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Prevent AI failures,
don't react to them
Trusted by enterprise 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 (solo-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
Agentic tool extraction: Multi-turn attack that exposes the agent's internal functions
Agentic Tool Extraction (ATE) is a multi-turn reconnaissance attack to extract complete tool schemas, function names, parameters, types, and return values. ATE exploits conversation context, using seemingly benign questions that bypass standard filters to build a technical blueprint of the agent's capabilities. In this article, we demonstrate how attackers weaponize extracted schemas to craft precise exploits and explain how conversation-level defenses can detect progressive extraction patterns before tool signatures are fully exposed.
Risk assessment for LLMs and AI agents: OWASP, MITRE Atlas, and NIST AI RMF explained
There are three major tools for assessing risks associated with LLMs and AI Agents: OWASP, MITRE Attack and NIST AI RMF. Each of them has its own approach to risk and security, while examining it from different angles with varying levels of granularity and organisational scope. This blog will help you understand them.
Beyond sycophancy: The risk of vulnerable misguidance in AI medical advice
Healthcare employees in Hyderabad have noticed a disturbing direction in self-doctoring: two of their patients relied on generic AI chatbot advice for their healthcare interventions, and some of them suffered serious medical consequences. Two recent cases demonstrate the vulnerability of misguidance to a subtle risk in deployed agents, which can allow the agent to be harmful by encouraging harmful behaviour.
