Mindgard gives organizations the visibility and protection to discover, assess, and defend their AI systems and agents.
Mindgard’s research has produced 150+ publicly disclosed AI vulnerabilities. The intelligence gained from those discoveries continuously strengthens the platform, helping organizations identify exploitable attack paths, including emerging agentic AI threats, and stay ahead of evolving attacks.
Mindgard secures AI models, agents, and applications against exploitable threats. Spun out of more than a decade of pioneering AI security research at Lancaster University, Mindgard combines offensive security expertise, ai red teaming, attacker intelligence, and automated protection to help organizations discover, assess, and defend their AI agents and systems.
Mindgard's values guide our actions and decisions. These principles form the foundation of our company's culture, shaping how we interact within our teams and with our clients. They inspire us to improve continuously and help us navigate the dynamic landscape of the AI security industry.
Mindgard was founded on pioneering research by Dr. Peter Garraghan at Lancaster University, which showed traditional AppSec could not address AI-specific risks.
Seed round led by top security investors, validating demand for an offensive-security approach to AI and the thesis that effective defenses must emulate real attacker behavior.
Expanded leadership with key hires: CEO James Brear, Head of Research Aaron Portnoy, and Offensive Security Lead Rich Smith, accelerating the research-led foundation.
Secured Fortune 500 design partners, validating enterprise demand for attacker-aligned AI security.
Mindgard helps organizations discover, assess and defend their AI systems. Spun out of more than a decade of AI security research at Lancaster University in the UK and headquartered in Boston and London, Mindgard operationalizes the expertise of AI researchers and offensive security practitioners through a Security Platform that performs Shadow AI discovery, AI red teaming, and run-time AI protection to assess and mitigate risk across models, agents, and applications.
