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The UK’s latest AI and cyber-security announcements are two separate initiatives: the Department for Science, Innovation and Technology (DSIT) published lifecycle risk-management guidance on 8 September 2026, while the Government Cyber Coordination Centre (GC3) plans to expand its AI-assisted code-scanning pilot. The guidance is a practical toolkit, not a new law; the pilot’s next phase is planned, not yet reported as underway.
What the UK’s AI risk toolkit is—and what it is not
DSIT’s AI Risk Management Toolkit is guidance for people who design, operate, procure or deliver AI-enabled products and services. It adapts the government’s Orange Book risk-management process to AI, with a lifecycle approach to identifying, assessing, treating, monitoring and reporting risks. DSIT presents it as a starting point for organisations, not a statutory compliance regime or a single centrally run programme.
The toolkit includes guidance, questions to help identify risks, a workbook for recording risks and treatments, and a monitoring dashboard. Organisations do not necessarily have to replace an existing risk register: DSIT says an existing system can be used if it records and updates risks, mitigations and responsible owners.
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What risks organisations are asked to manage
The toolkit treats AI risk as broader than technical failure or cyber security. Its categories include financial exposure; legal and regulatory compliance; transparency and explainability; fairness; accountability and governance; contestability and redress; technical robustness; security; and effects on people and the environment.
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DSIT recommends that organisations assign clear ownership, define their risk appetite and involve people from multiple disciplines in risk management. Assessment should be revisited when a model, its data, its deployment context or relevant external conditions change. In security assessments, the guidance asks teams to consider threats including data poisoning, data leakage, perturbation attacks, prompt injection and cyber-attacks. These are risk scenarios to assess, not incidents attributed to the GC3 pilot.
What the government’s AI cyber-defence pilot reported
GC3 ran weekly, in-person hackathons with specialists from the AI Security Institute (AISI) and the National Cyber Security Centre (NCSC). The teams used frontier AI to examine public code repositories across government. Their workflows varied: some broke reviews into structured AI-agent stages; others used conventional scanning tools to find candidates for model-assisted review; and some used reusable, domain-specific audit skills.
Human experts checked the candidate findings, assessed exposure, filtered false positives and routed issues for remediation. The government case study reports these results for the initial pilot:
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| Reported result | Scope and qualification |
|---|---|
| 407 findings | Reported by the Government Cyber Coordination Centre for the pilot; the case study says the findings included critical weaknesses involving authentication bypass, data exposure and remote code execution. |
| All critical weaknesses remediated | The case study reports that all critical weaknesses identified in the pilot were remediated. This describes remediation of those findings, not a guarantee that all vulnerabilities in the affected systems were eliminated. |
| No evidence of exploitation found | The case study reports no evidence that any finding had been exploited; that is not proof that exploitation could never have occurred. |
| £13,000 in token costs | The government case study reports this cost across nine government organisations over one month. It is a pilot figure, not a forecast for future phases. |
Why human review and remediation still matter
The pilot’s results depended on more than a model identifying possible weaknesses. Human reviewers judged whether findings were real and significant, assessed exposure and sent validated issues into a remediation process. Finding a vulnerability is not the same as fixing it.
The case study says the strongest approaches used tightly scoped work and structured workflows. Poorly scoped runs can spend tokens on low-value targets or generate more candidate findings than security teams can efficiently assess. The reported result therefore does not show that AI replaces conventional scanners or security professionals: some workflows used existing scanning tools, and people remained central to validation and follow-up. The case study also notes that models could trace issues across service boundaries and add business-logic context.
Has the pilot expansion started?
GC3 says it will begin a second phase that adds departments and models and expands from public code repositories to closed-source estates, with AISI and NCSC involvement continuing. The published case study describes this as a plan; it does not report completed second-phase work or results. It would be inaccurate to treat the expansion as already delivered or to describe the pilot as deployed across government.
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The Government Cyber Action Plan provides broader context: it sets out phases to build, scale and improve government-wide cyber resilience, including an intended route for successful pilots to become operational capabilities. That ambition does not establish that this particular GC3 pilot has already been adopted government-wide.
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Ofcom opened a separate engagement on AI’s role in cyber defence on 15 September 2026. It is examining how AI-enabled tools might support the security and resilience of critical communications networks and services, 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.
In its engagement, Ofcom said: “AI-enabled tools can help organisations detect threats more quickly, improve vulnerability management, accelerate incident response and strengthen the resilience of networks and services.” This is the regulator’s description of potential benefits, not a claim that the GC3 pilot has demonstrated those outcomes for communications networks.
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Other UK AI initiatives are separate
The Cabinet Office’s Government AI Risk Register is a distinct cross-sector resource. In a parliamentary answer on 18 September 2026, the Cabinet Office said it covers risks across sectors and draws on AISI and cross-Whitehall technical expertise. The answer did not establish whether the register assigns likelihood assessments to loss-of-control scenarios.
The Ministry of Defence’s Rapid AI Delivery Taskforce, announced on 10 June 2026, is another separate strand, focused on accelerating AI-enabled capabilities for the Armed Forces. Neither it nor the cross-sector risk register is the DSIT toolkit or GC3 cyber-defence pilot.
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