Giskard
Scan chatbots and AI agents for hallucinations, prompt injection, and data leaks, then turn findings into ongoing automated red-teaming tests. Giskard Guards adds real-time guardrails.
The verdict on Giskard: Teams that need to catch bias, vulnerabilities, and quality issues in an LLM application before it ships Giskard's value is pre-ship testing: it automatically generates adversarial test cases specific to your model's actual use case, catching hallucinations, prompt injection, and bias before they appear in production. Pricing: Free and open source / Giskard Hub custom enterprise pricing.
Best For
Teams that need to catch bias, vulnerabilities, and quality issues in an LLM application before it ships
Standout Feature
Automatically generates adversarial test cases specific to your model's actual use case, not generic red-teaming
TL;DR
A strong, open-source safety-testing layer, real evaluation expertise helps you get the most out of it.
Alternatives
Fit Score: use-case reference
How this is scoredGiskard scored 1 to 5 on five dimensions for the named use cases below. Part of our catalog-wide use-case reference, not a product-quality review rating.
An engineering team needing a structured pre-ship safety-testing layer for an LLM application, catching bias and prompt injection before production
4.0/5- Capability
- 5/5
- Reliability
- 4/5
- Value
- 4/5
- Ease
- 3/5
- Compatibility
- 4/5
Giskard automatically generates adversarial test cases specific to a model's actual use case, catching hallucinations, prompt injection, and bias before they appear in production, with a free open-source core.
A team looking for a fully automated safety solution that runs without setup or evaluation expertise
2.4/5- Capability
- 2/5
- Reliability
- 3/5
- Value
- 3/5
- Ease
- 2/5
- Compatibility
- 2/5
The tool's own take is explicit that getting the most out of it requires real evaluation expertise, and this fits teams needing a structured testing layer, not teams looking for a fully automated solution that runs without setup.
Editorial judgment grounded in this tool's own published pricing, features, and our existing pros and cons, not a certification or a measured benchmark. Product quality is assessed separately under our review methodology.
Overview
Not yet reviewedProduct quality is assessed from original evidence. How reviews work.
Giskard is an open-source testing and evaluation framework for AI models and LLM applications, it provides automated vulnerability scanning, bias detection, and quality assessment for machine learning systems before deployment and throughout the production lifecycle. The LLM testing module generates adversarial test cases automatically based on your model's specific use case: prompt injections, hallucination-inducing inputs, off-topic responses, harmful content generation attempts, and performance on edge cases that standard test sets don't cover. This automated red-teaming approach surfaces failure modes without requiring manual test case creation for every possible input type. The ML model testing capabilities cover traditional machine learning systems as well: performance regression detection, data drift monitoring, and fairness evaluation across demographic subgroups.
Giskard integrates with the major ML frameworks (scikit-learn, PyTorch, Hugging Face) and LLM platforms (LangChain, LlamaIndex, OpenAI). The scan results produce a structured report with severity rankings for identified issues and suggested remediation approaches. The open-source library is free; the Giskard Hub provides a collaborative web interface for managing evaluations across teams and models. Giskard is commonly used by AI engineering teams with production ML systems and LLM applications who need systematic quality assurance workflows rather than ad hoc testing, particularly in regulated industries where AI output quality has compliance implications.
The source of the legacy catalog rating and review count has not been substantiated. Neither is presented here as a verified review rating or included in review structured data.
Workflow Fit Scores are separate, use-case-specific editorial judgments, not user-review averages. Read the Fit Score methodology.
- • Catches issues early
- • Open source
- • Strong safety focus
- • Requires eval expertise
- • Newer enterprise hub
Key Features
- Automated LLM vulnerability scans
- Bias and robustness testing
- RAG evaluation
- CI/CD integration
Trust & Data
Verified 2026-09
- Training on your data
- NoGiskard's marketing pages and trust center explicitly advertise a zero-training policy protecting customer IP, alongside claimed SOC 2 Type II, GDPR, and HIPAA compliance. But Giskard's actual GDPR-mandated legal privacy policy, dated 2022, only covers how Giskard processes visitors' and customers' own personal data under GDPR; it does not itself mention the zero-training policy or the SOC2/HIPAA claims, which exist only in marketing copy and the trust center rather than in the binding privacy-policy text. HIPAA compliance here is a self-attested claim, since no official HIPAA certifying body exists, whereas SOC 2 Type II is backed by an actual report listed in the trust center's compliance documents.
- Compliance
- SOC 2 Type II, GDPR, HIPAA
- Retention
- Personal data tied to contracts or customer relationships is retained for the duration of the business relationship, transaction data is archived up to 5 years, and certain legally-required records up to 10 years; this governs website and contact personal data under GDPR, and no separate retention period for in-platform scan or report data was found.
- Export
- Full export
Sources: giskard-ai.github.io, trust.giskard.ai, giskard.ai
As published by the vendor. Verify independently before purchase decisions.
Our Take
The open-source core is free, and the Giskard Guards layer adds real-time guardrails. The honest context is that getting the most out of it requires real evaluation expertise, and the enterprise hub is newer with less production track record behind it. This fits engineering teams who need a structured safety-testing layer before deploying an LLM application, not teams looking for a fully automated solution that runs without setup.
Workflows Using This Tool
Step-by-step playbooks whose entries name Giskard. The step shown is where the workflow uses it.