Mindgard provides a continuous automated Red Teaming platform that identifies and mitigates security vulnerabilities in AI and GenAI applications. By leveraging a comprehensive AI attack library, the platform enables enterprise security teams to minimize cyber risks and enhance the security posture of their AI models.
Funding
$8.2M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
4VFounders
Product
Problem
Enterprises deploying AI and GenAI models face increasing cybersecurity risks, including prompt injection, model evasion, and data extraction, which can lead to data leaks, manipulated outputs, and reputational damage. Existing cybersecurity tools are not specifically designed to address these unique vulnerabilities in AI and machine learning systems.
Solution
Mindgard offers a continuous automated Red Teaming platform that identifies and helps remediate security vulnerabilities in AI and GenAI applications, enabling enterprises to deploy AI securely. The platform leverages a comprehensive AI attack library, continuously updated by AI security researchers, to test AI models against a diverse range of threats, including jailbreak, extraction, evasion, inversion, poisoning, and prompt injection attacks. Mindgard automates the red teaming process, providing instant feedback for security risk mitigation and seamless integration into MLOps pipelines to detect changes in AI security posture from prompt engineering, retrieval-augmented generation (RAG), fine-tuning, and pre-training.
Target Audience
The primary customers are enterprise security teams across all sectors who are building, buying, or adopting AI and GenAI and need to minimize AI cyber risk, accelerate AI adoption, and unlock AI/GenAI value to their business.
Features
- Automated red teaming of AI/GenAI models in minutes
- Market-leading AI attack library continuously enriched by PhD AI security researchers
- Comprehensive testing against a diverse range of AI systems, including multi-modal Generative AI and Large Language Models (LLMs), as well as audio, vision, chatbots, and agent applications
- Seamless integration into MLOps pipelines to detect changes in AI security posture
- Support for various adversarial machine learning attacks against all types of neural networks, including deep learning models and GenAI applications beyond LLMs
- Red teaming methods including model input/output analysis and application programmable interface (API) analytics
- Identification of AI prompt hacking vulnerabilities like jailbreaks, as well as more fundamental flaws in the AI models such as ‘extract model’ and ‘extract training data’ vulnerabilities