The Future of AI Governance

"The rapid evolution and swift adoption of generative AI have prompted governments to keep pace and prepare for future developments and impacts. Policy-makers are considering how generative artificial intelligence (AI) can be used in the public interest, balancing economic and social opportunities while mitigating risks. To achieve this purpose, this paper provides a comprehensive 360° governance framework: 1 Harness past: Use existing regulations and address gaps introduced by generative AI. The effectiveness of national strategies for promoting AI innovation and responsible practices depends on the timely assessment of the regulatory levers at hand to tackle the unique challenges and opportunities presented by the technology. Prior to developing new AI regulations or authorities, governments should: – Assess existing regulations for tensions and gaps caused by generative AI, coordinating across the policy objectives of multiple regulatory instruments – Clarify responsibility allocation through legal and regulatory precedents and supplement efforts where gaps are found – Evaluate existing regulatory authorities for capacity to tackle generative AI challenges and consider the trade-offs for centralizing authority within a dedicated agency 2 Build present: Cultivate whole-of-society generative AI governance and cross-sector knowledge sharing. Government policy-makers and regulators cannot independently ensure the resilient governance of generative AI – additional stakeholder groups from across industry, civil society and academia are also needed. Governments must use a broader set of governance tools, beyond regulations, to: – Address challenges unique to each stakeholder group in contributing to whole-of-society generative AI governance – Cultivate multistakeholder knowledge-sharing and encourage interdisciplinary thinking – Lead by example by adopting responsible AI practices 3 Plan future: Incorporate preparedness and agility into generative AI governance and cultivate international cooperation. Generative AI’s capabilities are evolving alongside other technologies. Governments need to develop national strategies that consider limited resources and global uncertainties, and that feature foresight mechanisms to adapt policies and regulations to technological advancements and emerging risks. This necessitates the following key actions: – Targeted investments for AI upskilling and recruitment in government – Horizon scanning of generative AI innovation and foreseeable risks associated with emerging capabilities, convergence with other technologies and interactions with humans – Foresight exercises to prepare for multiple possible futures – Impact assessment and agile regulations to prepare for the downstream effects of existing regulation and for future AI developments – International cooperation to align standards and risk taxonomies and facilitate the sharing of knowledge and infrastructure"

🌐 The Future of AI Governance & Power: What’s Next in 5 Years? As AI advances, the challenge of establishing global governance is becoming more critical. The UN AI Advisory Body recently outlined key recommendations for global AI governance, but achieving a true global framework remains difficult. Can we expect global AI governance, or will regional powers set their own rules? Fragmented AI Governance Landscape AI governance will likely remain fragmented, with major powers like the EU, US, and China driving their own standards. The EU’s AI Act, focusing on responsible AI development, may impact global markets through the “Brussels Effect,” while China is pushing its own “Beijing Effect” across Belt and Road nations, standardizing AI to its specifications. Meanwhile, the US could see the “California Effect” influence tech companies, potentially increasing guardrails on AI services, but these effects will vary by region. AI Compute and Power Challenges A critical challenge is the growing demand for compute power and energy. Training AI models like large language models requires enormous computational resources, creating barriers for smaller nations and organizations. Data centers hosting AI models consume increasing amounts of electricity, often from non-renewable sources, making this growth unsustainable without breakthroughs in energy-efficient AI. Compute and Energy Inequality Over the next five years, nations with access to high-performance computing (HPC) and vast energy resources (e.g., US, China) will dominate AI innovation, while others may fall behind. The global chip shortage and rising energy costs will further exacerbate this divide. Countries without affordable access to energy and HPC infrastructure will be left out of the AI revolution, widening the gap between AI leaders and laggards. Sustainability and AI’s Carbon Footprint AI’s carbon footprint is also growing. Training large AI models is energy-intensive, leading to increased pressure from governments for more sustainable solutions. Companies that innovate in green AI will have an edge, but transitioning to more energy-efficient AI will not be immediate, and the environmental impact may slow AI adoption in regions with strict environmental policies. The AI Arms Race Looking forward, the next five years will likely see an “AI arms race” where nations and companies compete for leadership in compute power, energy efficiency, and governance. Regions like the EU will push for ethical AI governance, while countries like the US and China will focus on scaling AI through advances in computational resources. In the absence of a unified global AI governance framework, those that balance innovation with sustainability, energy efficiency, and responsible governance will lead the way forward. #AI #AIgovernance #Sustainability #AIpower #Innovation #ArtificialIntelligence #AIfuture

