Mindgard: Secure your AI systems from new threats that traditional application security tools cannot address.

What is Mindgard

The deployment and use of Artificial Intelligence 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 organizations vulnerable. Mindgard's Dynamic Application Security Testing for AI (DAST-AI) is an automated red teaming solution that identifies and resolves AI-specific risks that can only be detected during runtime.

How to Use Mindgard

Integrate Mindgard into your existing CI/CD automation and all SDLC stages.
Provide an inference or API endpoint for model integration.
Mindgard will continuously test and identify AI-specific risks, integrating findings into your existing reporting and SIEM systems.

Use Cases of Mindgard

Mindgard is designed to secure AI systems, including Generative AI, LLMs, Natural Language Processing (NLP), audio, image, and multi-modal systems. It is suitable for organizations deploying AI technologies, ensuring they operate securely and mitigate potential risks.

Features of Mindgard

Identifies and helps resolve AI-specific risks

Mindgard's DAST-AI solution detects vulnerabilities unique to AI systems, such as prompt injection, jailbreaking, and data extraction.
Continuous security testing across the AI SDLC

Mindgard integrates into all stages of the AI software development lifecycle, providing continuous security assessments.
Integrates into existing reporting & SIEM systems

Mindgard seamlessly integrates with existing security tools, ensuring that AI-specific risks are reported and managed alongside traditional security findings.

FAQs from Mindgard

What makes Mindgard stand out from other AI security companies?

Mindgard was founded in a leading UK university lab and boasts over 10 years of rigorous research in AI security. It leverages public and private partnerships to ensure access to the latest advancements and qualified talent in the field.

Can Mindgard handle different kinds of AI models?

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.

How does Mindgard ensure data security and privacy?

Mindgard follows industry best practices for secure software development and operation, including the use of its own platform for testing AI components. It is GDPR compliant and expects ISO 27001 certification in early 2025.

Can Mindgard work with the LLMs I use today?

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

What types of organisations use Mindgard?

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.

Why don't traditional AppSec tools work for AI models?

The deployment and use of AI introduces new risks, creating a complex security landscape that traditional tools cannot address. 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.

What is automated red teaming?

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.

What are the types of risks Mindgard uncovers?

Mindgard identifies various AI security risks, including jailbreaking, extraction, evasion, inversion, poisoning, and prompt injection.

Why is it important to test instantiated AI models?

Testing instantiated models is crucial because it ensures that AI systems function securely in real-world scenarios. Deployment can introduce new vulnerabilities that are not apparent during development, and continuous testing helps mitigate these risks.

Mindgard