Mindgard

Mindgard: Automated AI Red Teaming & Security Testing

What is Mindgard?

Mindgard is an AI security testing solution that helps organizations secure their AI systems from emerging threats. Traditional application security tools can't address the unique risks associated with AI, making Mindgard a necessary layer of protection.

How does Mindgard work?

Mindgard's Offensive Security for AI solution uses automated red teaming to identify and resolve AI-specific risks that are only detectable during runtime. It integrates into existing CI/CD automation and all stages of the SDLC. Connecting Mindgard to your AI system requires only an inference or API endpoint for model integration. The platform supports a wide range of AI models, including Generative AI, LLMs, NLP, audio, image, and multi-modal systems.

Why is Mindgard important?

The deployment and use of AI introduces new risks that traditional security tools can't address. Many AI products are launched without adequate security, leaving organizations vulnerable. Gartner research indicates that 29% of enterprises with AI systems have already reported security breaches, but only 10% of internal auditors have visibility into AI risk.

Key Features and Benefits

• Automated Red Teaming: Simulates attacks on AI systems to identify vulnerabilities without manual intervention.
• AI-Specific Risk Identification: Identifies and resolves AI-specific risks such as jailbreaking, extraction, evasion, inversion, poisoning, and prompt injection.
• Continuous Security Testing: Provides continuous security testing across the AI SDLC.
• Integration with Existing Systems: Integrates into existing reporting and SIEM systems.
• Extensive Coverage: Works with various AI models and guardrails, including LLMs like OpenAI, Claude, and Bard.

Use Cases

• Securing AI Systems: Protects AI systems from new threats that traditional application security tools cannot address.
• Identifying AI Vulnerabilities: Uncovers and mitigates AI vulnerabilities, enabling developers to build secure, trustworthy systems.
• Continuous Security Assurance: Provides continuous security testing and automated AI red teaming across the AI lifecycle.

What types of risks Mindgard uncovers?

Mindgard identifies various AI security risks, including:

• 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.

Awards and Recognition

• Winner of Best AI Solution and Best New Company at the SC Awards Europe 2025.
• Featured in TechCrunch, Sifted, and other publications.

FAQs

• What makes Mindgard stand out from other AI security companies? Mindgard boasts over 10 years of rigorous research in AI security, ensuring access to the latest advancements and qualified talent.
• Can Mindgard handle different kinds of AI models? Yes, Mindgard is neural network agnostic and supports a wide range of AI models.
• How does Mindgard ensure data security and privacy? 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.
• 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.
• What types of organizations use Mindgard? Mindgard serves a diverse range of organizations, including those in financial services, healthcare, manufacturing, and cybersecurity.
• Why don't traditional AppSec tools work for AI models? The deployment and use of AI introduces new risks 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. Securing these risks requires a fundamentally new approach.

Conclusion

Mindgard is a comprehensive AI security testing solution that helps organizations protect their AI systems from emerging threats. With its automated red teaming capabilities, AI-specific risk identification, and continuous security testing, Mindgard enables developers to build secure and trustworthy AI systems. By integrating Mindgard into the AI SDLC, organizations can ensure their AI models and applications operate securely and reliably.