📚 I've been teaching Foreign Policy & AI to diplomats across the world and I always start with that now-famous 2017 moment when Putin said: "𝘞𝘩𝘰𝘦𝘷𝘦𝘳 𝘭𝘦𝘢𝘥𝘴 𝘪𝘯 𝘈𝘐 𝘸𝘪𝘭𝘭 𝘳𝘶𝘭𝘦 𝘵𝘩𝘦 𝘸𝘰𝘳𝘭𝘥." Naturally, people ask: So where has Russia been on the AI front since then? 🤔 Russia’s AI ambitions are not dead - they’ve just found a new stage. Moscow has turned to the BRICS bloc, whose founding members include #Brazil, #Russia, #India, #China, and #SouthAfrica, to build a parallel AI ecosystem. Here's what I’ve been reflecting on: 🤖 Russia has adopted the 2021 National Security Strategy, emphasizing the role of advanced technologies, including #AI, in strengthening #nationaldefense and #economic resilience. 🪆 The Ministry of Foreign Affairs’ 2023 Concept of the Foreign Policy highlights AI growth and deeper BRICS cooperation. 🧠 Russia sees AI as a pillar of its long-term global strategy. Despite sanctions and brain drain, it’s doubling down on #AI via #BRICS cooperation. 🌐 BRICS has become Moscow’s AI sandbox. What began as a geopolitical bloc is morphing into a tech and governance alliance, with AI at the center. 📈 BRICS now makes up 35% of the global economy, and with new members like UAE, Iran, Egypt, and Ethiopia, it’s evolving into a parallel AI ecosystem, beyond Western influence. BRICS introduced some significant AI governance efforts: 🔹 Established an AI Study Group to “develop AI governance frameworks and standards 🔹 Russia led the creation of the BRICS AI Alliance - a strategic initiative promoting collaborative joint research and regulation.  🔹 Advocated for the BRICS adoption of "Russia's Code of AI Ethics", signaling a clear Russian leadership in the AI governance space. 🔹 Building partnerships to deploy Russian/Chinese AI infrastructure in the Global South. 🔹 Encouraging BRICS nations to shift away from OpenAI and U.S.-centric models - 100 of the largest companies in BRICS nations are shifting away from Western models like OpenAI, toward emerging Chinese, Russian, and Emirati models. 💥 Recent moves include: 👉 A Russia–China Joint Declaration on AI Cooperation. 👉 A strategic AI pact with Iran. 👉 BRICS’ own AI Study Group, with ambitions to define global standards. 💡 BRICS is no longer just a diplomatic club. It's a strategic AI force — and we need to treat it as such. #AI #ForeignPolicy #BRICS #AIforPeace #Geopolitics #AIgovernance #TechDiplomacy #Russia #China #GlobalSouth #ArtificialIntelligence #InternationalRelations #DigitalSovereignty #DemocracyAndTech #AIAlliance

🌍 Can ICANN's Multistakeholder Model Open the Way to AI Governance? As AI rapidly reshapes the world, the question of its governance becomes more important. In my most recent article, I look at OpenAI CEO Sam Altman's recent Washington Post Op-ed, "Who will control the future of AI?" in which he references ICANN's multistakeholder model as a template for global AI governance, and drawing on my experience with ICANN's community and Internet governance processes, I explore: 🔹 Strengths and Challenges of ICANN's Multistakeholder Approach 🔹 Key distinctions in Internet and AI governance 🔹 Potential advantages and disadvantages of adapting ICANN's model to AI 🔹 The geopolitical complexities of creating a global AI governance body based on the ICANN model. ICANN's two-decade journey can provide valuable lessons. However, can this model address the unique challenges presented by AI? Given AI's far-reaching implications for national security and socioeconomic impact, governments are unlikely to accept or give control to a global multistakeholder AI governance body. Unlike the Internet, where the United States government played a sole role in ICANN's formation, AI governance will need more complex diplomacy and broad government buy-in and participation to ensure its legitimacy and acceptance. 🚀 Read the full article to dive into the possibilities, potential benefits, and challenges of a multistakeholder approach to AI governance. I would love to hear your thoughts! #AIGovernance #AIPolicy #AI #ICANN #Multistakeholder #InternetGovernance #ArtificialIntelligence #TechPolicy #ResponsibleAI #United Nations #UN #DigitalEthics #GlobalCollaboration #InternetGovernance #TechPolicy #EthicalAI #MultistakeholderModel #DigitalTransformation #GlobalGovernance #Innovation #AIRegulation #TechEthics #AILeadership #FutureOfAI #TechForGood #GovernanceFrameworks

