The UN is shaping a Global Dialogue on AI Governance 🌍🤖 as part of the Global Digital Compact and UN General Assembly framework. It reflects a simple reality: AI is already transforming economies, societies, and rights — and no single country can govern it alone. Key focus areas under discussion include: • AI safety, security & trust 🔐 • Global access & capacity gaps 🌐 • Human rights, transparency & accountability ⚖️ The first formal session is planned for July 2026 in Geneva, with further sessions to follow. As noted by UN Secretary-General António Guterres, the question is whether we govern AI together — or let it govern us. This initiative sits within the work of the United Nations General Assembly to create shared global approaches to emerging technology. Are we ready for AI governance at scale? Find out more: www.securityatlas.ai #AIGovernance #AI #UN #TechPolicy #DigitalGovernance #SecurityAtlas
Security Atlas AI
Software Development
New York, NY 419 followers
AI Risk Intelligence for Secure Enterprise Adoption
About us
Security Atlas AI is an enterprise risk intelligence platform designed to help organizations safely adopt and scale artificial intelligence. As AI usage accelerates across the enterprise, security, legal, procurement, and product teams face growing pressure to evaluate tools quickly while maintaining strong governance. Security Atlas AI solves this challenge by providing a structured, evidence-based framework to assess AI solutions across security, compliance, legal, and operational risk. Security Atlas AI transforms fragmented AI evaluations into a centralized, measurable, and scalable governance process, helping enterprises reduce risk, prevent tool sprawl, and accelerate responsible AI innovation. Built for modern enterprises. Designed for governed AI at scale.
- Website
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https://securityatlas.ai/
External link for Security Atlas AI
- Industry
- Software Development
- Company size
- 2-10 employees
- Headquarters
- New York, NY
- Type
- Privately Held
- Specialties
- Artificial Intelligence Risk, Vendor Risk Analysis, AI Governance, Enterprise AI Implementations, and AI Tools Management
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Primary
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New York, NY 10019, US
Employees at Security Atlas AI
Updates
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🚨 The EU AI Act is coming this August! Are you ready? This landmark regulation will shape how AI is developed, deployed, and governed across Europe. Compliance isn’t optional—it’s the new baseline for responsible AI. ⚖️🤖 Prepare now to align your AI systems with the rules, manage risk, and build trust. The future of AI governance is here. 🌐 #EUAIACT #AIRegulation #AIGovernance #ResponsibleAI #Compliance #TechLeadership #Innovation #AI #Europe
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A single risk score sounds simple — but AI governance isn’t. Different signals matter independently: 📊 Business value ⚠️ Initial risk 🔒 Enterprise readiness Blending them into one number can hide important trade-offs. Separation creates clarity. Clarity enables better decisions. #AI #Governance #SecurityLeadership #Risk #Procurement #AICompliance 🧠
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Why AI Risk Feels So Hard Right Now Most teams aren’t struggling because they lack expertise. They’re struggling because AI tools don’t fit legacy review processes. 📌 Too many vendors 📌 Too many frameworks (GDPR, EU AI Act, SOC 2…) 📌 Too little standardisation AI governance needs to be continuous, not one-time. The question is: Are your current processes built for AI — or just adapted for it? #AI #Governance #Infosec #Privacy #EUAIAct #Security 🛡️
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AI isn’t just an IT concern anymore. It’s moving into: 🏢 Procurement decisions ⚖️ Legal accountability 🔐 Security posture 📈 Business strategy Which means governance needs to be: ✔ Structured ✔ Repeatable ✔ Auditable The organisations that get this right early will scale AI with far less friction. #AI #EnterpriseAI #Governance #BoardLevel #RiskManagement #FutureOfWork 🚀
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Another insightful piece from Chris Hood—his newsletter continues to help demystify AI governance, a topic that can still feel ambiguous even for those working closely in the space. If you haven’t yet assessed whether your AI usage introduces governance or risk considerations, this is a useful starting point.
The number one question in AI governance is: who is accountable? We already have the answer. A human. Pick one. Every scenario being argued about AI, agents, agentic systems, automation tools, complex software systems, or software in general, traces back to a human. Every single one. One hundred percent of the time. The same organization that spent six months onboarding Workday with a standardized RFP process, security review, legal validation, and ownership, allowed an AI tool with production access to customer data to enter through a browser tab and a credit card. AI is software. The accountability structures already exist. Some organizations just simply forgot to apply the same policies. #AIGovernance #Software #management #AI
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Three Layers of AI Risk (That Most Teams Miss) AI vendor risk isn’t one-dimensional. It typically sits across three layers: 1️⃣ Permanent controls (security fundamentals) 2️⃣ Regulatory controls (compliance frameworks) 3️⃣ Dynamic intelligence (vendor-specific change over time) The challenge isn’t identifying risk once — it’s keeping up with how quickly it changes. Continuous visibility is becoming essential. #RiskManagement #AIGovernance #CyberSecurity #DataProtection #EnterpriseAI 📊
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Really compelling framing—especially the idea that judgement can be present in the process but still not land where decisions are actually made.
The AI summary was coherent. The context note was acknowledged. The judgement still didn’t land. Salus knew what the AI summary was missing. She could feel it before she could name it. The summary was coherent. The indicators were real. The tool was approved. And the pattern it had made from those facts was wrong in a way the meeting would not have the time or the frame to work out. She added a context note. She went in. She named what she knew. The case moved anyway. No one in the room had acted carelessly. No one ignored her. The note was acknowledged. Her judgement entered the room as context, not as force. This is the contradiction I keep seeing in practice. AI governance has become much better at assigning accountability. It can name who reviews, who signs off, who escalates. What it still struggles to tell us is whether judgement is functioning where that accountability has been placed. A policy is in place. A person is present, on the record, formally responsible. And yet the conditions that would let that presence matter as judgement may already have thinned. I’m calling that gap Judgement Integrity. Not a virtue or a personal quality, but a property of the system: whether it still preserves the chain by which judgement forms, surfaces, acts, and travels back into learning. When that chain starts to erode, the pattern is often recognisable. Judgement can become hollowed, compressed, displaced, or absorbed. The article develops those patterns, the distinctions between them, and what it takes to diagnose them in practice. The hardest question underneath all of it is the one I first heard in Salus’s room in another form: If invisible human compensation stopped tomorrow — the quiet widening of context, the backstage slowing down, the private carrying of what sign-off didn’t resolve — would this system’s oversight still hold? 💙 Co-weave invite: Where in your context is judgement losing force, losing room, losing a stable home, or settling into people?