Security Atlas brings order to AI governance, unifying the full vendor lifecycle into one powerful platform. From risk assessment and vendor intelligence to compliance mapping and intake automation, it evaluates every AI tool with structured scoring, verification layers, and governance workflows that replace fragmented, manual approval processes with clarity and speed. 🚀 Portfolio dashboard, three-score framework, and multi-tier verification deliver board-ready insight 🔒 #AIgovernance #SecurityAtlas https://securityatlas.com for enterprise AI safety today
Security Atlas AI
Software Development
New York, NY 300 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
- SocialCommunity and Lifestyle
Locations
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New York, NY 10019, US
Employees at Security Atlas AI
Updates
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Most AI governance frameworks are still built on legacy IT assumptions — and that’s no longer fit for purpose. Security Atlas AI takes a different approach: an AI-native governance framework built specifically for AI vendor risk, not retrofitted from static questionnaires. Traditional vs Security Atlas AI: • Static questionnaires → 🧠 Layered intelligence model • One-time reviews → 🔄 Continuous validation • No AI structure → 🤖 AI-native governance framework • No control classification → 📊 Permanent / Regulatory / Dynamic split • No comparison view → ⚖️ Side-by-side vendor intelligence • No prioritisation → 🎯 Three-score risk system This isn’t an upgrade — it’s a redesign of how AI vendor risk is governed. As AI adoption accelerates, governance must move from static checks to continuous, intelligence-driven oversight. Learn more: https://securityatlas.ai
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Enterprises in 2026 don’t rely on a universal AI vendor risk score, instead using context-specific frameworks where data governance dominates, binary compliance “gates” (like BAAs, FedRAMP and no-training clauses) override weighted scoring, and strong governance increasingly accelerates rather than slows AI adoption.
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A decade on, AlphaGo’s “Move 37” still stands as a powerful reminder that true breakthroughs often come from stepping beyond human intuition into entirely new ways of thinking—shaping the foundation for the AI systems we’re building today.
Ten years ago, AlphaGo’s legendary match in Seoul heralded the start of what is now recognised as the modern era in AI. In 2016, with over 200 million people watching, our AI system AlphaGo faced world champion Go player Lee Sae Dol. The match was defined by AlphaGo’s famous ‘Move 37’ in Game 2 - a play so unconventional it first appeared to be a mistake. But as the game unfolded, it became clear the play wasn’t just bold, it was decisive. One hundred or so moves later, Move 37 was in exactly the right place to decide the battle and allow AlphaGo to win the game. I knew at that moment that the AI techniques we developed with AlphaGo were ready to be applied to our real goal of using AI to accelerate scientific breakthroughs. The trajectory since then has been incredible: • 𝗔𝗹𝗽𝗵𝗮𝗭𝗲𝗿𝗼: Taught itself from scratch to master any 2-player perfect information game, including Go, chess and shogi. • 𝗔𝗹𝗽𝗵𝗮𝗙𝗼𝗹𝗱: Solved the 50-year grand challenge of protein structure prediction and is now a standard tool for millions of scientists around the world. • 𝗔𝗹𝗽𝗵𝗮𝗣𝗿𝗼𝗼𝗳 & 𝗔𝗹𝗽𝗵𝗮𝗘𝘃𝗼𝗹𝘃𝗲: Applying AlphaGo’s ‘reasoning as search’ to formal mathematics and algorithm discovery. • 𝗚𝗲𝗺𝗶𝗻𝗶: In Deep Think mode, our most capable model uses search and planning algorithms to explore lines of thought in parallel - an approach inspired by AlphaGo. Our goal is to build artificial general intelligence (AGI) that can help us make fundamental leaps in science and address some of the most pressing problems facing humanity, including energy and disease. The techniques we pioneered in AlphaGo are now paving the path towards AGI. Gemini uses some of the same search and planning approaches to reason across language, audio, video and images to build a model of how the world works. We think the combination of Gemini’s world model and AlphaGo’s techniques, as well as a system’s ability to call on specialised AI tools like AlphaFold, will prove to be critical for AGI. True creativity is a key capability that such an AGI system would need to exhibit. Move 37 was a glimpse of AI’s potential to think outside the box, but true original invention will require something more. It would need to not only come up with a novel Go strategy, as AlphaGo impressively did, but actually invent a game as deep and elegant, and as worthy of study as Go. AlphaGo has had an amazing impact over the past 10 years - look forward to seeing what it unlocks next!