In my work with organizations rolling out AI and generative AI solutions, one concern I hear repeatedly from leaders, and the c-suite is how to get a clear, centralized “AI Risk Center” to track AI safety, large language model's accuracy, citation, attribution, performance and compliance etc. Operational leaders want automated governance reports—model cards, impact assessments, dashboards—so they can maintain trust with boards, customers, and regulators. Business stakeholders also need an operational risk view: one place to see AI risk and value across all units, so they know where to prioritize governance. One of such framework is MITRE’s ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) Matrix. This framework extends MITRE ATT&CK principles to AI, Generative AI, and machine learning, giving us a structured way to identify, monitor, and mitigate threats specific to large language models. ATLAS addresses a range of vulnerabilities—prompt injection, data leakage, malicious code generation, and more—by mapping them to proven defensive techniques. It’s part of the broader AI safety ecosystem we rely on for robust risk management. On a practical level, I recommend pairing the ATLAS approach with comprehensive guardrails - such as: • AI Firewall & LLM Scanner to block jailbreak attempts, moderate content, and detect data leaks (optionally integrating with security posture management systems). • RAG Security for retrieval-augmented generation, ensuring knowledge bases are isolated and validated before LLM interaction. • Advanced Detection Methods—Statistical Outlier Detection, Consistency Checks, and Entity Verification—to catch data poisoning attacks early. • Align Scores to grade hallucinations and keep the model within acceptable bounds. • Agent Framework Hardening so that AI agents operate within clearly defined permissions. Given the rapid arrival of AI-focused legislation—like the EU AI Act, now defunct  Executive Order 14110 of October 30, 2023 (Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence) AI Act, and global standards (e.g., ISO/IEC 42001)—we face a “policy soup” that demands transparent, auditable processes. My biggest takeaway from the 2024 Credo AI Summit was that responsible AI governance isn’t just about technical controls: it’s about aligning with rapidly evolving global regulations and industry best practices to demonstrate “what good looks like.” Call to Action: For leaders implementing AI and generative AI solutions, start by mapping your AI workflows against MITRE’s ATLAS Matrix. Mapping the progression of the attack kill chain from left to right - combine that insight with strong guardrails, real-time scanning, and automated reporting to stay ahead of attacks, comply with emerging standards, and build trust across your organization. It’s a practical, proven way to secure your entire GenAI ecosystem—and a critical investment for any enterprise embracing AI.

What a week in AI & it’s only getting started ! The “DeepSeek sell-off” is this week’s  headline, but the real story goes deeper. AI’s true value has never been about raw model power alone—it’s about how AI is applied and governed to drive real business outcomes. This week confirmed what we at Credo AI have believed : AI is moving up the stack to enterprise adoption. The cost of cutting-edge models is plummeting, open-source innovation is accelerating, and AI proliferation is now inevitable. But with this acceleration comes a fundamental shift: governance is no longer a distant concern—it is now a core business imperative. Three Urgent Truths About AI’s Future 🔹 Every enterprise must own its AI governance. The era of centralized AI control is ending. Enterprises will no longer just consume AI ; they must govern it at the use case level—determining how AI is applied, ensuring compliance, and aligning it with their values. The ability to balance innovation , risk, accountability, and business outcomes will define the real winners of this AI revolution. 🔹 AI without governance is instability at scale. DeepSeek’s cyberattack underscores an uncomfortable reality: as AI becomes more accessible, the risks compound. We’ve entered an era where power without trust doesn’t lead to progress—it leads to chaos. AI governace, security, and alignment cannot be afterthoughts especially for enterprises investing in AI. 🔹 Governance isn’t a constraint—it’s the unlock. AI’s true potential won’t be realized unless organizations can deploy it with confidence, managing risk and ensuring compliance. Without governance, AI remains a promising experiment. With it, AI becomes a force multiplier for business transformation. ⭐️The Real AI Revolution and story : Trust at Scale AI’s rapid commoditization is shifting the conversation from capability to consequence. I believe the future of AI won’t be determined only by who builds the fastest models —but by who ensures those models are governed, aligned, and effective in the real world. AI’s future isn’t just about innovation—it’s about trust. Imagine the transformative possibilities ahead if governance and responsible AI use are at the core. This is the real opportunity. If governed, imagine what could go right with AI and all the better futures we will unlock.  👋This is where Credo AI can help you manage risk, ensure alignment with your organization goals, ensure oversight and accountability to power AI enablement. Reach out today ! www.credo.ai

