The UK government has set out a direction of travel for reporting and responding to serious AI incidents, with AI Minister Kanishka Narayan saying ministers will examine how near misses and high-impact failures can be identified and reported. The government also plans to develop national capability to contain incidents involving AI agents and support people who are affected.

The announcement is not a final statutory regime. Important details, including thresholds, reporting clocks, responsible authorities and enforcement consequences, remain unresolved. Even so, the policy signal matters for enterprises because it treats AI incidents as operational events requiring evidence, escalation and recovery, rather than as abstract model-risk questions.

Banks, insurers and other regulated firms should start mapping AI failures to existing cyber, operational resilience, conduct and consumer-duty processes. Agent logs, approval records, identity data and rollback evidence may all become necessary to reconstruct what happened. The practical test is whether an organisation can explain which system acted, under whose authority, what controls fired and how harm was contained.

The clearest near-term move is to run an AI incident tabletop exercise that includes legal, security, risk, customer support and business owners. That exposes gaps before reporting rules become mandatory.

Execution Level Governance- What audit-ready agent governance actually looks like
David Girvin, founder and CEO of Assury argues that model-in-the-loop review, AI governing AI, is fundamentally unreliable for regulated environments: even the best-performing models miss a meaningful share of violations, the reviewing model is typically provided by the same vendor being reviewed, and prompt injection or context poisoning can compromise both the acting agent and its supposed overseer simultaneously. He makes the case for deterministic, architecturally enforced controls instead, walking through Assury's approach of autonomy zones, session risk accumulation, and credential starvation, which lets a compromised agent be cut off from its tools instantly rather than relying on time-boxed access. The conversation touches on why David is sceptical of just-in-time credentialing as a solution for agent security more broadly, since agent sessions don't run on predictable human timescales, along with the current gap between how identity and security vendors are pitching agent protection and what he sees happening at the execution layer in practice. He also discusses the compliance and audit implications of probabilistic decision-making, arguing that regulated industries will increasingly need tamper-evident, hash-chained audit trails that can withstand scrutiny from auditors and regulators who are only beginning to understand agentic risk, and reflects on a named frontier lab's own published framework as an example of the gap between research and practitioner reality. Elsewhere, David reflects candidly on building a bootstrapped security company in an increasingly crowded market, why he turned down aggressive VC funding to stay in control of the product, and what a credible third-party assessment of his own gateway would need to look like given that Assury sits directly in the execution path for every customer's agents.
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