Protect AI vs Patronus AI
Both compete in Red-Teaming & AI Security. Protect AI positions itself as “End-to-end security for the AI and machine-learning supply chain”, while Patronus AIleads with “Automated evaluation, guardrails, and judges for LLM and agent reliability”. The table below compares what each publishes.
Where Protect AI pulls ahead
Publishes support for SOC 2, which Patronus AI does not. Security and ML teams needing to scan and defend models, pipelines, and LLM apps across the AI lifecycle
Where Patronus AI pulls ahead
ML and product teams that need automated, research-grade evaluation plus guardrails to ship reliable LLM apps
Both map to NIST AI RMF, so framework coverage alone will not separate them — the decision usually comes down to who operates the tool and how it fits your existing stack.
| Positioning | End-to-end security for the AI and machine-learning supply chain | Automated evaluation, guardrails, and judges for LLM and agent reliability |
|---|---|---|
| Category | Red-Teaming & AI Security | Red-Teaming & AI Security |
| Frameworks | NIST AI RMF, SOC 2 | NIST AI RMF |
| Deployment | SaaS, On-prem, Open-source, API | SaaS, API |
| Built for | Security, Data Science / ML, Risk | Data Science / ML, Risk, Compliance |
| Founded | 2022 | 2023 |
| Headquarters | Seattle, Washington, USA | San Francisco, California, USA |
| Ownership | Acquired by Palo Alto Networks (announced April 2025, ~$700M); part of Prisma AIRS | Independent, venture-backed |
| Funding | $60M Series B (2024) at ~$400M valuation prior to acquisition | $17M Series A (2024) led by Notable Capital, with Lightspeed and Datadog (~$20M total); subsequent Series B reported |
| Pricing | Enterprise licensing; open-source tools (ModelScan, LLM Guard) free | Commercial SaaS / usage-based; some open evaluators and models available |
| Key capabilities |
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| Integrations | Amazon Bedrock, Hugging Face, MLflow / CI-CD pipelines, Major LLM providers, Palo Alto Prisma AIRS | OpenAI, Anthropic, Bifrost gateway, Common ML/LLM stacks via API |
| Notable customers | None published | None published |
| Best for | Security and ML teams needing to scan and defend models, pipelines, and LLM apps across the AI lifecycle | ML and product teams that need automated, research-grade evaluation plus guardrails to ship reliable LLM apps |
| Limitations | Now integrated into Palo Alto Networks, so standalone products may converge into Prisma AIRS; breadth means some components (runtime guardrails vs. model scanning) are stronger than others | More an evaluation/observability platform than a hardened security firewall; deepest value requires building evaluation into workflows; younger company still expanding enterprise features |
Which should you shortlist?
Choose Protect AI if security and ML teams needing to scan and defend models, pipelines, and LLM apps across the AI lifecycle
Choose Patronus AI if mL and product teams that need automated, research-grade evaluation plus guardrails to ship reliable LLM apps
Neither is a substitute for a governance program. Whichever you pick, you still need people who can define the policies the tool enforces.
AI Governance Tool Selection Kit
A vendor-comparison worksheet plus EU AI Act, NIST AI RMF and ISO/IEC 42001 requirement checklists — so you can shortlist tools against the obligations that actually apply to you.
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