Independent practitioner

As a security practitioner, I believe the first Global Dialogue on AI Governance would be a success if it clearly moves the conversation out of abstractions and into operational reality. Firstly, success would look like shared clarity on what actually goes wrong with AI systems in practice. Most serious risks I see today don't come from intent or ethics failures alone, but from weak security foundations: poorly protected model access, unclear identity and authorization boundaries, insecure integrations, unvetted third‑party dependencies, and limited visibility once systems are deployed. If the Dialogue helps align stakeholders on these concrete failure modes and treats AI security as a core governance issue rather than a technical footnote that would be a meaningful step forward. Secondly, the Dialogue would succeed if it acknowledges that most AI systems are part of a complex supply chain. In real deployments, responsibility is often split across model providers, platform operators, data sources, and application teams, frequently across jurisdictions. Governance discussions need to reflect this reality. Practical outcomes would include shared expectations around accountability, transparency, and coordination when something goes wrong, without assuming a single regulatory model will fit all contexts. Thirdly, success would mean focusing on what is verifiable, not just what is aspirational. Trustworthy AI cannot rely only on principles or self‑attestation. Practitioners need clearer signals of what "good" looks like: risk management practices, security controls, monitoring expectations, and evidence that systems are behaving as intended over time. Even non‑binding guidance that points toward measurable, implementable practices would make the Dialogue immediately useful.

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

These four areas are the most urgent because they directly affect whether AI systems can be safely deployed, defended, and governed in real environments, not just discussed at a conceptual level. Safe, secure and trustworthy AI is a top priority because many real-world AI risks manifest through security failures rather than intent or ethics alone. Weak identity and access controls, insecure integrations, model abuse, data poisoning, and poorly governed third-party dependencies are already creating tangible harm. Treating security as a foundational element of AI governance is essential if systems are to be trusted at scale. Transparency, accountability, and human oversight because AI systems increasingly make or influence decisions without clear lines of responsibility. In practice, it is often unclear who is accountable when AI systems fail especially when models, platforms, and applications are operated by different parties across jurisdictions. Governance efforts need to emphasize traceability, explainability appropriate to context, and meaningful human control, particularly for high-impact use cases. Interoperability of governance approaches matters because organizations operating globally are navigating a growing patchwork of AI regulations, standards, and expectations. From an implementation standpoint, misaligned governance regimes increase risk, complexity, and uncertainty. The Dialogue has an important role to play in encouraging alignment and mutual recognition across frameworks, without forcing a single regulatory model. AI capacity-building is also needed to ensure that safe and secure AI is not limited to a small number of well-resourced actors. Effective governance depends on the ability of all countries and organizations to understand, implement, and enforce security and risk management practices. Building technical and governance capacity is therefore inseparable from achieving trustworthy AI globally.

Runtime AI governance (most real risks emerge after deployment (drift, misuse, expanding permissions, weak monitoring and incident response)) Identity and access for AI agents (current governance does not adequately address non-human actors acting autonomously or on behalf of users.) Shared responsibility across AI supply chains (unclear ownership and coordination across model, platform, data, and application providers amplifies harm during failures.)

In the enterprise and SaaS sector, governance gaps are creating increasing risk as AI adoption accelerates. AI systems, including agentic workflows, are being deployed into production faster than security controls, monitoring, and guardrails can mature, increasing exposure to misuse, drift, and unintended actions. As AI systems become more autonomous and interconnected, organizations struggle to maintain visibility into decisions and actions, making it difficult to intervene in real time or assign responsibility when failures occur. Stronger security‑by‑design practices create an opportunity to scale AI with confidence, reduce incidents, and build long‑term trust with customers and regulators.

The AI Dialogue can advance international cooperation by acting as a practical coordination forum rather than a purely declarative one. It can help align countries around shared understandings of AI risks and safeguards by grounding discussions in scientific evidence and real deployment experience.