HiddenLayer vs Giskard

Both compete in Red-Teaming & AI Security. HiddenLayer positions itself as “AI detection and response plus red teaming to protect machine learning models and AI products”, while Giskardleads with “Open-source and enterprise platform for testing and red-teaming LLM agents”. The table below compares what each publishes.

Where HiddenLayer pulls ahead

Enterprises and regulated organizations needing runtime protection and detection for production ML models

Where Giskard pulls ahead

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

	HiddenLayer	Giskard
Positioning	AI detection and response plus red teaming to protect machine learning models and AI products	Open-source and enterprise platform for testing and red-teaming LLM agents
Category	Red-Teaming & AI Security	Red-Teaming & AI Security
Frameworks	None published	EU AI Act, NIST AI RMF
Deployment	SaaS, On-prem, API	Open-source, SaaS, On-prem, API
Built for	Security, Data Science / ML, Risk	Data Science / ML, Risk, Compliance
Founded	2022	2021
Headquarters	Austin, Texas, USA	Paris, France
Ownership	Private (VC-backed)	Independent, venture-backed (Y Combinator alumnus)
Funding	$56M total; $50M Series A in 2023 led by M12 and Moore Strategic Ventures, with Booz Allen, IBM and Capital One Ventures	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	Machine Learning Detection and Response (MLDR) Model scanning Automated red teaming Real-time attack detection AI risk and governance reporting Response actions (alert, isolate, deceive)	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	Not published	Hugging Face, LangChain, MLflow, Common ML frameworks, Major LLM providers via API
Notable customers	None published	None published
Best for	Enterprises and regulated organizations needing runtime protection and detection for production ML models	ML, quality, and risk teams wanting open-source-first, framework-aligned LLM testing and continuous red teaming
Limitations	Historically ML-model centric; buyers seeking full agentic AI governance or compliance certification may need complementary tooling.	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 HiddenLayer if enterprises and regulated organizations needing runtime protection and detection for production ML models

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 HiddenLayer profile →Full Giskard profile →

AI Governance Tool Selection Kit

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