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A successful Global Dialogue on AI Governance should move beyond principles and documentation toward enforceable control at the moment of execution. Most current approaches focus on: transparency auditability post-event accountability Those are necessary, but insufficient once AI systems begin acting in real-world systems. A meaningful outcome would be a shared recognition that governance must operate at the execution boundary, where actions become binding. This includes: clear definitions of execution authority mechanisms to determine whether an action is allowed before it occurs standards for fail-closed behavior in high-risk contexts machine-verifiable evidence of decision paths at runtime Without this shift, governance remains descriptive rather than operational. The next phase of AI governance should focus on ensuring that systems can not only explain what happened, but control what is allowed to happen.

• Safe, secure and trustworthy AI
• Interoperability of governance approaches
• Transparency, accountability, and human oversight

Current AI governance frameworks emphasize safety, transparency, and human oversight, but they often operate after decisions have already been executed. The critical gap is the absence of a control layer that determines whether an action is allowed to become real at the moment of execution. Safety requires more than monitoring. It requires enforcement. Transparency requires more than logs. It requires provable decision paths tied to execution. Human oversight requires more than approval queues. It requires structured escalation and authority at the boundary where actions bind. Interoperability becomes essential because governance cannot be isolated within a single system. Execution control must operate across models, agents, tools, and external systems while preserving clear authority boundaries. Without a shared approach to execution control, governance becomes fragmented, and responsibility becomes ambiguous. The priority should be shifting from governance as documentation to governance as enforceable runtime control.

A critical issue not yet fully captured is the distinction between governance as observation and governance as control. Most current frameworks assume that: decisions can be audited actions can be explained systems can be monitored But they do not address the core question: whether an action should be allowed to occur before it becomes real. As AI systems move from generating outputs to initiating actions across financial systems, infrastructure, and public services, this distinction becomes fundamental. Without a mechanism to enforce admissibility at the execution boundary, governance remains reactive. This introduces several unresolved risks: delegation of authority without clear control surfaces inability to prevent harmful actions in real time over-reliance on post-event accountability fragmented responsibility across multi-agent systems An emerging requirement is the development of execution control infrastructure: systems that determine admissibility before execution enforce policy and authority at runtime produce machine-verifiable evidence of decisions operate across heterogeneous environments This is not a refinement of existing governance approaches. It is a missing layer.

AI governance gaps are already creating measurable risk across enterprise, financial, and public-sector environments as systems move from generating outputs to initiating actions. The most significant challenge is that current governance approaches are largely reactive. They emphasize transparency, auditability, and documentation after decisions have been executed. This creates a structural gap at the moment where actions become operationally binding. In practical terms, this leads to: unclear accountability when AI systems act across multiple services or jurisdictions inability to prevent harmful or unauthorized actions in real time over-reliance on human review processes that do not scale with system speed fragmentation of control across vendors, platforms, and orchestration layers These gaps are especially visible in sectors where AI is integrated into financial transactions, infrastructure management, and automated workflows, where the cost of incorrect or unauthorized actions is immediate. At the same time, this creates a significant opportunity. There is emerging demand for systems that can operate at the execution boundary, determining whether an action is allowed before it becomes real, rather than explaining it afterward. This includes: runtime enforcement of policy and authority fail-closed mechanisms for high-risk actions machine-verifiable evidence of decision paths clear separation between intelligence and execution authority Regions and organizations that adopt this model early will be better positioned to deploy AI systems safely at scale, while maintaining trust, accountability, and regulatory alignment.

The AI Dialogue can play a critical role by shifting international cooperation from principles to operational alignment at the point where AI systems act. Today, most cooperation focuses on high-level frameworks—ethics, transparency, and post-hoc accountability. While necessary, these do not address the core coordination problem: AI systems increasingly operate across borders, vendors, and infrastructures, yet there is no shared understanding of how actions should be governed at the moment they become real. The Dialogue can create value by: establishing a shared definition of the execution boundary, where AI decisions transition into real-world consequences promoting interoperability around admissibility, so systems can consistently determine whether actions are allowed before execution encouraging machine-verifiable evidence standards that enable cross-border auditability without requiring full data sharing supporting fail-closed design principles for high-risk actions in distributed environments This is particularly important in multi-agent and multi-system contexts, where a single outcome may depend on components governed by different jurisdictions and policy regimes. By aligning on how control is enforced at runtime—not just how systems are described or audited afterward—the Dialogue can enable safer international deployment of AI while preserving sovereignty and regulatory diversity.

