Aysdev Global Consultancy llp

To make the first Global Dialogue a real success, we need to stop talking big and start working on frameworks that can be checked and used across domains. From the point of view of project management and compliance, the following results are very important: 1. Security Controls that Work Together The conversation should lead to a plan for ISO-style standardised controls for AI. We need more than just ethical rules; we need an "ISMS for AI" that gives us clear, measurable standards for data integrity and security. A framework that lets audits and verifications happen smoothly across national borders would be a success. 2. Risk Reduction for Specific Sectors There is no one-size-fits-all way to govern. A successful outcome would set clear rules for high-stakes areas like energy and fintech. To avoid global failures, it is important to set clear "red lines" for digital payment security and protecting critical infrastructure. 3. Cybersecurity as the Base You can't have ethical alignment without technical strength. To be successful, cybersecurity must be the foundation of the governance model. To make sure that "aligned" models aren't hurt by outside threats, we need global standards for AI provenance and vulnerability management. 4. Putting into practice capacity-building Ultimately, success is determined by the implementation of these policies at the grassroots level. We need a clear and useful syllabus for professional training that can be added to school curricula. We can make sure that AI stays useful for a long time by giving the next generation of candidates a clear set of "best practices" for governing it. Geneva should not only make a statement but also provide a useful toolkit. The most important goal of the dialogue will have been reached if we leave with a structure that allows for independent verification and clear accountability.

• Protection and promotion of human rights
• Safe, secure and trustworthy AI
• AI capacity-building
• Social, economic, ethical, cultural, linguistic and technical implications of AI

My choice is based on more than 25 years of managing cross-domain risks where "trust" is not assumed; it is verified. These four areas show the change from abstract ideas to the real-world steps needed for a safe digital future. 1. AI that is safe, secure, and reliable In fields with a lot on the line, like oil & gas or fintech, security isn't just a "feature"; it's the base. My top priority is setting up standardised, interoperable controls, which is like an "ISMS for AI". We need measurable benchmarks to make sure that the data is correct and the system is strong enough so that "trust" is backed up by technical proof. 2. Building AI's skills Bridging the digital divide isn't just about getting people new hardware; it's also about sharing knowledge. When mentoring CXO candidates, it's clear that the next generation's success depends on a practical, professional curriculum. We need to give the Global South the technical skills they need to run their AI ecosystems well. 3. Effects on society, the economy, and technology AI is fundamentally changing how people make payments and the infrastructure that supports them around the world. We need to address systemic risks, which can be anything from economic displacement to technical weaknesses. Putting these issues first makes sure that new ideas don't move faster than we can handle the complicated, interconnected effects of these technologies. 4. Safeguarding and Advancing Human Rights Accountability should never be sacrificed for efficiency. We need "meaningful human oversight". To keep automated decisions in line with human values and legal protections, we need clear "red lines" and ways to ensure that everyone can see what's going on. This will stop a "black box" approach to governance. The Bottom Line: Geneva needs to give us a useful toolkit, not just a statement.

As a consultant and auditor, I see a number of important "blind spots" that will appear in 2026 that the broad themes of safety or capacity building alone don't fully cover. To transition from philosophy to professional practice, we must confront the following: 1. Non-Human Identity and Agentic Autonomy We are quickly going from "assistants" to autonomous agents that can plan, carry out, and interact with financial systems (like UPI) on their own. Our current frameworks are designed to hold people accountable. We need standards for AI identity management right away. How do we check, credential, and "fire" a non-human agent that fails a compliance check? 2. Resilience of the environment and energy Because I have worked in the energy sector, I am worried about how little is said about AI's physical resource footprint. AI governance and climate governance are closely related because data centres consume a significant amount of water, and training large language models (LLMs) requires a substantial amount of energy. We need a "Green AI" reporting standard that makes developers responsible for the environmental impact of their work. 3. The "Auditor Chasm" We talk about "AI capacity building", but we don't talk about the human capital chasm in the professional services sector. There aren't enough certified professionals around the world who can do "fiduciary-grade" AI audits. The governance dialogue is still just a theoretical exercise without a way to enforce it, because there is no standardised global syllabus for AI auditing like ISO 27001. 4. The Source of the Supply Chain Modern AI is a "black box" built on top of other black boxes. For AI to keep track of data lineage and model weights, we need a Bill of Materials (SBOM). If we don't know where the data came from, we can't protect against "Data Poisoning" or make sure that a model used in a critical sector wasn't hacked during its training supply chain. Fixing these "mechanical" problems is what will turn a diplomatic conversation into a real global standard.

