# AI Model Risk Intelligence Know Which Models You Can Trust Before You Deploy

DevFeed: [AI Model Risk Intelligence Know Which Models You Can Trust Before You Deploy](<https://devfeed.tech/articles/ai-model-risk-intelligence-know-which-models-you-can-trust-before-you-deploy-8253.md>)

Original publisher: [Read original article](<https://snyk.io/blog/why-we-rebuilt-evo-ai-model-risk-scoring/>)

Author: Ranko Cupovic

Published: 2026-08-04T04:00:00Z

Content type: article

Language: en

Sources: [Blog RSS Feed | Snyk](<https://devfeed.tech/sources/blog-rss-feed-snyk.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Adversarial attacks](<https://devfeed.tech/topics/adversarial-attacks.md>), [Security](<https://devfeed.tech/topics/security.md>), [asr](<https://devfeed.tech/topics/asr.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [Deployment](<https://devfeed.tech/topics/deployment.md>), [pii](<https://devfeed.tech/topics/pii.md>), [Code](<https://devfeed.tech/topics/code.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [application-security](<https://devfeed.tech/tags/application-security.md>), [asr](<https://devfeed.tech/tags/asr.md>), [attacks](<https://devfeed.tech/tags/attacks.md>), [awareness](<https://devfeed.tech/tags/awareness.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [blog](<https://devfeed.tech/tags/blog.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [developer](<https://devfeed.tech/tags/developer.md>), [devops](<https://devfeed.tech/tags/devops.md>), [enablement](<https://devfeed.tech/tags/enablement.md>), [model](<https://devfeed.tech/tags/model.md>), [pii](<https://devfeed.tech/tags/pii.md>), [safety](<https://devfeed.tech/tags/safety.md>), [security](<https://devfeed.tech/tags/security.md>), [snyk-platform](<https://devfeed.tech/tags/snyk-platform.md>), [snyk-security-intel](<https://devfeed.tech/tags/snyk-security-intel.md>)

## AI overview

Evo's AI model risk score combines attack success rate and attack impact into a 0-1000 score. It is based on adversarial testing against standard system-prompt hardening and breaks risk down by attacker goals such as PII extraction, system-prompt extraction, and insecure code generation to support deployment decisions.

## Source excerpt

AI model risk depends on how a model is deployed. Learn how Evo combines adversarial testing, attack impact, and deployment context to help teams compare models and enforce policy.