Protect AI vs Mindgard
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 Mindgardleads with “Continuous automated AI red teaming and security testing for enterprise AI systems”. The table below compares what each publishes.
Where Protect AI pulls ahead
Publishes support for NIST AI RMF, which Mindgard does not. Security and ML teams needing to scan and defend models, pipelines, and LLM apps across the AI lifecycle
Where Mindgard pulls ahead
Enterprise security teams needing continuous, attacker-perspective red teaming and testing of LLMs and AI systems
Both map to SOC 2, 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 | Continuous automated AI red teaming and security testing for enterprise AI systems |
|---|---|---|
| Category | Red-Teaming & AI Security | Red-Teaming & AI Security |
| Frameworks | NIST AI RMF, SOC 2 | SOC 2 |
| Deployment | SaaS, On-prem, Open-source, API | SaaS, API |
| Built for | Security, Data Science / ML, Risk | Security, Data Science / ML, Risk |
| Founded | 2022 | 2022 |
| Headquarters | Seattle, Washington, USA | Boston, USA (with London, UK office) |
| Ownership | Acquired by Palo Alto Networks (announced April 2025, ~$700M); part of Prisma AIRS | Private (VC-backed) |
| Funding | $60M Series B (2024) at ~$400M valuation prior to acquisition | Over $11.6M raised, including an $8M round in December 2024 led by .406 Ventures, with IQ Capital, Lakestar, Atlantic Bridge and WillowTree Investments |
| Pricing | Enterprise licensing; open-source tools (ModelScan, LLM Guard) free | Not published |
| Key capabilities |
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| Integrations | Amazon Bedrock, Hugging Face, MLflow / CI-CD pipelines, Major LLM providers, Palo Alto Prisma AIRS | OpenAI, Anthropic, AWS, Docker, Burp Suite, CI/CD pipelines |
| 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 | Enterprise security teams needing continuous, attacker-perspective red teaming and testing of LLMs and AI systems |
| 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 | Focused on security testing rather than broad AI governance or policy management; some framework-specific compliance mapping is not detailed publicly. |
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 Mindgard if enterprise security teams needing continuous, attacker-perspective red teaming and testing of LLMs and AI systems
Neither is a substitute for a governance program. Whichever you pick, you still need people who can define the policies the tool enforces.
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