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Prevent AI failures,
don't react to them
Trusted by entreprise AI teams
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Confidencein your security
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Get a free AI Risk Assessment and see what we find
Most companies believe their AI is secure, until we test it.
Request a free red teaming and discover what attackers could actually do to your system.
Leading experts in detecting all kinds of AI failures
AI agents are vulnerable to security attacks
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But over-secured AI Agents sacrifice business quality
Find out more industry specific examples in RealHarm
Detect AI vulnerabilities with the most advanced red teaming engine
Our red teaming engine continuously generates sophisticated attack scenarios whenever new threats emerge.
We deliver the largest coverage rate of security vulnerabilities with the highest domain specificity—all in one comprehensive platform.
Proactively prevent Business Compliance failures
Traditional security solutions miss business compliance issues that actually kill AI adoption: hallucinations, inappropriate denials, information omissions, and more.
Instead of only monitoring these incidents after they happen, we proactively catch business compliance issues before they hit production.
Align AI testing with real business requirements
Our visual annotation studio enables business experts to set business rules and approve quality standards through an intuitive interface.
Beyond developer-only tools, AI quality management is a shared responsibility between technical and business teams.
Automate test execution and prevent regression
Transform discovered vulnerabilities into permanent protection. Our system automatically converts findings into comprehensive test suites, creating a growing golden dataset that prevents regression.
Execute tests via Python SDK or web interface to ensure AI systems meet requirements after each update.
We integrate with your observability stack.
Enterprise-grade security
On premise/cloud
Flexible installation on your infrastructure or cloud (AWS) environment.
Secure access controls
Secure environment with role-based access management and enterprise SSO integration.
Data protection
Complete data isolation and encryption with EU-hosted infrastructure & GDPR compliance.
Research leaders in AI security & safety
We're research partners with Google DeepMind on Phare, a multilingual benchmark evaluating LLMs across key safety & security dimensions, including hallucination, factual accuracy, bias, and potential harm
Research and funding partners:
What do our customers say?
Giskard has become a cornerstone in our LLM evaluation pipeline providing enterprise-grade tools for hallucination detection, factuality checks, and robustness testing. It provides an intuitive UI, powerful APIs, and seamless workflow integration for production-ready evaluation.
Giskard has become our go-to tool for testing our landmark detection models. It allows us to identify biases in each model and make informed decisions.
Giskard has streamlined our entire testing process thanks to their solution that makes AI model testing truly effortless.
Resources
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How LLM jailbreaking can bypass AI security with multi-turn attacks
Multi-turn jailbreaking attacks like Crescendo bypass AI security measures by gradually steering conversations toward harmful outputs through innocent-seeming steps, creating serious business risks that standard single-message testing misses. This article reveals how these attacks work with real-world examples and provides practical techniques to detect and prevent them.
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RealPerformance, A Dataset of Language Model Business Compliance Issues
Giskard launches RealPerformance to address the gap between the focus on security and business compliance issues: the first systematic dataset of business performance failures in conversational AI, based on real-world testing across banks, insurers, and other industries.
LLMs recognise bias but also reproduce harmful stereotypes: an analysis of bias in leading LLMs
Our Phare benchmark reveals that leading LLMs reproduce stereotypes in stories despite recognising bias when asked directly. Analysis of 17 models shows the generation vs discrimination gap.
Your questions answered
What is the difference between Giskard and LLM platforms like LangSmith?
- Automated Vulnerability Detection:
Giskard not only tests your AI, but also automatically detects critical vulnerabilities such as hallucinations and security flaws. Since test cases can be virtually endless and highly domain-specific, Giskard leverages both internal and external data sources (e.g., RAG knowledge bases) to automatically and exhaustively generate test cases. - Proactive Monitoring:
At Giskard, we believe itʼs too late if issues are only discovered by users once the system is in production. Thatʼs why we focus on proactive monitoring, providing tools to detect AI vulnerabilities before they surface in real-world use. This involves continuously generating different attack scenarios and potential hallucinations throughout your AIʼs lifecycle. - Accessible for Business Stakeholders:
Giskard is not just a developer tool—itʼs also designed for business users like domain experts and product managers. It offers features such as a collaborative red-teaming playground and annotation tools, enabling anyone to easily craft test cases.
How does Giskard work to find vulnerabilities?
Giskard employs various methods to detect vulnerabilities, depending on their type:
- Internal Knowledge:
Leveraging company expertise (e.g., RAG knowledge base) to identify hallucinations. - Security Vulnerability Taxonomies:
Detecting issues such as stereotypes, discrimination, harmful content, personal information disclosure, prompt injections, and more. - External Resources:
Using cybersecurity monitoring and online data to continuously identify new vulnerabilities. - Internal Prompt Templates:
Applying templates based on our extensive experience with various clients.
Should Giskard be used before or after deployment?
Giskard can be used before and after deployment:
- Before deployment:
Provides comprehensive quantitative KPIs to ensure your AI agent is production-ready. - After deployment:
Continuously detects new vulnerabilities that may emerge once your AI application is in production.
After finding the vulnerabilities, can Giskard help me correct the AI agent?
Yes! After subscribing to the Giskard Hub, you can opt for support from our LLM researchers to help mitigate vulnerabilities. We can also assist in designing effective safeguards in production.
What type of LLM agents does Giskard support?
The Giskard Hub supports all types of text-to-text conversational bots.
Giskard operates as a black-box testing tool, meaning the Hub does not need to know the internal components of your agent (foundational models, vector database, etc.).
The bot as a whole only needs to be accessible through an API endpoint.
What’s the difference between Giskard Open Source and Giskard Hub?
- Giskard Open Source → A Python library intended for developers.
- Giskard Hub → An enterprise solution offering a broader range of features such as:
- A red-teaming playground
- Cybersecurity monitoring and alerting
- An annotation studio
- More advanced security vulnerability detection
For a complete overview of Giskard Hub’s features, follow this link.
I can’t have data that leaves my environment. Can I use Giskard’s Hub on-premise?
Yes, you can easily install the Giskard Hub on your internal machines or private cloud.
How much does the Giskard Hub cost?
The Giskard Hub is available through annual subscription based on the number of AI systems.
For pricing details, please follow this link.