To all Executives looking to build AI systems responsibly, Yoshua Bengio and a team of 100+ of AI Advisory Experts from more than 30 countries recently published the International AI Safety Report 2025, consisting of ~300 pages of insights. Below is a TLDR (with the help of AI) of the content of the document that you should pay attention to, including risks and mitigation strategies, as you continuously deploy new AI-powered experiences for your customers. 🔸AI Capabilities Are Advancing Rapidly: • AI is improving at an unprecedented pace, especially in programming, scientific reasoning, and automation • AI agents that can act autonomously with little human oversight are in development • Expect continuous breakthroughs, but also new risks as AI becomes more powerful 🔸Key Risks for Businesses and Society: • Malicious Use: AI is being used for deepfake scams, cybersecurity attacks, and disinformation campaigns • Bias & Unreliability: AI models still hallucinate, reinforce biases, and make incorrect recommendations, which could damage trust and credibility • Systemic Risks: AI will most likely impact labor markets while creating new job categories, but will increase privacy violations, and escalate environmental concerns • Loss of Control: Some experts worry that AI systems may become difficult to control, though opinions differ on how soon this could happen 🔸Risk Management & Mitigation Strategies: • Regulatory Uncertainty: AI laws and policies are not yet standardized, making compliance challenging • Transparency Issues: Many companies keep AI details secret, making it hard to assess risks • Defensive AI Measures: Companies must implement robust monitoring, safety protocols, and legal safeguards • AI Literacy Matters: Executives should ensure that teams understand AI risks and governance best practices 🔸Business Implications: • AI Deployment Requires Caution. Companies must weigh efficiency gains against potential legal, ethical, and reputational risks • AI Policy is Evolving. Companies must stay ahead of regulatory changes to avoid compliance headaches • Invest in AI Safety. Companies leading in ethical AI use will have a competitive advantage • AI Can Enhance Security. AI can also help detect fraud, prevent cyber threats, and improve decision-making when used responsibly 🔸The Bottom Line • AI’s potential is massive, but poor implementation can lead to serious risks • Companies must proactively manage AI risks, monitor developments, and engage in AI governance discussions • AI will not “just happen.” Human decisions will shape its impact. Download the report below, and share your thoughts on the future of AI safety! Thanks to all the researchers around the world who took created this report and took the time to not only surface the risks, but provided actionable recommendations on how to address them. #genai #technology #artificialintelligence

💡Anyone in AI or Data building solutions? You need to read this. 🚨 Advancing AGI Safety: Bridging Technical Solutions and Governance Google DeepMind’s latest paper, "An Approach to Technical AGI Safety and Security," offers valuable insights into mitigating risks from Artificial General Intelligence (AGI). While its focus is on technical solutions, the paper also highlights the critical need for governance frameworks to complement these efforts. The paper explores two major risk categories—misuse (deliberate harm) and misalignment (unintended behaviors)—and proposes technical mitigations such as:   - Amplified oversight to improve human understanding of AI actions   - Robust training methodologies to align AI systems with intended goals   - System-level safeguards like monitoring and access controls, borrowing principles from computer security  However, technical solutions alone cannot address all risks. The authors emphasize that governance—through policies, standards, and regulatory frameworks—is essential for comprehensive risk reduction. This is where emerging regulations like the EU AI Act come into play, offering a structured approach to ensure AI systems are developed and deployed responsibly.  Connecting Technical Research to Governance:   1. Risk Categorization: The paper’s focus on misuse and misalignment aligns with regulatory frameworks that classify AI systems based on their risk levels. This shared language between researchers and policymakers can help harmonize technical and legal approaches to safety.   2. Technical Safeguards: The proposed mitigations (e.g., access controls, monitoring) provide actionable insights for implementing regulatory requirements for high-risk AI systems.   3. Safety Cases: The concept of “safety cases” for demonstrating reliability mirrors the need for developers to provide evidence of compliance under regulatory scrutiny.   4. Collaborative Standards: Both technical research and governance rely on broad consensus-building—whether in defining safety practices or establishing legal standards—to ensure AGI development benefits society while minimizing risks. Why This Matters:   As AGI capabilities advance, integrating technical solutions with governance frameworks is not just a necessity—it’s an opportunity to shape the future of AI responsibly. I'll put links to the paper below. Was this helpful for you? Let me know in the comments. Would this help a colleague? Share it. Want to discuss this with me? Yes! DM me. #AGISafety #AIAlignment #AIRegulations #ResponsibleAI #GoogleDeepMind #TechPolicy #AIEthics #3StandardDeviations

Concerned about agentic AI risks cascading through your system? Consider these emerging smart practices which adapt existing AI governance best practices for agentic AI, reinforcing a "responsible by design" approach and encompassing the AI lifecycle end-to-end: ✅ Clearly define and audit the scope, robustness, goals, performance, and security of each agent's actions and decision-making authority. ✅ Develop "AI stress tests" and assess the resilience of interconnected AI systems ✅ Implement "circuit breakers" (a.k.a kill switches or fail-safes) that can isolate failing models and prevent contagion, limiting the impact of individual AI agent failures. ✅ Implement human oversight and observability across the system, not necessarily requiring a human-in-the-loop for each agent or decision (caveat: take a risk-based, use-case dependent approach here!). ✅ Test new agents in isolated / sand-box environments that mimic real-world interactions before productionizing ✅ Ensure teams responsible for different agents share knowledge about potential risks, understand who is responsible for interventions and controls, and document who is accountable for fixes. ✅ Implement real-time monitoring and anomaly detection to track KPIs, anomalies, errors, and deviations to trigger alerts.