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Assessing AI security risk is a lifecycle exercise, not a one-off checklist. Start by defining the system’s purpose and boundary, map its data and dependencies, model both AI-specific and conventional cyber threats, assign controls and accountable owners, record residual risk, and continue testing and monitoring after release. UK organisations can align this work with the National Cyber Security Centre (NCSC) guidance and the UK Government’s voluntary Code of Practice for the Cyber Security of AI while retaining their existing enterprise-risk, data-protection and incident-management processes.
What an AI security risk assessment must establish
A useful assessment should let a board or accountable manager answer five questions:
- What is the AI system intended to do, and what is outside its approved use?
- Which information, models, infrastructure, people and connected services could be affected?
- How could an attacker, supplier failure, bad data or an ordinary software defect change the system’s behaviour?
- Which safeguards reduce those risks, who owns each safeguard, and what risk remains?
- How will the organisation detect, respond to and recover from a failure after deployment?
The NCSC’s manager and board guidance says security should be integrated into AI projects and workflows “from inception”. That means the assessment starts when a business case is being considered, not after a model has been purchased or connected to production data.
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Write the business purpose and approved use
Describe the business problem, the expected benefit and why an AI approach is appropriate. State the decisions or actions the system may support and those it must never make. Include the intended users, operating environment, geographic scope and any assumptions about human review.
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Also record whether the organisation is acting as a developer, system operator, data custodian, user, or several of these at once. Different roles create different responsibilities for design choices, data, updates, monitoring and incident response.
Draw the system boundary
Include every component that can influence the outcome or be influenced by it:
- Foundation, fine-tuned or traditional machine-learning models and their versions.
- Training, fine-tuning, retrieval and feedback datasets.
- System prompts, user prompts, guardrails, filters and evaluation code.
- Model-serving infrastructure, software libraries, containers, identity systems and networks.
- Retrieval stores, file shares, APIs, plugins, agents and downstream applications.
- Users, administrators, developers, suppliers and support organisations.
Mark what the system can read, change, send or trigger. A text-only assistant and an agent that can alter records or send payments have very different security boundaries even if they use the same model.
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Name a business risk owner who can accept or reject residual risk, plus technical owners for the model, data, infrastructure, integrations and operations. Give each treatment an owner and a due date. Security, privacy, legal, procurement and service-management roles should be consulted, but consultation is not a substitute for a person with authority to make the risk decision.
2. Inventory assets, data flows and dependencies
Create an inventory that is detailed enough to trace an input to an output and an action. For every asset, record its location, sensitivity, owner, permitted users and recovery method.
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| Asset or dependency | Questions to record |
|---|---|
| Model and weights | Which model and version are in use? Who supplied or trained it? Where are weights stored? Can they be replaced or rolled back? |
| Data | What training, retrieval, feedback and operational data is used? Where is it processed and retained? Does it contain confidential, personal or regulated information? |
| Prompts and policies | Who can change system prompts, tools, filters, safety rules and configuration? Are changes versioned and approved? |
| Applications and APIs | Which identities invoke the model? What rate limits, authentication and authorisation apply? What systems receive outputs? |
| Logs and telemetry | What inputs, outputs, tool calls and administrator actions are logged? Who can read or alter logs, and how long are they retained? |
| Infrastructure | Which cloud, on-premises, network, container, accelerator and backup resources are required? What is the recovery path if they fail? |
| People and suppliers | Who develops, operates, reviews and supports the system? Which external model, hosting, data, evaluation or software providers are critical? |
Map data flows, including information sent to an external model or API. Document the provider’s processing location, retention, use for service improvement, deletion process and subcontractors where those facts are available. Treat logs, credentials, model files and detailed failure-mode information as sensitive assets; they can reveal personal data, attack paths or capabilities an attacker could exploit.
3. Perform AI-specific supplier and dependency due diligence
Use the organisation’s normal software and supplier-risk process, adding questions that address model behaviour and data handling. A brand name or a statement that a service is “secure” is not evidence that it meets your requirements.
