A declaration signed in Paris by nine governments and ten AI companies calls for a common framework for evaluating and managing frontier-model risk. It commits participants to make non-sensitive evaluation findings comparable, develop a public repository and strengthen cooperation on dangerous misuse, model autonomy and loss-of-control risks.

The company commitments are unusually operational. Developers say they will disclose capabilities, limitations and known risks, support qualified independent evaluation where appropriate, and maintain fail-safe mechanisms capable of pausing or shutting down model operations within their control. The declaration also addresses safeguard removal, continuous risk management, internal red teaming and reporting lines that protect researchers. Governments, meanwhile, commit to secure channels for whistleblowers and to explore better alignment of incident classification and early-warning arrangements.

The signatories include the United Kingdom, France, Japan, Canada and the Netherlands, alongside Anthropic, Microsoft, OpenAI, Mistral AI, Cohere and others. The declaration is a political commitment rather than a self-executing law, so its value will depend on implementation, disclosure quality and whether evaluation results become genuinely comparable.

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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