict runtime permissions and keep them away from sensitive networks until checks are complete.
Consider people and connected systems
Impact analysis should cover confidentiality, integrity and availability, but also operational disruption, affected users, customers, staff and other systems. Ask what happens if a confident but wrong answer is accepted, if an automated action is repeated at scale, or if the model becomes unavailable at a critical time.
4. Score scenarios and record residual risk
Use the organisation’s established likelihood-and-impact method where one exists. The important requirement is consistency and traceability, not a particular scoring scale. Record the assumptions behind a rating, including exposure, attacker capability, dependency reliability, detectability and the number or sensitivity of affected assets.
Every scenario record should contain:
- Threat or failure scenario and assumptions.
- Affected assets, people, services and essential functions.
- Existing vulnerabilities and controls.
- Likelihood and impact method, score and rationale.
- Required treatment, implementation evidence and deadline.
- A named business owner and technical owner.
- Residual risk after treatment and the decision-maker who accepts it.
- Review date and triggers for earlier reassessment.
Choose whether to reduce, transfer, accept, avoid or change the use case. Residual risk remains after controls; acceptance must therefore be explicit, assigned and revisited rather than implied by deployment.
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5. Design controls into the system
Limit access and authority
- Apply least privilege and secure defaults to models, APIs, datasets, pipelines, consoles and connected systems.
- Separate development, testing and production environments.
- Use distinct identities for users, services and administrators, with strong authentication and reviewable permissions.
- Restrict the data that can be retrieved and the actions that tools can perform.
- Require confirmation or independent approval for high-impact actions.
Protect inputs, outputs and external data
- Define what sensitive information may be sent to external services and block or redact prohibited data.
- Validate and constrain inputs, retrieved content, tool parameters and model outputs.
- Use allow-lists, schemas, rate limits and transaction bounds where the system can invoke actions.
- Treat external documents and user content as untrusted instructions, not policy.
Secure the lifecycle and evidence
- Document model, data and prompt sources, versions, limitations, guardrails, retention and known failure modes.
- Scan and isolate third-party components, preserve provenance and protect build and deployment pipelines.
- Protect logs, backups, secrets and configuration; retain a known-good state for recovery.
- Communicate limitations and user responsibilities in the interface and operating procedures.
Make human oversight meaningful
Human review is a control only when a responsible person can understand and assess the output, has enough time and authority to intervene, and receives the information needed to spot uncertainty or failure. Define when review is mandatory, what evidence the reviewer sees, how disagreement is handled and how overrides are logged. Do not use “a human in the loop” as a substitute for technical restrictions when the reviewer cannot realistically detect a failure.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Test before release and after material change
Test security before production deployment using evaluations appropriate to the system, and use red teaming where the risk warrants it. Test both the model and the surrounding application: authentication, authorisation, retrieval controls, prompt boundaries, tool permissions, logging, rate limits, failure handling and recovery. Include malicious prompts, poisoned or misleading content, sensitive-data requests, malformed inputs, dependency outages and attempts to trigger unauthorised actions.
Record test scope, environment, data, findings, severity, remediation, retest results and any accepted residual risk. A test that produces no evidence of what was attempted cannot demonstrate that a control works.
7. Operate, monitor and respond
Monitor for security-relevant change
Monitor inputs and outputs in a way that supports detection and investigation while respecting privacy and data-protection requirements. Look for unusual or adversarial inputs, access anomalies, sudden or gradual behaviour changes, data drift, unexpected tool calls, increased refusals or failures, and signs of compromise. Protect monitoring data and limit access to it.
Prepare an AI-specific incident path
Extend the organisation’s incident response plan to cover model and data rollback, prompt or retrieval isolation, credential revocation, supplier escalation, user notification, evidence preservation and safe shutdown or degraded operation. Define who can suspend automated actions and who decides whether the system may return to service. Keep a known-good model, configuration and data state where feasible.
Reassess when the system changes
Review the assessment after incidents and after significant changes to the model, prompts, datasets, integrations, permissions, configuration, supplier, processing location, use case or threat picture. Version important changes and evaluate them before deployment. A model update that appears minor can alter behaviour, data handling or tool use and should not bypass risk review.
Governance, skills and independent support
Boards and senior managers should receive a clear view of critical assets, worst-case failures, accountability, supplier exposure, incident readiness and capability gaps. Operators need training on system limits, safe data handling, escalation and their own approval responsibilities. Developers and security teams need practical knowledge of model files, prompt and retrieval risks, evaluation, access control and recovery.
Where internal capability is insufficient, an independent AI security assessment, supplier-assurance review or staff-training engagement can help. Check the provider’s technical competence, independence, scope, deliverables, treatment of confidential data and ability to test the actual deployment rather than offering a generic policy review.
A practical assessment record
A concise record can be maintained in the organisation’s existing risk-management system. Include the following fields:
- System name, purpose, version, environment and accountable business owner.
- Roles: developer, operator, data custodian, user and supplier.
- Assets, data flows, locations, retention and permissions.
- Models, prompts, datasets, retrieval sources, APIs, libraries and connected systems.
- Threat scenarios, assumptions, affected parties, likelihood, impact and current controls.
- Required safeguards, evidence, owner, deadline and implementation status.
- Residual-risk decision, approver and review date.
- Test results, monitoring signals, incident procedures and recovery state.
- Change triggers and the date of the next assessment.
Common assessment failures and their fixes
| Failure | Why it fails | Fix |
|---|---|---|
| Scoring before defining the system | Important data, integrations and actions remain outside the rating | Write the purpose, boundary, roles and permissions first |
| Assessing only the model | Application, identity, APIs, libraries and suppliers create exploitable paths | Threat-model the complete service and its dependencies |
| Trusting a supplier’s brand or marketing | Security depends on configuration, contract, data handling and operations | Perform documented, AI-specific due diligence and retain evidence |
| Using human review as a slogan | Reviewers may lack context, time or authority to catch failures | Define review conditions, evidence, intervention powers and audit trails |
| Treating deployment as the finish line | Models, data, prompts and threats change after launch | Monitor, version, test and reassess on defined triggers |
| Accepting residual risk informally | No one is clearly accountable when controls are incomplete | Name the approver, rationale, expiry or review date and recovery plan |
What good looks like
A defensible UK assessment is specific enough that another competent team can reconstruct the system, understand its most serious failure paths, see which controls are operating, identify the person accountable for each decision and determine what happens when the system or its supplier changes. It combines system-based analysis with component-focused checks where useful, especially for organisations supporting essential functions. It also produces operational evidence: test results, access reviews, supplier records, monitoring alerts, incident exercises and approved residual-risk decisions.
The result is not a permanent “safe” label. It is a maintained decision about whether a particular AI use is acceptable, under which controls, with what fallback and who must act when assumptions no longer hold.
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