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|---|---|
| Security responsibilities | A clear shared-responsibility description covering the model, API, hosting, identity, data and customer configuration. |
| Data handling | Types of data accepted, processing locations, retention, deletion, training use, subprocessors and breach-notification commitments. |
| Limits and failure modes | Documented restrictions, known unsafe behaviours, rate limits, context limits, tool permissions and service dependencies. |
| Update practice | How model, weights, prompts, libraries and infrastructure changes are announced, tested, versioned and rolled back. |
| Assurance | Relevant security documentation, independent assessments, test results, vulnerability reporting and incident history where the supplier can provide them. |
| Continuity | Service-level commitments, export or migration options, an alternative provider or a safe degraded mode for critical functions. |
| Incident communication | Named contacts, escalation times, information supplied during an incident and cooperation with your investigation. |
Record unanswered questions rather than silently treating them as low risk. For a critical dependency, decide whether the uncertainty itself requires a compensating control, a contract condition, a restricted use case or a different design.
4. Threat-model the AI system and its use
Describe attacker goals, entry points, vulnerable components and consequences. Analyse AI-specific abuse together with ordinary attacks on identities, networks, applications, APIs and software supply chains.
| Threat scenario | What to examine | Potential consequence |
|---|---|---|
| Prompt injection and unsafe input | Malicious instructions in user prompts, documents, web pages or retrieved content; attempts to override system rules or manipulate tool calls. | Disclosure, unsafe output, unauthorised actions or compromise of connected systems. |
| Data poisoning or tampering | Unauthorised changes to training, fine-tuning, feedback, retrieval or operational data; weak approval and provenance controls. | Systematic bias, backdoors, incorrect decisions or loss of confidence in outputs. |
| Privacy leakage and model extraction | Queries that reveal training information, membership inference, excessive output detail, or unauthorised access to weights and endpoints. | Exposure of personal or confidential information, intellectual-property loss or reconstruction of model functionality. |
| Compromised model files or libraries | Untrusted weights, serialized objects, packages, containers and build pipelines; insufficient scanning or isolation. | Arbitrary code execution, credential theft, persistence or compromise of the wider environment. |
| Over-permissioned integration | Broad read/write access, shared service accounts, unrestricted plugins, weak approval gates or tools that act on untrusted output. | A model error or manipulation becomes a data breach, fraudulent transaction or operational incident. |
| Availability and behaviour change | Provider outage, resource exhaustion, denial of service, model drift, silent updates, corrupted outputs or dependency failure. | Service interruption, degraded decisions, unsafe automation and knock-on effects for people or dependent systems. |
For each scenario, document assumptions, affected assets and people, existing vulnerabilities, the likelihood and impact method used by your organisation, current controls, additional treatment, owner, deadline and the residual-risk decision. Include indirect effects: a wrong output may be copied into another system, acted on by a busy employee or used to train a later model.
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The NCSC Cyber Assessment Framework (CAF) says there is no single blueprint for cyber security. Choose a method that fits the organisation, analyse dependencies and interactions, and combine system-based and component-focused views where that improves coverage. A one-time checklist cannot replace analysis of how the service actually operates.
5. Set security requirements and choose risk treatment
Turn the threat model into testable requirements. Controls should reduce a stated risk, not merely demonstrate that a policy exists.
Constrain access and actions
- Apply least privilege and secure defaults to models, APIs, data stores, development pipelines and connected systems.
- Use separate identities and credentials for development, testing and production; prohibit shared administrator accounts.
- Allow only the tools and data required for the approved task. Put high-impact actions behind explicit authorisation and, where appropriate, human approval.
- Filter or block sensitive information leaving the organisation, and define what users may submit to external services.
Protect components and provenance
- Obtain model files and libraries from known sources, record versions and hashes where practical, and review licences and provenance.
- Scan and isolate third-party weights or serialized files before loading them; do not assume a model file is passive data.
- Harden build and deployment pipelines, protect signing and release credentials, and retain a known-good version for rollback.
Make data and behaviour traceable
- Document data sources, permitted uses, retention, limitations, guardrails, evaluation results and known failure modes.
- Version prompts, datasets, model configurations and integrations. Require review for changes that could alter behaviour or access.
- Protect logs and backups from unauthorised reading or modification, while applying appropriate privacy and data-protection controls.
