AI Safety Asia: Crisis Diplomacy and Evidence-Based AI Governance at India Summit 2026

Something shifted at the India AI Impact Summit 2026, and it wasn’t subtle. The conversation about governing advanced AI systems stopped circling around whether governments should step in and landed squarely on how. Not in theory. Not in a white paper. In practice, with real scenarios, real stakes, and real urgency.

Key Takeaways

1. AI Safety Asia Advanced Crisis Diplomacy Mechanisms for Cross-Border AI Incidents. AI Safety Asia (AISA) co-hosted a session on AI crisis diplomacy at the India AI Impact Summit 2026, bringing together experts including Professor Stuart Russell and Audrey Tang to work through plausible cross-border crisis scenarios. The session focused on building operational channels between technical evaluators and diplomatic decision-makers to enable coordination when AI-related incidents move faster than traditional governance structures can respond.
2. The International AI Safety Report 2026 Confronts the Evidence Dilemma for Policymakers. Chaired by Turing Award winner Yoshua Bengio and launched at the summit, the International AI Safety Report 2026 provides an independent scientific assessment of frontier AI risks—including malicious use, autonomous malfunctions, and systemic disruption. The report documents rapid advances in reasoning systems alongside continued reliability challenges and concludes that risk management requires layered defenses, not a single safeguard.
3. Asia Is Building Its Own AI Governance Capacity, Not Waiting for Western Models. AISA’s work challenges the assumption that governance frameworks will be developed in Washington, Brussels, or London and adopted elsewhere. Across Asia and the Middle East, policymakers are constructing AI governance through local regulatory agencies and regional priorities—a fundamentally different approach from Europe’s top-down AI Act or North America’s market-led model.
4. 90% of Government Organizations Lack Centralized AI Governance. According to the Kiteworks 2026 Data Security and Compliance Risk Forecast Report, 90% of government organizations have no centralized AI data gateway, and one-third have no dedicated AI data controls at all. These are organizations handling citizen data, classified information, and critical infrastructure—operating AI systems that were deployed without governance.
5. Joint Testing Between Countries Builds the Trust Infrastructure That Crisis Response Requires. The summit highlighted that joint AI safety evaluations between countries are not just about measuring model performance—they build the trust and working relationships that allow regulators to coordinate before incidents escalate. This mirrors broader cybersecurity trends: The WEF Global Cybersecurity Outlook 2026 found 74% of security leaders value cyber regulations, but cross-border consistency remains the primary challenge.

AI Safety Asia (AISA), a Hong Kong-based organization focused on building regional governance capacity, convened two sessions at the summit that cut right to the heart of what makes AI governance so difficult in 2026: speed, fragmentation, and the growing gap between what these systems can do and what institutions are equipped to handle.

The first session tackled crisis diplomacy. The second marked the official launch of the International AI Safety Report 2026. Together, they painted a picture of a world that is no longer debating whether AI needs guardrails but is racing to figure out who builds them, who enforces them, and what happens when something goes wrong across three countries at once.

When AI Crises Cross Borders, Who Picks Up the Phone?

On February 17, inside Bharat Mandapam in New Delhi, AISA co-hosted a session titled “AI Crisis Diplomacy: Governing AI in a Fragmented World.” The partners included the Center for Human-Compatible AI (CHAI) and the International Association for Safe and Ethical Artificial Intelligence (IASEAI). The panel featured some serious firepower: Professor Stuart Russell, Audrey Tang, Dr. Yuko Harayama, Wan Sie Lee, and Azizjon Azimi, with AISA’s Chief Strategy Officer Alejandro Reyes moderating.

What made this session stand out wasn’t the credentials on stage. It was the scenarios the panelists worked through. Not abstract thought experiments, but plausible crises that test the limits of current governance structures. Think of a deepfake incident that destabilizes diplomatic relations before anyone can verify whether it’s real. Or an AI-enabled cyberattack that cascades across multiple jurisdictions faster than any government can respond. Or an autonomous infrastructure system hosted in one country, operated by another, and affecting a third.

The problem these scenarios expose is not detection. Detection technology exists and is getting better. The real problem is coordination under uncertainty. When a crisis moves at machine speed, human institutions that rely on deliberation, chain-of-command approvals, and bilateral protocols simply cannot keep pace. And right now, there are very few operational channels between the technical evaluators who can assess what’s happening and the diplomats who need to decide what to do about it.

This matters beyond theory. The World Economic Forum’s Global Cybersecurity Outlook 2026 report found that 94% of survey respondents see AI as the most significant driver of change in cybersecurity this year. Meanwhile, 87% identified AI-related vulnerabilities as the fastest-growing cyber risk over the past year. These are not speculative concerns. They are the lived reality of organizations already deploying these systems.

The “Too Fast to Regulate” Myth Gets Dismantled

One of the most persistent arguments against AI governance is that the technology moves too fast for regulation to keep up. The panelists in New Delhi took that argument apart with a compelling counter: We’ve done this before.

Aviation doesn’t evolve slowly. Neither does nuclear energy, nor pharmaceuticals. Yet all three are governed through frameworks that set acceptable risk thresholds and require evidence that systems meet them. The speed of innovation in those fields did not make governance obsolete. It made governance essential. AI should be treated no differently.

