G

The testing
platform for AI models

Protect your company against biases, performance & security issues in AI models.

From tabular models to LLMs
Listed by Gartner
AI Trust, Risk and Security
# Get started
pip install giskard[llm]
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Giskard - Open-source testing framework for LLMs & ML models | Product Hunt

Trusted by Enterprise AI teams

Why?

AI pipelines are broken

AI risks, including quality, security & compliance, are not properly addressed by current MLOps tools.
AI teams spend weeks manually creating test cases, writing compliance reports, and enduring endless review meetings.
AI quality, security & compliance practices are siloed and inconsistent across projects & teams,
Non-compliance to the EU AI Act can cost your company up to 3% of global revenue.

Enter Giskard:
AI Testing at scale

Automatically detect performance, bias & security issues in AI models.
Stop wasting time on manual testing and writing custom evaluation reports.
Unify AI Testing practices: use standard methodologies for optimal model deployment.
Ensure compliance with the EU AI Act, eliminating risks of fines of 3% of your global revenue.
Giskard Open-Source

Easy to integrate for data scientists

In a few lines of code, identify vulnerabilities that may affect the performance, fairness & security of your model. 

Directly in your Python notebook or Integrated Development Environment (IDE).

import giskard
qa_chain = RetrievalQA.from_llm(...)
model = giskard.Model(
   
qa_chain,
   
model_type="text_generation",
    
name="My QA bot",
    
description="An AI assistant that...",
   
feature_names=["question"],
)
giskard.scan(model)
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Giskard Enterprise

Collaborative AI Quality, Security & Compliance

Entreprise platform to automate testing & compliance across your AI projects.
Try our latest open-source release!

Evaluate RAG Agents automatically

Leverage RAGET's automated testing capabilities to generate realistic test sets, and evaluate answer accuracy for your RAG agents.
TRY RAGET

Who is it for?

Data scientists
Heads of AI teams
AI Governance officers
You work on business-critical AI applications.
You work on enterprise AI deployments.
You spend a lot of time to evaluate AI models.
You’re preparing your company for compliance with the EU AI Act and other AI regulations.
You have high standards of performance, security & safety in AI models.

“Giskard really speeds up input gatherings and collaboration between data scientists and business stakeholders!”

Head of Data
Emeric Trossat

"Giskard really speeds up input gatherings and collaboration between data scientists and business stakeholders!"

Head of Data
Emeric Trossat

"Giskard has become a strong partner in our purpose for ethical AI. It delivers the right tools for releasing fair and trustworthy models."

Head of Data Science
Arnault Gombert

"Giskard enables to integrate Altaroad business experts' knowledge into our ML models and test them."

Jean MILPIED

"Giskard allows us to easily identify biases in our models and gives us actionable ways to deliver robust models to our customers."

Chief Science Officer
Maximilien Baudry

Join the community

Welcome to an inclusive community focused on AI Quality, Security & Compliance! Join us to share best practices, create new tests, and shape the future of AI standards together.

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All those interested in AI Quality, Security & Compliance are welcome!

All resources

Knowledge articles, tutorials and latest news on AI Quality, Security & Compliance

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LLM jailbreaking

Defending LLMs against Jailbreaking: Definition, examples and prevention

Jailbreaking refers to maliciously manipulating Large Language Models (LLMs) to bypass their ethical constraints and produce unauthorized outputs. This emerging threat arises from combining the models' high adaptability with inherent vulnerabilities that attackers can exploit through techniques like prompt injection. Mitigating jailbreaking risks requires a holistic approach involving robust security measures, adversarial testing, red teaming, and ongoing vigilance to safeguard the integrity and reliability of AI systems.

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Giskard and Grafana for data drift monitoring

Giskard + Grafana for Data Drift Monitoring

Learn how to monitor and visualize data drift using Giskard and Grafana in this guide. Perfect for generating intuitive visual representations, this tutorial takes you through the essential steps of setting up Grafana dashboards and integrating Giskard for effective data drift testing and visualization.

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Data poisoning attacks

Data Poisoning attacks on Enterprise LLM applications: AI risks, detection, and prevention

Data poisoning is a real threat to enterprise AI systems like Large Language Models (LLMs), where malicious data tampering can skew outputs and decision-making processes unnoticed. This article explores the mechanics of data poisoning attacks, real-world examples across industries, and best practices to mitigate risks through red teaming, and automated evaluation tools.

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