Mindgard vs Giskard

Both compete in Red-Teaming & AI Security. Mindgard positions itself as “Continuous automated AI red teaming and security testing for enterprise AI systems”, while Giskardleads with “Open-source and enterprise platform for testing and red-teaming LLM agents”. The table below compares what each publishes.

Where Mindgard pulls ahead

Publishes support for SOC 2, which Giskard does not. Enterprise security teams needing continuous, attacker-perspective red teaming and testing of LLMs and AI systems

Where Giskard pulls ahead

Publishes support for EU AI Act, NIST AI RMF, which Mindgard does not. ML, quality, and risk teams wanting open-source-first, framework-aligned LLM testing and continuous red teaming

	Mindgard	Giskard
Positioning	Continuous automated AI red teaming and security testing for enterprise AI systems	Open-source and enterprise platform for testing and red-teaming LLM agents
Category	Red-Teaming & AI Security	Red-Teaming & AI Security
Frameworks	SOC 2	EU AI Act, NIST AI RMF
Deployment	SaaS, API	Open-source, SaaS, On-prem, API
Built for	Security, Data Science / ML, Risk	Data Science / ML, Risk, Compliance
Founded	2022	2021
Headquarters	Boston, USA (with London, UK office)	Paris, France
Ownership	Private (VC-backed)	Independent, venture-backed (Y Combinator alumnus)
Funding	Over $11.6M raised, including an $8M round in December 2024 led by .406 Ventures, with IQ Capital, Lakestar, Atlantic Bridge and WillowTree Investments	Seed funding (reported ~$2-3M+); investors include Y Combinator, Elaia, and others
Pricing	Not published	Open-source library (free); Giskard Hub commercial enterprise subscription
Key capabilities	Automated AI red teaming AI discovery and reconnaissance Attack surface mapping Vulnerability assessment Model scanning Runtime protection	Open-source LLM/model vulnerability scanning Automated test-suite generation Continuous red teaming Hallucination and prompt-injection testing Robustness and bias evaluation Business-domain test management (Giskard Hub)
Integrations	OpenAI, Anthropic, AWS, Docker, Burp Suite, CI/CD pipelines	Hugging Face, LangChain, MLflow, Common ML frameworks, Major LLM providers via API
Notable customers	None published	None published
Best for	Enterprise security teams needing continuous, attacker-perspective red teaming and testing of LLMs and AI systems	ML, quality, and risk teams wanting open-source-first, framework-aligned LLM testing and continuous red teaming
Limitations	Focused on security testing rather than broad AI governance or policy management; some framework-specific compliance mapping is not detailed publicly.	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 Mindgard if enterprise security teams needing continuous, attacker-perspective red teaming and testing of LLMs and AI systems

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.

Full Mindgard profile →Full Giskard profile →

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