Make human oversight real
Human oversight is a control only when the responsible person can understand the output, has enough time and information to challenge it, and can stop or reverse an action. Provide confidence or evidence indicators where useful, expose relevant source material, and design an escalation route for uncertain or harmful results. A human who merely clicks “approve” on an opaque, high-volume workflow is not a meaningful safeguard.
Decide how to treat residual risk
For each scenario, state whether the organisation will reduce, avoid, transfer or accept the risk. Acceptance must be explicit, time-bounded where appropriate, assigned to an authorised owner and revisited when assumptions change. The UK Code notes that residual risk remains even after controls; recording it prevents an unowned decision from being mistaken for security.
6. Test before release and after material change
Testing should cover the complete system, not only the model’s benchmark score. Define success and failure conditions before testing so that a convenient result cannot redefine the requirement.
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- Test authentication, authorisation, tenant separation, rate limits, secrets handling and logging.
- Use adversarial prompts and untrusted retrieved content to test instruction following, data exfiltration and unsafe tool use.
- Test poisoned or malformed data, corrupted model files, dependency compromise and rollback procedures in an isolated environment.
- Measure whether output filters, action limits and human approval gates work under normal load and attack-like conditions.
- Run security evaluation or red-team exercises proportionate to the impact of failure, and track findings to closure or formal acceptance.
- Verify that users receive clear information about limitations, approved uses and their responsibilities.
Repeat relevant tests after model, prompt, dataset, integration, configuration or infrastructure changes. A provider’s silent model update can be a material change even when your application code is unchanged, so establish a notification and re-evaluation mechanism in the supplier relationship.
7. Prepare incident response and recovery
Extend the organisation’s incident plan to cover AI-specific evidence and decisions. Define who can disable a model, revoke an API key, disconnect a tool, switch to a safe degraded mode or restore a known-good version.
- List alert conditions such as unusual prompt patterns, data-access spikes, unexpected tool calls, output-quality shifts, model-file changes or supplier notifications.
- Preserve relevant prompts, outputs, tool calls, identities, configuration versions and network evidence, subject to privacy and retention rules.
- Set escalation routes for security, service owners, privacy, legal, communications and affected business leaders.
- Prepare containment options that do not destroy evidence: disable high-risk actions, isolate the model, restrict data, or revert to a tested version.
- Exercise recovery with the people who will perform it. Confirm that backups, configuration records, credentials and alternative services are actually usable.
- After an incident, update the threat model, controls, supplier requirements and user guidance rather than merely closing the ticket.
8. Monitor the system in operation
Monitoring should support detection and investigation without collecting more personal information than necessary. Define retention, access and review rules with security and privacy teams.
Monitor inputs, outputs, access, tool calls, performance, resource use and dependency health. Look for both sudden attacks and gradual changes: prompt-injection patterns, abnormal data requests, drift in input data, declining evaluation results, unexplained output changes, repeated policy violations and unusual administrator activity. Establish thresholds that trigger investigation, not just dashboards that nobody owns.
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Review whether controls still match the actual use. A pilot that began as read-only may gain write access, new data sources or a larger user population. Those changes alter the risk even if the underlying model is unchanged.
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9. Govern the assessment as a living record
Keep a versioned assessment package containing:
- Purpose, approved and prohibited uses, system boundary and role assignments.
- Architecture and data-flow diagrams, asset inventory and dependency register.
- Threat scenarios, assumptions, impact analysis, controls, test evidence and residual-risk decisions.
- Model, data, prompt, configuration and supplier versions, with change approvals.
- Monitoring, incident, recovery and reassessment triggers.
Reassess after a significant change to the model, data, prompt, integration, configuration, supplier, user population or business purpose; after an incident or near miss; and when credible threat information changes. Set a periodic review interval appropriate to the system’s impact and rate of change, but do not wait for the calendar when a trigger occurs.
How UK guidance fits together
NCSC Guidelines for Secure AI System Development
The NCSC published version 1.0 of its Guidelines for Secure AI System Development on 27 November 2023. The guidance covers secure design, development, deployment, and operation and maintenance. Use those lifecycle sections to assign requirements and evidence to the right stage rather than treating security as a final gate.
