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The UK has published new AI risk-management guidance and separately plans to expand a government AI-assisted cyber-defence pilot. The two initiatives are not one programme: the Department for Science, Innovation and Technology (DSIT) toolkit is guidance for managing risks across an AI system’s lifecycle, while the Government Cyber Coordination Centre (GC3) pilot tested AI-assisted scanning of public code repositories. The pilot’s reported first-phase results are available; its planned expansion has not yet reported outcomes.
What the UK’s AI risk toolkit is—and what it asks organisations to do
DSIT published its AI Risk Management Toolkit on 8 September 2026. It is guidance and a starting point for risk-management practice, not a new law. It is intended for people involved in designing, operating, procuring or delivering AI-enabled products and services, and aligns its process with the government’s Orange Book approach to risk management. Read DSIT’s toolkit.
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The toolkit supports risk identification and assessment, treatment, monitoring and reporting throughout an AI system’s lifecycle. It includes guidance, critical questions to help identify risks, a workbook for recording risks and treatments, and a monitoring dashboard. Organisations can use an existing risk register if it records risks, mitigations and owners and is kept up to date.
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The guidance covers more than technical failure. Its categories include:
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- Financial risk and legal or regulatory compliance
- Transparency and explainability, fairness, accountability and governance
- Contestability and redress
- Technical robustness and security
- Effects on people and the environment
DSIT recommends multidisciplinary risk teams, clear ownership and a defined risk appetite. Teams should reassess when the model, data, deployment context or external conditions change. Security considerations include data poisoning, data leakage, perturbation attacks, prompt injection and cyber-attacks; these are threats to consider in an organisation’s assessment, not reported incidents from the government pilot.
What the government’s AI cyber-defence pilot found
GC3 ran weekly, in-person hackathons in which teams used frontier AI to examine public code repositories across government, with specialists from the AI Security Institute (AISI) and the National Cyber Security Centre (NCSC). Workflows varied: some used staged AI agents, some combined conventional scanning tools with model-assisted review, and others applied reusable, domain-specific audit skills. Human experts checked candidate findings, assessed exposure, filtered false positives and routed issues for remediation. Read the government case study.
GC3’s case study reports these results for the initial phase:
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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →| Reported measure | Scope and qualification |
|---|---|
| 407 findings | Reported by GC3 for the pilot’s initial phase across nine government organisations over one month. |
| Critical weaknesses remediated | The case study says all critical weaknesses identified in the pilot were remediated, including issues involving authentication bypass, data exposure and remote code execution. |
| No evidence of exploitation identified | The case study reports no evidence of exploitation for any finding; this is not proof that the pilot prevented exploitation. |
| £13,000 in token costs | Reported by GC3 for the month-long pilot across nine government organisations; it is not a forecast of the cost of future phases. |
The case study’s practical lesson is that AI can surface candidate issues, but it does not replace validation or the work of fixing them. Tightly scoped tasks and structured workflows produced stronger results. Poorly scoped runs can spend tokens on low-value targets or send more alerts to security teams than they can usefully triage. The case study also describes models tracing issues across service boundaries and adding business-logic context; conventional scanners remained part of some workflows.
Has the planned pilot expansion started?
GC3 says a second phase is planned, with more departments, additional models and a move beyond public code to closed-source estates. AISI and NCSC involvement is also set to continue. The published case study describes this as a plan; it does not provide results from the second phase or establish that the pilot has been deployed government-wide.
The broader Government Cyber Action Plan sets out a build, scale and improve approach to public-sector cyber resilience and includes an intended route for moving successful pilots into operational capabilities. That wider ambition does not mean this particular pilot has already been scaled across government. Read the Government Cyber Action Plan.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How Ofcom’s work fits in
Ofcom opened a separate engagement on 15 September 2026 about AI’s role in cyber defence for critical communications networks and services. It is examining how AI-enabled tools could support security and resilience, alongside questions of trust, assurance, accountability and compliance. Ofcom planned discussions with industry, technical experts and vendors during autumn 2026, with findings expected in early 2027. Read Ofcom’s engagement.
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Other UK AI initiatives are separate
The government’s AI Risk Register is a cross-sector resource distinct from DSIT’s toolkit. In a parliamentary answer on 18 September 2026, the Cabinet Office said the register draws on AISI and cross-Whitehall technical expertise and includes risks spanning sectors. The answer did not say whether loss-of-control scenarios have likelihood assessments.
The Ministry of Defence’s Rapid AI Delivery Taskforce, announced on 10 June 2026, is another separate initiative focused on accelerating AI-enabled capabilities for the Armed Forces. It is not part of the risk toolkit or GC3 cyber-defence pilot.
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