What this means in practice is that governments need to stop accepting vague reassurances from AI developers and start insisting on demonstrable safety evidence and credible liability frameworks. The panel’s message was clear: Disclaimers and opaque risk assessments are not a governance strategy. They are a liability strategy—and an increasingly thin one at that.

Governments already know how to cooperate during crises. Pandemic response showed it. Cybersecurity coordination across borders has shown it. The gap in AI governance is not about the absence of diplomatic architecture. It’s about the absence of operational channels between the people who understand the technical risk and the people who have the authority to act on it.

Joint Testing as Trust-Building Infrastructure

The AISA session surfaced a point that deserves wider attention: Joint testing efforts between countries are not just about measuring model performance. They are about building trust.

When regulators from different countries participate in shared evaluations, they develop a working relationship. They learn each other’s terminology, priorities, and constraints. That shared understanding is what allows a regulator in one country to pick up the phone, compare signals, and verify information with a counterpart in another country—before a minor incident escalates into a diplomatic crisis.

This mirrors broader trends in cybersecurity governance. According to the WEF Global Cybersecurity Outlook 2026, 74% of security leaders globally hold a positive view of the effectiveness of cyber-related regulations. But there’s a catch: Respondents in markets with more mature regulations, like Europe and North America, reported greater difficulty applying them consistently across borders. Complexity and compliance burdens increase with regulatory maturity. That’s a governance problem that joint evaluation frameworks could directly address.

The Kiteworks 2026 Data Security and Compliance Risk Forecast Report adds another dimension. It found that 90% of government organizations lack centralized AI governance, and a full third have no dedicated AI data controls whatsoever. These are organizations handling citizen data, classified information, and critical infrastructure. The gap between deployment speed and governance readiness is widening, not shrinking.

The Evidence Dilemma: Act Now or Wait for Proof?

The next day, February 18, AISA co-hosted the launch of the International AI Safety Report 2026 at the High Commission of Canada in India. This event was organized in partnership with the High Commission, the UK AI Security Institute, and Mila, the Quebec Artificial Intelligence Institute.

The report is chaired by Professor Yoshua Bengio, the Turing Award-winning researcher and founder of Mila, with co-leads Carina Prunkl and Stephen Clare. It provides an independent scientific assessment of frontier AI capabilities and risks, covering everything from malicious use and autonomous malfunctions to systemic disruption.

The central tension the report confronts is what you might call the evidence dilemma. Policymakers are being asked to make consequential decisions about AI safety under conditions of genuine uncertainty. The data is incomplete. The risk models are imperfect. And the technology is evolving faster than the science that evaluates it.

But the alternative to acting under uncertainty is waiting for perfect evidence. And waiting means exposure. The report documents rapid advances in reasoning systems and AI agents alongside continued reliability challenges and growing risks in cyber and biological domains. The clear takeaway: Risk management cannot rest on a single safeguard. It requires layers of defense—technical measures, institutional oversight, and broader societal resilience working together.

Asia Is Not Waiting for Governance Models to Arrive From Elsewhere

Perhaps the most significant undercurrent at both sessions was the role of Asia and the broader Global South in shaping AI governance frameworks. The default assumption in many policy circles is that governance models will be developed in Washington, Brussels, or London and then adopted elsewhere. AISA’s work challenges that assumption head-on.

Across Asia, policymakers, regulators, and technical experts are building their own governance capacity, shaped by local institutional realities and regional priorities. The Middle East, for example, is constructing AI governance through SDAIA oversight and active regulatory engagement—a fundamentally different approach from Europe’s top-down AI Act or North America’s market-led model.

AISA’s mission is to ensure that regional expertise informs both national decisions and international debates. That matters because AI governance is not a one-size-fits-all proposition. A framework designed for the regulatory infrastructure of the European Union will not map cleanly onto the institutional landscape of Southeast Asia or the Gulf states. Effective governance must be locally grounded while contributing to global norms.

What This Means for Organizations Deploying AI

For businesses and institutions operating in or from Asia, the signal from the India AI Impact Summit is unmistakable. Expect emerging expectations around model documentation, red-teaming, and international information-sharing. The days of treating AI governance as a future concern are over.

Organizations should start aligning internal AI risk registers and data protection impact assessment processes with likely regional standards. That means moving beyond compliance checklists and building genuine governance infrastructure—centralized AI oversight, purpose-binding controls, and incident response protocols that can operate across jurisdictions.

The WEF report found that the share of organizations assessing the security of AI tools before deployment nearly doubled from 37% in 2025 to 64% in 2026. That trend will accelerate. And organizations that wait for regulations to be finalized before acting will find themselves playing catch-up in an environment where the rules are being written faster than most compliance teams can adapt.

AI Governance No Longer a Philosophical Debate

AI governance is no longer a philosophical debate. It is an operational challenge. The India AI Impact Summit 2026 showed that the conversation has shifted from declarations of intent to questions of implementation: Who verifies safety claims? Who coordinates when an incident crosses borders? Who is accountable when an autonomous system causes harm and no single ministry is in charge?

The next AI-driven crisis will not unfold on a diplomatic timetable. It will move at machine speed. Whether diplomacy and safety infrastructure can keep pace depends entirely on the institutions, relationships, and verification channels being built right now. Not after the fact.

AISA’s work at the summit represents a tangible step toward building that infrastructure from the ground up—rooted in evidence, grounded in regional realities, and designed for a world where the speed of technology has permanently outpaced the speed of traditional governance.