AI Model Risk Intelligence Know Which Models You Can Trust Before You Deploy
Blog post from Snyk
Evo introduces a novel approach to assessing and managing AI model risk by creating a real risk score that combines the likelihood and impact of adversarial attacks, resulting in a score from 0 to 1000, where lower is better. This methodology shifts from traditional static evaluations to a dynamic, context-aware system that reflects how models are actually deployed and behave under attack in real-world scenarios. By focusing on Attack Success Rate (ASR) and the specific goals of attackers, Evo provides granular insights that enable security teams to understand and prioritize risks, build appropriate guardrails, and enforce policies effectively. The risk intelligence integrates with existing frameworks like OWASP and NIST, facilitating seamless policy enforcement and empowering organizations to govern AI adoption at scale by providing visibility into AI components and their interactions. This approach addresses the challenges of indirect attacks and the complexities of AI deployment, ensuring that risk scores are actionable and aligned with the specific use cases and environments in which AI models operate.
No tracked trend matches for this post yet.
Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.