In India, the size of our digital transformation, as shown by the Unified Payments Interface (UPI), makes the AI governance gap a real risk instead of just a policy issue. 1. Impact on the sector: The "Security Theatre" risk Our most significant problem in the oil & gas and fintech sectors is that there are no standardised, interoperable controls. Without a global "ISMS for AI" (like ISO 27001), we could end up with "security theatre", which means using advanced systems that look strong but aren't really safe. India has the chance to set "fiduciary-grade" AI auditing standards for the Asia-Pacific region that protect important infrastructure. 2. The chance to go from "syllabus" to "system" There is a big gap in building AI skills. We graduate thousands of BTech and BBA students each year, but there is a gap between what they learn in school and their ability to audit complex models in real life. We can make India a global centre for AI governance professionals by filling this gap. This will give the "human-in-the-loop" oversight that international frameworks don't have right now. 3. Language and Cultural Independence From a social and technical point of view, using datasets that are focused on the West puts our linguistic diversity at risk. If AI governance doesn't put local data sovereignty first, we could end up with "digital colonialism", where models don't understand the complex situations in India's different states. The chance is to create localised, high-integrity models that make sure everyone can get to them without losing their cultural identity. 4. Human Rights and Responsibility The problem is making sure that efficiency doesn't come at the expense of human rights. Automated bias can take away the rights of millions of people in a country with a lot of people like ours. Success means going from "ethics-by-design" to "audit-by-design", where openness is a technical requirement that makes sure every automated decision is still accountable to a human authority. We can make sure that innovation drives growth without putting our systemic security at risk by treating governance as a functional toolkit instead of a legal barrier.

The Global Dialogue on AI Governance is the "Dialogue of Dialogues." It is the first universal platform where all 193 UN Member States can work together to create a single, international framework instead of separate principles. As an auditor and consultant, I see its role in promoting cooperation as being based on three main functions: 1. Making interoperability stableThe Dialogue is the "Rosetta Stone" for rules around the world. Instead of a broken "splinternet" of laws that don't work together, it encourages frameworks like the EU AI Act and regional projects in the Global South to work together. This makes sure that a security control or audit done in India is accepted and respected around the world, especially in fields like Fintech and Energy. 2. Making Scientific Evidence Standard: The Dialogue uses reports from the Independent International Scientific Panel on AI to replace political "hype" with assessments based on facts. This "IPCC for AI" model sets a common technical standard that lets countries work together on "red lines" for high-risk models and safety rules for important infrastructure. 3. Putting Capacity Building into Action Success isn't just about sharing best practices; it's also about moving resources from one place to another. The Dialogue is how the Global Fund for AI will work, making sure that the Global South has the computing power, data sovereignty, and professional training it needs to take part. This changes "capacity building" from a buzzword into a real course of study for the next generation.4. Getting to Auditability In the end, the Dialogue changes the subject from "Trust Me" to "Show Me." It gives a clear path for independent, third-party verification, making sure that human rights and transparency are not just moral standards, but also requirements that can be checked for any model used around the world.

To maximise impact, the AI dialogue must act as the System integrator" for existing, fragmented frameworks. We shouldn't reinvent the wheel; we should synchronise the following: 1. Connecting the Foundations ISO/IEC 42001 & 27001: These are the "blueprints". As an auditor, I see these as the golden standards for management systems. The dialogue should build on these to create a global "ISMS for AI," moving them from voluntary industry certifications to foundational regulatory requirements. Global Digital Compact (GDC): This is the policy anchor. The Dialogue acts as the mechanical arm of the GDC, taking its high-level goals and turning them into auditable technical protocols. GPAI and OECD: These organisations provide world-class research. The dialogue brings the universal mandate, ensuring that insights from these "clubs" are accessible and applicable to all 193 Member States. 2. The Added Value: From Principles to Practice The "Dialogue of Dialogues" offers three unique values that regional initiatives cannot provide: Universal Legitimacy: Unlike the G7 or the Bletchley/Seoul Summits, this forum is the first platform where the Global South has an equal seat. This prevents a "regulatory splinternet" and ensures standards respect diverse linguistic and cultural contexts. The "IPCC for AI" Model: By formalising the Independent International Scientific Panel on AI, it provides a shared technical truth. This is critical for sectors like fintech and energy, where we need evidence-based safety "red lines". Resource Operationalisation: It moves beyond policy to compute and capital. Through the Global Fund for AI, it creates a mechanism to provide the Global South with hardware and the professional training necessary to build high-integrity models. Success means turning these disparate initiatives into a functional, global toolkit for the next generation of professionals.

Contributions from Stakeholders: Technical auditors and the industry must provide the mechanical "controls". Their job is to turn broad ethical ideas into measurable, ISO-style compliance metrics. This ensures that AI systems in important areas like fintech or energy can be technically verified. Academia and training institutes need to close the "human capital gap". Instead of just doing academic research, they should also create standardised, hands-on syllabi. We need to make sure we have the right professionals to enforce global standards by giving candidates real-world examples and cyber-defence strategies. Civil society and local governments must protect linguistic and cultural sovereignty by making sure that global datasets don't leave out the unique aspects of life in the Global South. Suggestions for Structure and Format: Domain-Specific Working Groups: Organise conversations around high-risk areas such as digital payments and critical infrastructure. A single plenary can't cover the specific, systemic risks of different industries. Gap Analysis Sessions: These sessions should be like a formal certification audit and should focus on finding the exact "gaps" between current regional frameworks (like the EU AI Act) and the reality of using AI in developing countries. Technical Sandboxes: Include hands-on demonstrations where stakeholders can see how well things work together. Showing how digital watermarking or a cross-border AI security audit really works will keep negotiations grounded in technical reality. We ensure that the output is a functional, deployable toolkit rather than just a statement of intent by organising the dialogue as a full "management review" instead of a regular summit.

