Description
Mindgard is an AI security platform designed to automate the process of red teaming and security testing for AI models, agents, and applications. The platform's core function is to discover, assess, and defend AI systems against potential threats by mimicking adversarial reconnaissance techniques. By acting as an autonomous red teamer, Mindgard maps the AI attack surface, revealing how adversaries might discover and exploit AI agents and systems. This proactive approach exposes critical safety and risk implications, allowing development teams to address vulnerabilities before they lead to real-world impact.
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The platform employs continuous analysis and runtime protection mechanisms to help teams find, fix, and stop attacks in their tracks. Mindgard's capabilities extend across various aspects of AI security, including AI agent evaluation and security scanning, AI-BOM (Bill of Materials) for shadow AI risk exposure, and automated AI infrastructure crawling. It also offers recon AI attack surface enumeration, psychometric agent profiling, and agent profile and guardrail busting.
Mindgard's approach is built on attacker-style reconnaissance, which allows it to uncover exploitable risks rather than just noise. This focus on high-impact findings is a result of over a decade of AI security research originating from Lancaster University. The platform is designed for operational efficiency, deployable through CI/CD pipelines, Burp Suite, or with a single click, making advanced AI security insights accessible without requiring in-house AI security specialists. It integrates seamlessly with existing enterprise AI workflows, APIs, and CI/CD support, securing AI across production environments and infrastructure, from open-source models to managed AI platforms.
Key differentiators include agent-native reconnaissance, which profiles AI systems from an attacker's perspective to map models, agents, tools, and behaviors before attack execution. This method surfaces higher-impact vulnerabilities more rapidly than traditional prompt-heavy approaches. Mindgard's research has contributed to over 100 public disclosures of vulnerabilities in leading AI systems, underscoring its effectiveness. The platform provides actionable AI security insights, enabling teams to secure their AI systems comprehensively.
- CategoryAI Cybersecurity Tools
- Pricing ModelContact sales
- DeploymentCI/CD, Burp Suite, Single Click
- PlatformsWeb
Mindgard AI Security's Core Features
Automated AI Red Teaming
AI Model Security Testing
AI Agent Security Assessment
AI Application Security
Attack Surface Mapping
Vulnerability Discovery
Risk Assessment
Runtime Protection
Agent-Native Reconnaissance
Exploitable Risk Detection
CI/CD Integration
API and Workflow Integration
AI Governance and Compliance Reporting
How to use Mindgard AI Security?
Discover: Map AI systems and identify potential attack vectors.
Assess: Evaluate AI models, agents, and applications for vulnerabilities.
Red Team: Simulate adversarial attacks to uncover exploitable risks.
Defend: Implement runtime protection and hardening measures.
Remediate: Fix identified vulnerabilities to secure AI systems.
Monitor: Continuously analyze and protect AI environments.
Mindgard AI Security's Use Cases
- AI Vulnerability Discovery
- AI Attack Surface Mapping
- Automated Red Teaming
- AI Agent Security
- Runtime AI Protection
- Shadow AI Risk Management
