Giskard AI for ML Model Testing and LLM Safety

Overview

Giskard AI provides tools for the testing and monitoring of machine learning models and large language models (LLMs). The platform is designed to assist data scientists and ML engineers in ensuring the reliability, robustness, and ethical compliance of their AI systems. Its open-source core allows developers to integrate testing directly into their existing ML workflows using a Python library (Giskard documentation).

The system offers features for identifying various model vulnerabilities, including performance biases, data leakage, and adversarial attacks. For LLMs, Giskard includes specialized scans to detect issues related to security, hallucination, prompt injection, and toxicity. This functionality enables teams to conduct automated testing throughout the model lifecycle, from development to deployment and continuous monitoring.

Giskard is suitable for organizations that require rigorous validation of their AI models to mitigate risks and comply with regulatory standards such as GDPR. Its collaborative features support data science teams in sharing test results and aligning on model quality. The platform's modular design, offering both a self-hosted open-source version and managed commercial tiers, allows for flexibility in deployment and scaling based on organizational needs.

For example, a team developing a credit scoring model could use Giskard to automatically test for disparate impact across demographic subgroups, ensuring fairness before deployment. Similarly, an organization deploying a customer service chatbot powered by an LLM could employ Giskard LLM Scan to identify potential prompt injection vulnerabilities or instances of biased responses, contributing to a more secure and reliable user experience.

The platform positions itself within the broader MLOps ecosystem, focusing specifically on the quality assurance aspects of ML model development and deployment. While other tools like Evidently AI focus on data drift and model performance monitoring (Evidently AI Getting Started), Giskard emphasizes comprehensive testing for vulnerabilities and ethical concerns, complementing monitoring solutions by proactively identifying issues before they manifest in production.

Key features

• Giskard Scan: Automated vulnerability scanning for ML models, detecting issues such as performance bias, data leakage, and robustness to adversarial examples (Giskard Scan Documentation).
• Giskard Test Suite: A framework for creating and running custom test suites for ML models, allowing developers to define specific performance, robustness, and fairness criteria.
• Giskard LLM Scan: Specialized testing for Large Language Models to identify vulnerabilities like prompt injection, hallucination, toxicity, and security risks (Giskard LLM Scan Documentation).
• Open-Source Core: A self-hostable, open-source version that enables local development and integration into existing ML pipelines.
• Collaborative Platform: Features for data science teams to share test results, collaborate on model quality, and track model health over time.
• Python SDK: A Python library for programmatic interaction, allowing deep integration into ML development workflows and CI/CD pipelines.

Pricing

Giskard AI offers a community open-source tier and tiered commercial plans. Pricing details are subject to change and are current as of May 2026 (Giskard Pricing Page).

Common integrations

• MLflow: Integration with MLflow for tracking experiments and managing the model lifecycle, allowing Giskard tests to be part of the MLflow pipeline (MLflow Integration Guide).
• Pandas: Direct compatibility with Pandas DataFrames for input and output, streamlining data preparation and analysis within testing workflows.
• Scikit-learn: Seamless integration with scikit-learn models, enabling direct testing of models built using the scikit-learn framework (Scikit-learn Integration Guide).
• Hugging Face Transformers: Support for testing models from the Hugging Face Transformers library, particularly relevant for LLM evaluation.

Alternatives

• WhyLabs: Offers AI observability and monitoring for data and model performance, focusing on drift detection and anomaly explanation (WhyLabs Homepage).
• Evidently AI: An open-source framework for ML model evaluation and monitoring, specializing in data drift, model performance, and data quality issues (Evidently AI Homepage).
• Arthur AI: Provides an ML monitoring platform for performance, bias, and explainability across various model types (Arthur AI Homepage).

Getting started

To begin using Giskard AI, you can install the Python library and perform a basic scan on an ML model. This example demonstrates installing Giskard and running a scan on a simple scikit-learn model.

# 1. Install Giskard
pip install giskard

# 2. Import necessary libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
import giskard as gsk

# 3. Create a dummy dataset
data = {
    'feature_1': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    'feature_2': [10, 9, 8, 7, 6, 5, 4, 3, 2, 1],
    'label': [0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
}
df = pd.DataFrame(data)

X = df[['feature_1', 'feature_2']]
y = df['label']

# 4. Train a simple scikit-learn model
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model = RandomForestClassifier(random_state=42)
model.fit(X_train, y_train)

# 5. Wrap your model with Giskard's Model object
giskard_model = gsk.Model(
    model=model,  # Your trained scikit-learn model
    model_type="classification",  # 'classification' or 'regression'
    feature_names=X.columns.tolist(),
    name="Simple Random Forest Classifier",
    classification_labels=[0, 1] # For classification models
)

# 6. Create a Giskard Dataset from your test data
giskard_dataset = gsk.Dataset(
    df=X_test,  # Your test data features
    target=y_test, # Your test data labels (optional, but recommended for evaluation)
    name="Test Dataset"
)

# 7. Run the Giskard Scan
# This will analyze your model and dataset for potential vulnerabilities
scan_results = gsk.scan(giskard_model, giskard_dataset)

# 8. Print the scan results
print(scan_results)

This code snippet installs the Giskard library, creates a basic machine learning model, and then uses Giskard to wrap the model and a test dataset. Finally, it executes gsk.scan() to analyze the model for potential issues, providing an initial assessment of its quality and robustness. Further details on more advanced testing and LLM-specific scans are available in the Giskard documentation.