Synergy Compliance Consulting (SCC)

Three outcomes would make it a genuine success. First, convergence on a baseline governance architecture. Not one global regulation that is neither realistic nor desirable. But agreement on what every responsible AI governance framework must address: risk classification, human oversight, transparency obligations, and accountability chains. ISO 42001 offers a strong foundation. The Dialogue should acknowledge and build on it, rather than reinvent the wheel. Second, meaningful inclusion of the Global South. AI governance shaped exclusively by the EU, the US, and major tech powers will be imposed on, not adopted by, the rest of the world. Developing nations , including Lebanon and the broader Arab region , need a genuine seat at the table, not a consultation afterthought. Third, a clear signal to the private sector. Organisations are investing in AI today. They cannot wait years for policy certainty. The Dialogue should produce actionable guidance that businesses, auditors, and regulators can reference now not aspirational language that dissolves into ambiguity.

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

My four priorities reflect both my professional practice and the realities I observe across the MENA region. Safe, secure and trustworthy AI is the foundation. Without it, every other governance ambition collapses. As an ISO 42001 Lead Auditor, I see organisations deploying AI systems without the controls, risk assessments, or accountability structures that "trustworthy" actually demands. Safety is not a feature , it is a governance obligation. AI capacity-building is equally urgent, particularly for developing nations. Governance frameworks are only as effective as the people implementing them. Lebanon and the broader Arab region lack sufficient auditors, policymakers, and practitioners trained in AI governance. Closing that skills gap is a prerequisite for everything else. Interoperability of governance approaches is where I spend considerable consulting effort. My clients operate across jurisdictions with conflicting requirements : ISO 42001, the EU AI Act, emerging national frameworks. Fragmentation creates compliance paralysis. The Dialogue must advance mutual recognition and alignment, not produce yet another standalone framework. Transparency, accountability, and human oversight completes the picture. These are not soft values , they are auditable controls. Without them, AI governance remains a policy aspiration rather than a verifiable practice. These four priorities, taken together, build a governance ecosystem that is functional, inclusive, and enforceable.

Yes. Three issues deserve explicit recognition. Agentic AI governance. The listed themes were largely conceived with predictive or generative AI in mind. Agentic AI systems , those that plan, act, and chain decisions autonomously across tools and environments introduce a fundamentally different risk profile. Existing frameworks, including ISO 42001, do not yet adequately address accountability when no single human authorises each action. This is not a future concern. Organisations are deploying agentic systems today. AI governance in conflict-affected and fragile states. The Dialogue risks producing frameworks calibrated to stable, well-resourced institutions. Countries emerging from conflict , including Lebanon , face AI governance challenges that are structurally different: collapsed institutional capacity, data sovereignty vulnerabilities, and foreign-operated AI systems filling governance vacuums. These contexts require tailored guidance, not scaled-down versions of OECD frameworks. The distinction between AI governance and AI ethics. These terms are used interchangeably in too many policy documents. Governance is operational, auditable, and enforceable. Ethics is foundational but insufficient alone. Conflating the two produces frameworks that feel comprehensive on paper but deliver no verifiable accountability in practice. The Dialogue should establish this distinction clearly and build its architecture accordingly.

The MENA region is at a crossroads. AI adoption is accelerating across financial services, public administration, healthcare, and logistics while governance infrastructure remains critically underdeveloped. That gap is not abstract. It has operational consequences. The most significant challenge is institutional readiness. Most organisations in Lebanon and the wider Arab region have no formal AI governance structure. They lack trained auditors, documented risk frameworks, and regulatory clarity. When clients ask me whether they should pursue ISO 42001 certification, the honest answer is that many lack the foundational management systems to support it. Governance ambition is outpacing governance capacity. Regulatory fragmentation compounds the problem. Organisations with regional or international exposure face conflicting signals : EU AI Act obligations, Gulf national AI strategies, and global standards like ISO 42001 that no regulator yet mandates. The result is compliance paralysis or, worse, selective compliance that creates false assurance. The opportunity is real, however. The MENA region has a chance to leapfrog to build AI governance frameworks that are interoperable, standards-based, and fit for its specific legal, cultural, and linguistic context, rather than simply importing Western models wholesale. That requires investment in local capacity, Arabic-language governance tooling, and regional regulatory dialogue. The Global Dialogue can catalyse exactly that , if it chooses to.

The AI Dialogue occupies a position no standards body, regional bloc, or bilateral agreement currently holds a genuinely universal forum. That is its distinctive value, and it should use it deliberately. Three roles matter most. Arbitrator of convergence. The governance landscape is fragmenting faster than organisations can absorb. ISO 42001, the EU AI Act, the US Executive Order, Gulf national strategies , each claims legitimacy, none speaks to the others coherently. The Dialogue can establish a shared reference architecture

Several initiatives deserve explicit recognition and active connection. ISO/IEC 42001:2023 is the most operationally mature AI governance framework currently available. It provides auditable controls, a risk-based management system structure, and certification pathways that organisations can implement today. The Dialogue should reference it explicitly rather than treating standards as a technical afterthought. The OECD AI Principles and the Global Partnership on AI (GPAI) have produced substantive analytical work on trustworthy AI. Rather than duplicating that research, the Dialogue should build directly on it with particular attention to GPAI's responsible AI working group outputs. The EU AI Act represents the most comprehensive binding regulatory framework to date. Its risk classification architecture offers a replicable model, even for jurisdictions not subject to it. The Dialogue can help non-EU nations adapt not adopt its logic. UNESCO's Recommendation on the Ethics of AI brings cultural and linguistic diversity into the governance conversation in a way that purely technical frameworks do not. The added value the Dialogue uniquely brings is integration. Each of these initiatives operates in relative isolation. None has the convening authority to connect them into a coherent, interoperable governance ecosystem. That is precisely the gap the Dialogue can fill provided it prioritises architecture over another declaration.