The AI Dialogue should build on existing governance and technical frameworks, while addressing the gap between policy definition and execution control. Relevant foundations include: NIST AI Risk Management Framework and ISO standards for structured risk and lifecycle governance the EU AI Act for regulatory classification and accountability requirements Zero Trust security architectures for continuous verification and fail-closed principles existing audit, logging, and observability practices across cloud and distributed systems These initiatives provide important guidance, but they largely operate at the levels of design, assessment, or post-event analysis. The added value of the AI Dialogue is to connect these frameworks to the moment of execution. Specifically, it can: define how policy, identity, and risk signals are enforced in real time across system boundaries encourage standardized interfaces for decision inputs, refusal states, and execution evidence promote interoperable "receipt" models that allow actions to be verified across organizations and jurisdictions support minimal, practical validation patterns so cooperation is based on shared behavior, not just shared terminology In doing so, the Dialogue can move AI governance from fragmented compliance efforts toward a coherent, enforceable control layer that operates consistently across global systems

Different stakeholders should contribute based on where they sit relative to the execution boundary of AI systems. Governments define policy intent and regulatory constraints. Industry implements systems that must enforce those constraints in real environments. Researchers provide validation, testing methodologies, and risk modeling. Civil society ensures that outcomes reflect human rights and societal impact. The Dialogue should structure contributions around this alignment rather than general discussion. Recommended format: 1. Boundary-focused working groups Each group should focus on a specific control problem at execution (for example: admissibility, identity binding, or evidence generation), not abstract principles. 2. Written "initial share" before sessions Participants submit structured inputs under shared headings (boundary definition, admissibility logic, evidence requirements, interoperability surfaces). This prevents ambiguity and improves technical clarity. 3. Validation-oriented sessions Instead of purely discussion-based formats, sessions should include concrete artifacts such as sample decision flows, refusal states, or execution records to ground the conversation. 4. Separation of technical and commercial agendas Technical alignment should occur independently from policy negotiation or commercial discussion to avoid conflating objectives. This structure allows stakeholders to contribute in a way that produces actionable alignment rather than high-level consensus statements.

Global AI governance discussions currently underrepresent the stakeholders closest to real-world execution and consequence. These include: engineers and system architects responsible for implementing runtime controls operators managing AI systems in production environments compliance and audit teams who must validate behavior under regulatory scrutiny smaller enterprises and emerging-market deployers who operate under resource constraints but face real risk exposure These groups experience the practical limitations of current governance approaches, particularly the gap between policy definition and enforceable control at execution. To include them effectively: create technical participation tracks alongside policy discussions, allowing contributors to submit system-level insights and artifacts lower barriers to contribution through structured templates that focus on real system behavior rather than formal policy language support regional and sector-specific input channels to capture differences in infrastructure maturity and risk exposure incentivize contributions that include concrete examples, such as failure cases, audit challenges, or integration constraints Including these perspectives ensures that governance frameworks reflect how AI systems actually operate, not just how they are intended to behave.

Meaningful engagement requires moving beyond panel discussions toward formats that reflect how AI systems are built and governed in practice. The following formats can significantly improve outcomes: 1. Execution scenario workshops Participants evaluate specific, realistic scenarios where AI actions cross into real-world consequence. The focus is on how systems determine admissibility before execution, not retrospective analysis. 2. Shared artifact reviews Instead of abstract discussion, participants exchange and review concrete artifacts such as decision pipelines, refusal states, or execution records. This creates alignment around observable behavior. 3. Interoperability simulations Multi-party exercises where different systems interact across defined boundaries, testing how policies, identity, and evidence propagate between environments. 4. Minimal validation frameworks Short, structured exercises that test whether systems meet agreed expectations (for example: fail-closed behavior, traceability, or consistency of decision outcomes). 5. Continuous dialogue loops Rather than one-time events, the Dialogue should support iterative cycles of submission, feedback, and refinement, allowing governance approaches to evolve alongside technology. These formats ensure that engagement produces practical, testable alignment rather than static recommendations.

Effective AI governance requires approaches that operate at the point where systems act, not only where they are designed or evaluated. The most impactful practices emerging today share a common characteristic: they introduce enforceable control at the execution boundary. Examples include: 1. Zero Trust architectures Continuous verification of identity, context, and intent before allowing actions. This principle is directly applicable to AI systems, where every action should be treated as untrusted until validated. 2. Pre-execution control pipelines Structured decision flows that evaluate authority, policy, risk, and constraints before execution occurs. This shifts governance from post-hoc analysis to real-time enforcement. 3. Execution contracts Machine-readable definitions of what an action is allowed to do, under which conditions, and with what limits. These make governance testable, enforceable, and auditable across systems. 4. Simulation and sandboxing Running high-risk actions in controlled environments prior to commit allows systems to evaluate potential consequences without real-world impact. 5. Immutable execution logging (WORM models) Recording every action, decision path, and input state in a tamper-resistant manner enables traceability, auditability, and regulatory defensibility. 6. Separation of intelligence and authority Ensuring that AI systems can propose actions, but do not inherently have the authority to execute them without independent validation. 7. Interoperable evidence and receipt models Standardized, machine-verifiable records that allow actions to be validated across organizational and jurisdictional boundaries. These approaches move AI governance from descriptive frameworks to enforceable infrastructure, which is essential as AI systems increasingly operate in real-world environments.