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AI adoption is accelerating—but is your governance keeping up? ⚠️ Security Atlas AI empowers teams to manage risk, compliance, and vendor decisions with confidence. Replace manual processes with a structured framework, gain real-time visibility into your AI ecosystem, and align security, legal, and procurement in one platform 🔐📊 From GDPR to the EU AI Act, stay ahead of evolving regulations while accelerating innovation 🚀 Discover how: https://lnkd.in/emWsYMnS #AI #CyberSecurity #RiskManagement #Compliance #AIGovernance #Procurement #TechLeadership
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AI is scaling faster than governance ⚡, and organizations are feeling the pressure. Shadow AI sprawl 👻, overloaded review teams 📝, shifting regulations 📜, and no clear way to compare vendors ⚖️ create blind spots and risk. Security Atlas gives teams the visibility 👁️, benchmarks 📊, and workflows 🔄 to manage AI safely—and at scale. 👉 https://lnkd.in/epiH2Er9 #AIGovernance #CyberSecurity #AICompliance #ShadowAI #RiskManagement
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AI agents outnumbering humans isn’t a future problem — it’s already quietly taking shape inside modern infrastructure. If we don’t establish identity, accountability, and auditability at the agent level now, we’re building systems we won’t be able to trust or explain. Worth a look! 🤖
Is it possible that by 2027, AI agents are projected to outnumber humans on the planet? If so, most of them will have no verifiable identity. No birth certificate. No behavioral contract. No human owner recorded in any governance system. Just an API key and a config file, taking actions inside your infrastructure. We already have a name for this in security circles. Shadow IT. But shadow agents are different in kind, not just degree. A shadow SaaS subscription reads your data. A shadow agent acts on it. When something goes wrong the investigation starts from zero. Which agent did this? Who built it? What was it authorized to do? #AIGovernance #AgenticAI #ShadowAI #EnterpriseAI #CISO #EUAIAct
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🌍 One of the biggest challenges in AI governance? Urgency distorts decision-making. A tool looks valuable → pressure builds → risk gets overlooked ⚠️ At Security Atlas AI, we separate that problem with 3 independent scores 🧩 ⚡ Business Value (0–50) Measures scale, timeline, and expected impact 📈⏱️💡 ⚠️ Initial Risk (Low / Medium / High) A fast intake signal based on data exposure and system access 🔐🔗 👉 Completely independent from business demand 🛡️ Enterprise Readiness (0–100) Deep evaluation across 13 domains — from security and privacy to AI governance and legal ⚖️🤖 💡 The principle is simple: Value ≠ Risk ≠ Readiness Keeping them separate leads to clearer, more defensible decisions — without pressure skewing the outcome. #AIGovernance #EnterpriseAI #Risk #Security #Compliance
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🚨 AI is increasingly influencing decisions across the enterprise — often before a human ever reviews them. From hiring to risk and operational workflows, systems are moving faster than traditional governance approaches can keep up with 🤖 The challenge? 👉 Many organisations are still relying on static policies and periodic reviews to govern dynamic, continuously evolving AI systems. That creates a gap between: • What AI is doing • And what governance can actually observe So the question for enterprise teams becomes: ❓ How do you govern something that changes in real time? More teams are starting to explore: 🔍 Continuous evaluation instead of static enforcement 🔍 Greater visibility into system behaviour 🔍 Governance built into the system itself — not added afterwards Because ultimately, governance isn’t just about control… 👉 it’s about understanding and being able to justify decisions as they happen. For anyone working in security, risk, or AI — this shift is becoming hard to ignore 🔐⚖️ #AIGovernance #EnterpriseAI #Security #Risk #Compliance
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Enterprise AI adoption is accelerating, but governance isn't keeping pace. - Business units adopting tools faster than security, legal and procurement can review them - Legal reviewing contracts after commitments are already made - No centralized record of what's approved, denied, or pending - Board-level AI risk questions answered with tool inventories, not governance postures Is governance keeping pace with your AI strategy? #SecurityAtlasAI #AIgovernance #AI