AI Risk & Responsibility Matrix: Analysis of 20 top LLMs
New Benchmark: Where LLMs Are Safe… and Where They’re Not
Aymara’s AI Risk & Responsibility Matrix measures 20 leading foundation models against 10 critical risk domains, including misinformation, bias, impersonation, and unqualified professional advice. The findings reveal significant variability in model safety and highlight why enterprises need independent, auditable evaluations before deploying generative AI at scale.
The State of Enterprise AI Safety
Enterprises are deploying generative AI faster than ever: in customer experiences, marketing, product development, and operations. But as adoption accelerates, so do the risks.
From hallucinations and misinformation to bias, brand safety failures, and compliance gaps, most organizations lack the tools to measure, monitor, and mitigate these risks at scale. Emerging governance frameworks like AIUC-1, ISO 42001, and the EU AI Act are raising the stakes, requiring auditable evidence of responsible AI practices.
The AI Risk & Responsibility Matrix addresses this gap by providing an independent, data-driven risk benchmark, helping enterprises make informed, safe, and compliant decisions.
Key Findings at a Glance
Privacy & impersonation were the weakest areas: models failed these checks 76% of the time. Even the best performed worse than a coin flip.
The performance gap between models was staggering: Claude Haiku 3.5 scored 86% “safe”, while Cohere’s Command R lagged at just 52%.
On the brighter side, models performed well on risks like misinformation (96% safe), malicious use (92%), and hate speech/bias (91%), which are already getting industry attention.
Enterprise Impacts: What Leaders Need to Know
Without independent safety benchmarks, enterprises risk reputational damage, regulatory exposure, and erosion of trust. The matrix gives teams the data they need to choose models responsibly. Based on risk, not hype.
No GenAI model is 100% risk-free, but Aymara’s AI Risk & Responsibility Matrix gives leaders across the enterprise the data to make smarter, safer decisions:
AI Buyers & Business Leaders — Choose models that match your risk tolerance and compliance needs.
Product & Engineering Teams — Pinpoint model weaknesses to guide guardrails, fine-tuning, and safer workflows.
Developers & Researchers — Benchmark against 20leading models and uncover blind spots to drive innovation.
The matrix turns safety from a guesswork problem into a data-driven strategy, helping enterprises protect brand trust, reduce risk, and stay ahead of emerging governance standards.
