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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 & IP Controls
Enforce Role-Based Access Control (RBAC), audit trails and integrate with your Identity Provider. Ensure your Intellectual Property is protected with 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 test coverage of both security & quality vulnerabilities, with the highest domain specificity—all in one automated scan.
Stop hallucinations & business failures at the source
Standard AI security tools operate at the network layer, completely missing domain-specific hallucinations, and over-zealous moderation. These aren't just compliance risks, they are broken product experiences.
Stop relying on reactive monitoring. Integrate proactive quality testing into your pipeline to catch and fix business failures of AI agents during development, not after deployment.
Unify testing across business, engineering & security teams
Our visual Human-in-the-Loop dashboards enable your entire team to review, customize, and approve tests through a collaborative interface.
With Giskard, AI quality & security become shared goals 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 detected issues into reproducible test suites, to enrich your golden test dataset continuously and prevent regression.
Execute tests programmatically via our Python SDK or schedule them in our web UI to ensure AI agents 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
Should Giskard be used before or after deployment?
Giskard enables continuous testing of LLM agents, so it should be used both before & 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.
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.
What type of LLM agents does Giskard support?
The Giskard Hub supports specifically Conversational AI agents in text-to-text mode.
Giskard operates as a black-box testing tool, meaning the Hub does not need to know the internal components of your LLM agent (foundation models, vector database, etc.).
The bot as a whole only needs to be accessible through an API endpoint.
What’s the difference between Giskard Hub (enterprise tier) and Giskard Open-Source (free-tier)?
For a complete feature comparison of Giskard Hub vs Giskard Open-Source, please read this documentation.
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.
After finding the vulnerabilities, can Giskard help me correct the AI agent?
Yes! After subscribing to the Giskard Hub, you can opt for technical consulting support from our AI security team to help mitigate vulnerabilities. We can assist in designing effective guardrails in production.
I can’t have data that leaves my environment. Can I use Giskard’s Hub on-premise?
Yes, specifically for mission-critical workloads in the public sector, defense or other sensitive applications, our engineering team can help you install Giskard Hub in on-premise environments. Contact us here to know more.
What's the pricing model of Giskard Hub?
For pricing details, please follow this link.
Resources
AI phishing attack in Australia: 270,000 fake government emails expose AI security gap
A massive phishing campaign impersonating Australian government services sent over 270,000 fake emails in four months, with security researchers pointing to AI assistance based on the sudden jump in sophistication and quality. The real security challenge isn't a vulnerability in the models themselves, it's that attackers can use LLM capabilities to rapidly generate convincing phishing content, either through contextual framing or jailbreaking techniques that bypass safety guardrails.
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.
