‍Automated AI Red Teaming & Security Testing‍

Founded in a leading UK university lab, Mindgard boasts over 10 years of rigorous research in AI security, with public and private partnerships that ensure access to the latest advancements and the most qualified talent in the field.

Yes, Mindgard is neural network agnostic and supports a wide range of AI models, including Generative AI, LLMs, Natural Language Processing (NLP), audio, image, and multi-modal systems. This versatility allows it to address security concerns across various AI applications.

Mindgard follows industry best practices for secure software development and operation, including use of our own platform for testing AI components. We are GDPR compliant and expect ISO 27001 certification in early 2025.

Absolutely. Mindgard is designed to secure AI, Generative AI, and LLMs, including popular models like ChatGPT. It enables continuous testing and minimisation of security threats to your AI models and applications, ensuring they operate securely.

Mindgard serves a diverse range of organisations, including those in financial services, healthcare, manufacturing, and cybersecurity. Any enterprise deploying AI technologies can benefit from Mindgard's platform to secure their AI assets and mitigate potential risks.

The deployment and use of AI introduces new risks, creating a complex security landscape that traditional tools cannot address. As a result, many AI products are being launched without adequate security assurances, leaving organisations vulnerable—an issue underscored by a Gartner finding that 29% of enterprises deploying AI systems have reported security breaches, and only 10% of internal auditors have visibility into AI risk. Many of these new risks such as LLM prompt injection and jailbreaks exploit the probabilistic and opaque nature of AI systems, which only manifest at runtime. Securing these risks, unique to AI models and their toolchains, requires a fundamentally new approach.

Automated red teaming involves using automated tools and techniques to simulate attacks on AI systems, identifying vulnerabilities without manual intervention. This approach allows for continuous, efficient, and comprehensive security assessments, ensuring AI models are robust against potential threats.

Mindgard identifies various AI security risks, including:
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- Jailbreaking: Manipulating inputs to make AI systems perform unintended actions.
- Extraction: Reconstructing AI models to expose sensitive information.
- Evasion: Altering inputs to deceive AI models into incorrect outputs.
- Inversion: Reverse-engineering models to uncover training data.
- Poisoning: Tampering with training data to manipulate model behaviour.
- Prompt Injection: Inserting malicious inputs to trick AI systems into unintended responses.