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Agentic AI risks:
Handled.
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We find what breaks your AI agents before it happens 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
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.
AI security, delivered as a service from findings to deployment
A go/no-go report that clears your agent for production
After every assessment, you receive a structured report with a clear verdict: deploy or fix. Security vulnerabilities ranked by criticality, functional scenarios tested against your requirements, and a Giskard Label if your agent passes.
From findings to fixes, we manage the entire remediation
Vulnerabilities don't end at the report. We qualify each finding, discuss severity with your team, prioritize what matters, open tickets in your workflow, and re-run every test until the fix is confirmed.
What do our customers say?
Giskard researchers spent several weeks testing our chatbot for prompt injections, denial-of-service attacks, and more. We detected vulnerabilities during the build phase and secured the chatbot before it went into production.
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.
We use Giskard to test the AI assistant and supervise what it does. This helps prevent what we saw at other companies, where the AI provided guidance to customers that put them in a very very bad position.
Giskard has streamlined our entire testing process thanks to their solution that makes AI model testing truly effortless.
Resources
Harm-first security: why we should start from the harm and not from the techniques
Security teams still measure AI risk with catalogs of attack techniques, yet incident records point elsewhere: practitioners in the OWASP GenAI working group ranked prompt injection first and misinformation last, while hallucination is among the top causes of real incidents. This article argues that the order should be reversed, so that the harms an organization fears, including those with no attacker behind them, decide which techniques are worth testing. It shows how NotPetya, NIS2, and DORA already pushed security in that direction, and why vulnerability scanners and prompt-battery "AI red teaming" cannot produce a risk model on their own.
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StereoTales accepted at NeurIPS 2026: frontier LLMs recognize harmful stereotypes, then write them anyway
StereoTales, Giskard's paper on harmful stereotypes in large language models, has been accepted at NeurIPS 2026, the largest peer-reviewed conference in machine learning. The study prompted 23 frontier models to write more than 650,000 stories in 10 languages, and found that every model produces associations human raters judge harmful.
AI Red Teaming vs. penetration testing: Why pentest methodology breaks on LLM agents
AI agents are now connected to core business systems, and security teams are often asked to test them the same way they test web applications. But penetration testing relies on a clear pass/fail result, and LLMs don't provide one: the same prompt can succeed on one run and fail on the next, and whether an answer is a problem depends on each company's own rules. Penetration testing relies on a machine-verifiable result. LLM systems do not provide one, and the rest of the discipline follows from that absence.
Your questions answered
Should Giskard be used before or after deployment?
Giskard enables continuous testing of AI agents, so it should be used both before & after deployment:
- Before deployment:
Provides comprehensive quantitative KPIs to ensure your AI agent is production-ready and has controlled risks. - After deployment:
Continuously detects new vulnerabilities and control risks that may emerge once your AI agent is in production.
How does Giskard work to find vulnerabilities?
Giskard employs various methods to detect risks, depending on their type:
- Proprietary Giskard AI Security & Safety Research: Detecting issues such as harmful content, personal information disclosure, prompt injections, and more.
- Risk-based test generation: Applying scenarios based on our extensive experience with various clients.
- External Knowledge: Leveraging our clients' reference knowledge bases to identify domain-specific hallucinations.
- External Security Resources: Using cybersecurity monitoring and online data to continuously identify new vulnerabilities.
What type of AI agents does Giskard support?
The Giskard Hub supports any AI agent processing text data, from RAG chatbots to agentic AI workflows processing JSON documents.
Giskard operates as a black-box testing tool, meaning our product does not need to know the internal components of your AI agent and does not need access to any production or personal data from users of your AI agents.
The agent only needs to be accessible through an API endpoint, to be tested by Giskard.
What’s the difference between Giskard paid product and Giskard Open-Source?
For a complete feature comparison of Giskard Hub vs Giskard Open-Source, please read this documentation.
What is the difference between Giskard and agentic developement 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! As part of our audits, Giskard provides remediation guidance to help you correct vulnerabilities and issues detected in your AI agents, and also provides a dedicated agent guardrails module for runtime protection.
I can’t have data that leaves my environment. Can I use Giskard’s Hub on-premise?
Yes, for mission-critical workloads in the public sector, infrastructure, defense or other mission-critical applications, our forward-deployed engineers can help install Giskard 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.