UK Government Code of Practice for the Cyber Security of AI
The Code describes a voluntary approach intended to help establish a global baseline through standards work. Its principles address risk assessment and due diligence, threat analysis, human responsibility, asset protection and other lifecycle practices. It is not, on the evidence available here, a general statutory obligation. Organisations must still check their own data-protection, safety, contractual and sector-specific duties.
The Department for Science, Innovation and Technology reported that 80% of respondents endorsed the proposed intervention in its 2024 Call for Views, with support for individual principles ranging from 83% to 90%. Those figures measure consultation support; they do not measure incident prevalence, control effectiveness or implementation by UK organisations.
NCSC Cyber Assessment Framework
CAF Principle A2, Risk Management, is especially useful for organisations responsible for essential functions. It emphasises understanding risks to those functions, setting requirements and communicating decisions to accountable leaders. CAF is not presented as a universal requirement for every organisation, and its assessment depth should match the service and consequences involved.
Worked example: an internal document assistant
Suppose a company wants an assistant that searches internal documents and drafts answers for staff. The assessment should not stop at “the model is behind the firewall.” Define the boundary to include the document index, ingestion pipeline, identity provider, model API, prompts, logs and any export or email function.
- Purpose: answer policy questions and cite source documents; no authority to change records or send messages.
- Data flow: authenticated staff query the service; retrieval returns permitted passages; the model receives those passages; the answer and citations return to the user.
- Threats: a poisoned document contains instructions to reveal other files; a user attempts prompt injection; an over-broad index returns confidential material; a provider retains prompts unexpectedly.
- Controls: enforce document-level authorisation before retrieval, treat retrieved text as untrusted, restrict outbound data, log access, isolate ingestion, test malicious documents and maintain a rollback index.
- Decision: record which residual risks are accepted, who accepted them and what would trigger a reassessment, such as adding email-sending capability or expanding the index to sensitive records.
The example illustrates why the data store, permissions and workflow can matter as much as the model. The same model would require a different assessment if it could alter documents or make consequential decisions.
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| Failure | Correction |
|---|---|
| Scoring a named model before defining its use | Document purpose, users, data, permissions and dependencies first; risk belongs to the complete system. |
| Testing only accuracy or benchmark performance | Add abuse cases, access-control tests, dependency failure, data leakage and recovery exercises. |
| Trusting supplier assurances without evidence | Request responsibilities, retention, update, incident and continuity details; record unknowns and compensating measures. |
| Giving an agent broad permissions “for convenience” | Use least privilege, separate identities, allow-lists and approval gates for high-impact actions. |
| Calling a user a human-in-the-loop without designing oversight | Give reviewers evidence, time, authority and a way to stop or reverse actions; test that the process works under load. |
| Writing the assessment once and filing it | Version the record, monitor behaviour and reassess after material changes, incidents and supplier updates. |
| Leaving residual risk implicit | Name the accepting authority, decision date, rationale, expiry or review point and conditions for escalation. |
When to obtain specialist help
Independent assessment, supplier assurance or staff training can be sensible where internal capability is limited, the system supports an essential function, or testing requires specialist skills. Define the scope, deliverables, independence, evidence standards and access requirements before appointing anyone, and verify that the provider understands both AI security and your organisation’s operational context. External assistance does not transfer accountability for the final risk decision.
Quick Recap
A concise sign-off checklist
- Purpose, prohibited uses, roles and system boundary are documented.
- Models, data, prompts, infrastructure, APIs, users, suppliers and downstream systems are inventoried.
- Data locations, retention, permissions, external transfers and recovery paths are known or recorded as uncertainties.
- AI-specific and conventional cyber threats are modelled with impacts on confidentiality, integrity, availability, operations, people and connected systems.
- Controls cover least privilege, input and output handling, component provenance, isolation, logging, backups, human oversight and user communication.
- Security testing, adversarial evaluation and rollback have evidence and named owners.
- Incident escalation, containment, recovery and post-incident review are rehearsed.
- Residual risks are explicitly accepted, avoided, transferred or treated, with review triggers.
- Monitoring and reassessment continue throughout the system’s life.
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