From my vantage point as an auditor and trainer, the "ground floor" of AI implementation is strikingly silent in global forums. We are missing the voices of those who must actually turn ethics into code. 1. The "Audit-Level" Practitioners Policy is often written by those who will never have to conduct a formal gap analysis. We need the perspectives of ISO-certified auditors and cybersecurity professionals who understand how "transparency" translates into a technical control. Inclusion means moving them from the audience to the drafting table to ensure frameworks are auditable, not just aspirational. 2. SMEs in the Global South Small-to-medium enterprises in emerging markets are the backbone of digital transformation but are often priced out of compliance. We should include them through regional regulatory sandboxes—safe environments where local firms can test AI models against international standards without the crushing overhead of "Big Tech" legal teams. 3. Linguistic and Cultural Custodians Linguistic diversity isn't just about translation; it's about context. We need active engagement from local language scholars and community leaders who can ensure that "alignment" doesn't inadvertently erase cultural nuance or local data sovereignty. 4. The "Next-Gen" Educators The trainers shaping candidates are usually not in the room. By including academic mentors from developing nations, we can align global policy with the actual skill sets being taught, ensuring a "Syllabus-to-System" pipeline that supports long-term governance. Inclusion isn't just a seat at a dinner; it's a role in the management review. By shifting from plenary speeches to technical workshops, we ensure these under-represented voices contribute to a functional, global toolkit that works for everyone, not just the technologically elite.

To move from high-level diplomacy to actionable results, the AI Dialogue should adopt formats that mirror the iterative, hands-on nature of professional auditing and systems engineering. As someone who balances global consulting with training the next generation, I recommend four innovative formats: 1. Regulatory "Sprints" (Policy Hackathons) Instead of static plenary sessions, we should host cross-border "sprints" where policymakers, technical auditors, and developers work in 48-hour cycles to prototype specific governance modules—such as a "Global AI Bill of Materials" or a standardised transparency report. This moves us from discussing ethics to building the mechanical tools for compliance. 2. Sectoral "Stress-Test" Simulations In sectors like fintech and energy, we need "war game" simulations. By running simulated AI failures in a controlled environment, stakeholders can identify where current governance "breaks". This provides the raw data needed to define evidence-based "red lines" and safety protocols that are grounded in technical reality, not theory. 3. Collaborative Gap Analysis Workshops Using a format similar to a formal ISO 27001 management review, these workshops would allow countries and entities to conduct a peer-reviewed "gap analysis" of their current frameworks. This fosters a non-confrontational environment for identifying where local regulations (like those in the Global South) diverge from international standards, facilitating the interoperability we desperately need. 4. Hybrid "Classroom-to-Dialogue" Links As a trainer, I see the value in connecting the dialogue directly to academic centres. Virtual "classroom dialogues" allow candidates to provide real-time feedback on policy drafts. This ensures that the "Syllabus-to-System" pipeline is robust and that the next generation of professionals is prepared to implement the standards we create today. Geneva should be a working laboratory, not just a stage. These formats ensure that the Dialogue produces a functional, auditable toolkit for the global community.

Effective governance isn't about restricting innovation; it's about providing the auditable guardrails that allow it to scale securely. From my perspective in global consulting and auditing, four approaches stand out as concrete solutions: 1. ISO/IEC 42001 (The AI Management System) Just as ISO 27001 revolutionised information security, ISO/IEC 42001 provides a gold standard for AI governance. It moves beyond ethics by mandating a structured risk management process. For sectors like oil & gas or fintech, this ensures that AI isn't a "black box" but a managed asset with clear accountability and performance metrics. 2. Multi-Sectoral Regulatory Sandboxes The "sandbox" model-pioneered in fintech by the RBI-is a best practice for AI. It allows firms to test high-risk models (like autonomous agents in digital payments) in a controlled environment with regulatory oversight. This "test-before-flight" approach identifies systemic vulnerabilities without stifling market entry. 3. AI Bill of Materials (A-SBOM) An A-SBOM, which is based on cybersecurity best practices, keeps track of where datasets, model weights, and third-party libraries come from. This provides the technical transparency needed for a formal gap analysis, ensuring that the AI supply chain is as secure as the code itself. 4. Integrated Capacity Building Governance is only as strong as the people enforcing it. A successful model integrates AI security into boardroom and university curricula. By teaching candidates how to perform "fiduciary-grade" AI audits and vulnerability assessments, we bridge the gap between academic theory and the professional reality of managing complex, cross-domain projects. By focusing on these functional toolkits, we move the needle from "aspirational" to "executable", ensuring that global AI ecosystems are safe, secure, and resilient.