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Prevent AI failures,
don't react to them
Trusted by entreprise AI teams
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Test your AI agents to catch issues before they happen in production
AI agents are vulnerable to security attacks, posing risks to your data & reputation
But overly secured AI agents can deliver poor-quality responses to your users
Learn about the real AI incidents we help you avoid
Your data is safe:
Sovereign & Secure infrastructure
Data Residency & Isolation
Choose where your data is processed (EU or US) with data residency & isolation guarantees. Benefit from automated updates & maintenance while keeping your data protected.
Granular Access controls & IP
Enforce Role-Based Access Control (RBAC), audit trails and integrate with your Identity Provider. Guarantee that your IP is protected without our 0-training policy.
Compliance & Security
End-to-end encryption at rest and in transit. As a European entity, we offer native GDPR adherence alongside SOC 2 Type II and HIPAA compliance.
Uncover the AI vulnerabilities that manual audits miss
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.
Stop hallucinations & business failures at the source
Standard AI security tools operate at the network layer, completely missing business failures like hallucinations and over-zealous refusals. These aren't just compliance risks, they are broken product experiences.
Stop relying on reactive monitoring. Integrate behavioral testing directly into your pipeline to catch and fix agent alignment issues during development, not after deployment.
Unify testing across business, engineering & security teams
Our visual Human-in-the-Loop dashboards enables your entire team to review and approve quality & security tests through a collaborative interface.
With Giskard, AI quality & security becomes a shared goal with a common language for your business, engineering & security teams.
Save time with continuous testing to prevent regressions
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.
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 streamlined our entire testing process thanks to their solution that makes AI model testing truly effortless.
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.
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.
Resources
OWASP Top 10 for LLM 2025: Understanding the Risks of Large Language Models
The landscape of large language model security has evolved significantly since the release of OWASP’s Top 10 for LLM Applications in 2023, which we covered in our blog at the time. The 2025 edition represents a significant update of our understanding of how Gen AI systems are being deployed in production environments. The update does not come as a surprise, as companies like MITRE also continuously update their risk framework, Atlas. The lessons from enterprise deployments, and direct feedback from a global community of developers, security professionals, and data scientists working in AI security.
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Anthropic claims Claude Code was used for the first Autonomous AI cyber espionage campaign
Anthropic has reported that Claude Code was used to orchestrate a cyber espionage campaign, with the AI independently executing 80–90% of the tactical operations. In this article, we analyze the mechanics of this attack, and explain how organizations can leverage continuous red teaming to defend against these threats.
Understanding single-turn, multi-turn, and dynamic agentic attacks in AI red teaming
AI red teaming has evolved from simple prompt injection into three distinct attack categories: single-turn attacks that test immediate defenses, multi-turn attacks that build context across conversations, and dynamic agentic attacks that autonomously adapt strategies in real-time. This article breaks down all three attack categories, and explains how to implement red teaming to protect production AI systems.
