Protect AI vs Giskard
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 Giskardleads with “Open-source and enterprise platform for testing and red-teaming LLM agents”. The table below compares what each publishes.
Where Protect AI pulls ahead
Publishes support for SOC 2, which Giskard does not. Security and ML teams needing to scan and defend models, pipelines, and LLM apps across the AI lifecycle
Where Giskard pulls ahead
Publishes support for EU AI Act, which Protect AI does not. ML, quality, and risk teams wanting open-source-first, framework-aligned LLM testing and continuous red teaming
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 | Open-source and enterprise platform for testing and red-teaming LLM agents |
|---|---|---|
| Category | Red-Teaming & AI Security | Red-Teaming & AI Security |
| Frameworks | NIST AI RMF, SOC 2 | EU AI Act, NIST AI RMF |
| Deployment | SaaS, On-prem, Open-source, API | Open-source, SaaS, On-prem, API |
| Built for | Security, Data Science / ML, Risk | Data Science / ML, Risk, Compliance |
| Founded | 2022 | 2021 |
| Headquarters | Seattle, Washington, USA | Paris, France |
| Ownership | Acquired by Palo Alto Networks (announced April 2025, ~$700M); part of Prisma AIRS | Independent, venture-backed (Y Combinator alumnus) |
| Funding | $60M Series B (2024) at ~$400M valuation prior to acquisition | Seed funding (reported ~$2-3M+); investors include Y Combinator, Elaia, and others |
| Pricing | Enterprise licensing; open-source tools (ModelScan, LLM Guard) free | Open-source library (free); Giskard Hub commercial enterprise subscription |
| Key capabilities |
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| Integrations | Amazon Bedrock, Hugging Face, MLflow / CI-CD pipelines, Major LLM providers, Palo Alto Prisma AIRS | Hugging Face, LangChain, MLflow, Common ML frameworks, Major LLM providers 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, quality, and risk teams wanting open-source-first, framework-aligned LLM testing and continuous red teaming |
| 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 | Testing/evaluation focus rather than inline runtime enforcement; smaller company and funding base; enterprise features concentrated in the paid Hub tier |
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 Giskard if mL, quality, and risk teams wanting open-source-first, framework-aligned LLM testing and continuous red teaming
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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