Mindgard

Mindgard is an AI security platform that red teams models, agents and applications with attacker-style reconnaissance and adaptive attacks, then supplies evidence, remediation guidance and runtime protection. It is built on Lancaster University research.

Mindgard is an AI security platform built around offensive testing. Instead of only filtering prompts at runtime, it approaches a target the way an attacker would: it maps models, agents, tools, instructions and behaviors first, then plans and runs adaptive attacks to find weaknesses that can actually be exploited. The workflow is organized into four stages named Discover, Recon, Attack and Defend, and the product is aimed at security teams, engineering groups and governance staff responsible for AI chatbots, applications, infrastructure and agentic workflows. In practice, the Discover stage covers AI asset inventory topics such as an AI bill of materials, shadow AI exposure and automated infrastructure crawling. Recon enumerates the attack surface and profiles agents, including attempts to bust through guardrails. The Attack stage handles automated red teaming, agent security testing and compliance-oriented risk reporting. Defend adds runtime protection and response, agent hardening and audits of existing defenses. Findings come with evidence and remediation guidance, and teams can retest fixes as systems change. The vendor says it can be running in minutes through APIs, CI/CD pipelines, Burp Suite or a single-click workflow, and that it shortens risk assessments from weeks to hours. The company traces its technology to more than a decade of AI security research at Lancaster University. It says its work has surfaced over 150 publicly disclosed vulnerabilities, with published cases involving Google's Antigravity IDE, the Zed IDE, OpenAI's Sora and xAI's Grok. That research feeds a proprietary knowledge base meant to prioritize proven attack paths over large volumes of generic test output. Among alternatives, Mindgard positions itself against open-source frameworks such as Garak, PyRIT and Promptfoo, which demand configuration and upkeep by the user, and against AI security vendors with overlapping discovery, testing and runtime features. It also distinguishes itself from guardrails and AI firewalls by testing whether those controls hold up, rather than replacing them. Pricing is not published on the homepage, so buyers start with a demo.