Effective dialogue requires deliberate design. Good intentions without structured participation produce declarations, not outcomes. On stakeholder contributions, each constituency brings something irreplaceable. Governments bring regulatory authority and political commitment. Standards bodies bring operational frameworks that survive political cycles. The private sector brings deployment reality what governance looks like when it meets actual AI systems under commercial pressure. Civil society brings the human rights and equity dimensions that technical actors consistently underweight. Practitioners and auditors , people implementing governance on the ground , bring the gap analysis that policymakers rarely see. All four must be present as contributors, not audiences. On format, the Dialogue should resist the temptation to default to plenary declarations. Three structural recommendations: First, working groups organised by thematic priority, with clear mandates, diverse membership, and published outputs not just summary statements. Second, a practitioner track running alongside the governmental track. Implementation experience must inform policy design in real time, not be consulted retrospectively. Third, a structured follow-up mechanism with named focal points and a defined review cycle. Without it, the Dialogue produces a document. With it, it produces a process. The measure of success is not how many stakeholders attended. It is whether the outputs are still being referenced and acted upon twelve months later.

The underrepresentation is structural, not accidental. Correcting it requires deliberate design, not goodwill statements. The Arab world and the broader Global South are the most significant absence. AI governance conversations are dominated by the EU, the US, the UK, and a handful of Asian economies. The Arab region with its distinct legal traditions, linguistic context, and governance maturity curve is treated as a recipient of frameworks, not a shaper of them. That must change. Conflict-affected and fragile states are effectively invisible in these discussions. Yet they face the sharpest AI governance risks , foreign-operated systems, collapsed oversight institutions, and data vulnerabilities that stable democracies never encounter. Practitioners and implementers , auditors, consultants, compliance officers are routinely excluded from policy design despite being the people who translate governance commitments into operational reality. Their absence explains why so many frameworks are theoretically coherent but practically unworkable. Linguistic minorities deserve explicit attention. AI governance documents produced exclusively in English, French, or Mandarin are inaccessible to the communities most vulnerable to ungoverned AI deployment. Inclusion mechanisms must go beyond translation. They require funded participation, regional preparatory consultations, dedicated working group seats, and outputs that reflect non-Western governance priorities not merely acknowledge them.

Most multilateral dialogues fail not because of bad ideas but because of bad formats. The standard cycle , keynote, panel, plenary, declaration produces consensus language that no one owns and everyone forgets. Three formats would genuinely change the dynamic. Governance stress-testing sessions. Present real AI deployment scenarios , an autonomous hiring system in a Gulf state, a predictive policing tool in a fragile democracy, an LLM-powered medical triage system in an under-resourced hospital and require mixed stakeholder groups to apply existing frameworks in real time. The gaps that surface are more instructive than any gap analysis paper. Red team tracks. Invite practitioners and civil society specifically to challenge proposed governance outputs before adoption not as critics on a panel, but as structured adversaries with time, mandate, and a formal response mechanism. Good governance frameworks should survive scrutiny. Build the scrutiny in. Asynchronous regional input channels. Not every meaningful voice can travel to New York or Geneva. Structured digital consultation periods with genuine synthesis into Dialogue outputs, not appendix acknowledgements , would dramatically widen participation without inflating the event itself. The format should match the ambition. If the goal is a living governance process, the engagement design

Several examples stand out , not as perfect models, but as instructive ones. ISO/IEC 42001:2023 remains the most operationally credible AI governance framework available. It is auditable, certifiable, and management-system-based meaning organisations can integrate it with existing ISO 9001 or ISO 27001 structures rather than building governance infrastructure from scratch. In my consulting practice, it is the most actionable starting point for organisations serious about governance beyond policy statements. The EU AI Act's risk classification architecture offers a replicable logic that non-EU regulators can adapt. Tiering obligations by risk level rather than applying uniform requirements to every AI application is pragmatic and proportionate. Singapore's Model AI Governance Framework demonstrates that effective governance does not require binding legislation to deliver practical value. It is principles-based, sector-sensitive, and implementable. The NIST AI Risk Management Framework provides a structured vocabulary bridging technical and governance communities , a genuine contribution to the interoperability challenge. On platforms, VerifyWise , an open-source AI governance platform , offers organisations a concrete tool for tracking compliance against ISO 42001 and EU AI Act requirements. OECD.AI Policy Observatory aggregates global AI policy developments into a single accessible reference point, reducing duplication across national efforts. Atlas of AI Governance initiatives emerging from academic institutions provide comparative visibility across jurisdictions that policymakers currently .