Benefits
- Cut time spent on Model Development, Testing and Validation
- Automated Risk Scoring, Code Quality and Model Dependencies
- No-code Inventory and Workflow Management
- Evidence controls and compliance through automated test results and supporting documentation
- Scan models/data for privacy and vulnerabilities
- Aligned with NIST AI Framework
FEATURES
Code Quality
The Code Quality helps users to know the details of errors and warnings generated for the AI file.
Link Map
The Link Map helps users to understand the link of the AI file with libraries, inputs & outputs. It helps to show CVE for the AI libraries.
Vulnerability
The Vulnerability Test helps users to find CVE for the AI libraries.
Privacy
The Privacy Test helps users to search for privacy-related keywords within AI file.
Risk Score Card
The Risk Score Card allows the users to analyze and review the Attributes, Attribute Type, Attribute Count, Attribute Risk Factor associated with the AI file and their Risk Contribution.
AI Content Detection
The Generative AI Content/AI Model helps users to find if the content/model is based on the AI or not.
LLM Risk Assessment
LLM Risk Assessment identifies vulnerabilities in LLM-generated responses to ensure reliable and trustworthy content.
LLM Hallucination
The LLM Hallucination Test helps users understand the Hallucination Rate of custom LLMs trained on a specific dataset.
LLM Source Attribution
The LLM Source Attribution test helps users understand where with a structured & unstructured data an LLM is generating a response to a prompt from.
Fairness
The Fairness Test evaluates the fairness of AI models. It assesses whether the predictions or decisions made by the AI model exhibit bias or discrimination towards certain groups based on sensitive attributes such as race, gender, or age.
Interpretability
The Interpretability Test helps users to find the importance of input features in the model's decision-making process, highlighting which features have the most significant impact on the predictions.
Validity & Reliability
The Validity & Reliability Test helps users to evaluate the trustworthiness and robustness of AI models.
Data Drift & Quality
Data Drift & Quality Testing
AI Risk Management Framework
Explore the realm of Artificial Intelligence (AI) with our AI Risk Management Policy. This concise guide covers the spectrum of AI models, including supervised, unsupervised, and deep learning, and emphasizes making AI trustworthy based on the NIST AI Risk Management Framework.
Learn to assess and manage AI Risk, cultivate a culture of risk awareness, and utilize periodic testing with tools like ours. This policy is your essential toolkit for responsible and effective AI utilization in your organization.