Mindgard has expanded its automated and continuous AI security platform with what it describes as the industry’s first autonomous reconnaissance capability for AI models, agents, and applications. With this launch, the company is giving enterprise security teams a faster and more cost-effective way to discover, assess, and defend AI deployments against both security and safety risks.

As organizations continue to embed AI into critical workflows, the security landscape is becoming more complex. AI systems no longer operate in isolation. Instead, they interact with tools, prompts, integrations, and external services that can create new openings for attackers. Because of this shift, security teams need greater visibility into how AI systems actually behave in live environments. Mindgard is addressing that challenge by automating one of the most time-consuming parts of AI security testing: reconnaissance.

At the center of the announcement is Mindgard Reconnaissance, a new capability that automates the intelligence-gathering phase of AI security assessments. Rather than relying on slow, manual investigation, security teams can now rapidly map the real attack surface of AI models, agents, and systems. As a result, they can better understand how these technologies function in production and where exploitable weaknesses may exist.

The platform identifies key elements such as guardrails, system prompts, tools, integrations, and external services. In turn, this visibility helps organizations uncover how agentic attack paths may form across connected AI environments. More importantly, the new capability allows security teams to move directly into targeted risk assessment. That means they can surface high-impact risks earlier, prioritize the most exposed areas, and focus their resources where they matter most.

Mindgard has built this platform on an attack library that originated from Lancaster University, which it describes as the world’s largest AI security laboratory. Drawing on more than a decade of AI security research and offensive security expertise, the company has positioned its platform to support enterprises that need deeper, research-backed protection as AI adoption accelerates.

The company also highlighted the platform’s recent impact. According to Mindgard, its technology is already being used by Fortune 500 security teams. In addition, over the last 90 days, the platform identified more than 80 publicly reported vulnerabilities across major AI technologies, including xAI’s Grok, OpenAI’s ChatGPT, and Google’s Antigravity IDE. This point is especially significant because it shows how AI vulnerabilities are emerging across widely used platforms, not just in niche or experimental environments.

By automating reconnaissance, Mindgard is helping organizations shorten the path from discovery to remediation. Instead of spending valuable time figuring out how an AI system is structured, security teams can immediately begin evaluating real exposure and taking action. Consequently, enterprises can strengthen AI security programs while also reducing assessment costs and improving operational efficiency.

“Mindgard’s research resulted in actionable vulnerability submissions that we were able to act on swiftly,” said John Swanson, Head of Security at Zed Industries. “Addressing these vulnerabilities hardened the Zed editor against a class of vulnerabilities common to development tools integrating AI, improving the security posture of Zed and our broader developer community as a whole.”

Overall, this launch strengthens Mindgard’s position in the growing AI security market. At a time when organizations need to secure increasingly dynamic AI systems, the company is offering a platform that combines automation, continuous monitoring, and offensive security insight to identify and fix exploitable vulnerabilities before they create larger business risks.

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