ServiceNow’s October release notes for AI Control Tower describe expanded capabilities for discovering, governing, securing and measuring enterprise AI. The update includes mechanisms to identify unsanctioned services using network and endpoint information, with options to block access and enrich the inventory of AI assets.

The product direction reflects a common governance problem. Employees can adopt public models, browser tools and embedded assistants faster than risk teams can catalogue them. Without discovery, an organisation cannot reliably assess data exposure, contractual terms, model behaviour or regulatory obligations.

Blocking is useful but not sufficient. A control programme also needs ownership, risk classification, approved alternatives and a fast route for legitimate exceptions. Overly broad restrictions can push use further underground, while weak controls leave sensitive data and consequential decisions outside oversight.

For banks and insurers, the strongest operating model joins service discovery to identity, data classification, procurement and incident management. Each AI service should have a named owner, a permitted purpose, data boundaries and evidence of monitoring. ServiceNow’s release provides a relevant Big 20 example, but buyers should test integration quality and detection coverage in their own estate rather than treating a platform inventory as complete.

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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