AI Risk & Responsibility Matrix: Analysis of 20 top LLMs
New Benchmark: Where LLMs Are Safe… and Where They’re Not
Generative AI is moving fast, and so are the risks. Aymara’s AI Risk & Responsibility Matrix measures 20 leading LLMs against 10 critical risk domains from bias and misinformation to impersonation and unqualified professional advice.
The results? Significant variability in model safety and a clear case for independent, auditable evaluations before deploying genAI at scale.
The State of Enterprise AI Safety
Enterprises are embedding AI into customer experiences, marketing, product development, and operations. But as adoption accelerates, so do the risks—and most lack the tools to measure, monitor, and mitigate them.
From hallucinations and misinformation to brand safety failures and compliance gaps, the stakes are rising as new governance frameworks like AIUC-1, ISO 42001, and the EU AI Act demand auditable proof of responsible AI practices.
Key Findings at a Glance
Privacy & Impersonation Weak Spots — Models failed 76% of these tests; even the best scored below a coin flip.
Huge Performance Gaps — Claude Haiku 3.5 scored 86% safe vs. Cohere’s Command R lagged at just 52%.
Better on Misinformation & Bias — Models performed well on misinformation (96%), malicious use (92%), and hate speech/bias (91%), but other high-impact risks remain largely unaddressed.
Enterprise Impacts: What Leaders Need to Know
No generative AI model is 100% risk-free. Without independent safety benchmarks, enterprises risk:
Reputational damage from unsafe outputs
Regulatory exposure under new governance standards
Erosion of trust with customers and stakeholders
Aymara’s matrix turns guesswork into strategy, giving leaders the data they need to:
AI Buyers & Business Leaders: Choose models aligned with risk tolerance and compliance needs
Product & Engineering Teams: Pinpoint weaknesses to guide guardrails and safer workflows
Developers & Researchers: Benchmark models and uncover industry-wide blind spots
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.
See Where Your Model Measures Up
Want to know where your models stand? Request a free snapshot or book a demo to see how Aymara evaluates safety, compliance, and risk. All in one platform.
