Detect emerging AI harms
before they become crises.
Real-time incident intelligence for policymakers, researchers, insurers and law enforcement.
AI systems are reshaping society faster than our institutions can respond. Governance operates on lagging indicators - months or years between an incident and a policy response. As AI approaches transformative capabilities, that latency becomes dangerous.
Arcola AI exists to close the evidence gap. We aggregate incident data from multiple independent sources, apply structured classification with confidence scoring, and deliver a live, triangulated picture of how AI risk is emerging and evolving globally. No single source can provide this view. We build the connective tissue that makes the AI safety ecosystem more effective.
Monitor
Continuous scanning of news, social media, incident databases, regulatory filings, litigation records, and frontier lab disclosures. Our platform processes hundreds of thousands of signals per month, separating meaningful risk indicators from noise.
Classify
Each pipeline applies its own taxonomy and scoring rubric. Our default framework uses the MIT Risk Domain Taxonomy and a multi-dimensional severity scale based on CSET's taxonomy of AI harm. Partners can define their own classification logic for specialist use cases. Every classification captures reasoning for full traceability.
Analyse
Cross-source correlation exposes patterns that no single database can reveal. We track whether harm types are emerging, expanding, or being brought under control. We identify escalation pathways and flag near-misses - cases where different conditions would have caused far greater harm.
Who we serve
The same underlying intelligence serves multiple audiences. What changes is the lens.
Policymakers and Government
AI governance needs evidence, not anecdote. Current policy operates on lagging indicators - months or years between incidents and regulatory response. Our platform provides structured, real-time intelligence on how AI harms are evolving, tracking catastrophic risk precursors and enabling proportionate governance response grounded in data rather than headlines.
Law Enforcement
AI-enabled crime is a growing challenge - from deepfake fraud and synthetic identity theft to AI-generated disinformation. Understanding these patterns requires structured data on how AI is being misused, where incidents are concentrating, and how threat profiles are changing over time. Our platform provides that evidence base.
Researchers and Civil Society
AI safety research depends on comprehensive, well-classified incident data. We aggregate and triangulate across independent sources, apply a validated classification framework, and make the result available through open datasets, APIs, and collaborative partnerships. Our methodology is published and transparent.
Insurers and Risk Professionals
AI risk is already embedded across insurance portfolios - in cyber, tech E&O, D&O, and product liability lines. But without systematic incident data, the industry cannot distinguish safe deployments from dangerous ones or anticipate claims drivers. Our platform provides the external risk intelligence layer: historical incident analysis, real-time monitoring, litigation tracking, and severity scoring that maps to insurance classes of business.
Features
TIME
The Guardian
Past Affiliations
Centre for the Governance of AI
BlueDot Impact
Imperial College London
UCL
Cambridge University
Unreasonable Impact
Whether you're shaping AI governance, investigating AI-enabled crime, building risk models, or conducting safety research - we'd like to hear from you.