Mindgard was created from more than a decade of AI security research at Lancaster University and embeds offensive security and AI research expertise directly into its platform. Its technology has identified more than 150 publicly disclosed vulnerabilities across prominent AI systems. This research-led, attacker-aligned approach gives enterprises evidence-based insight into how their AI could be exploited and what they need to do about it.

Mindgard goes beyond identifying vulnerabilities. It validates which weaknesses are exploitable, explains their potential impact, and provides evidence and guidance to help teams close them. Teams can retest fixes, verify that defenses remain effective, and deploy runtime protections where needed. This creates a continuous process of finding, proving, remediating, and verifying AI risk as systems change.

Mindgard can be operational in minutes through APIs, CI/CD pipelines, Burp Suite, or a single-click workflow. It integrates AI security into existing development, engineering, and security processes so teams can assess systems before deployment and continuously in production. Mindgard reduces AI risk assessment from weeks to hours without requiring organizations to build a specialist AI security function or assemble and maintain multiple testing tools.

Garak, PyRIT, and Promptfoo give technical teams useful frameworks for building and running AI security tests. However, organizations must configure, operate, maintain, and interpret these tools themselves. Mindgard provides an enterprise-ready offensive security platform that can be operational in minutes, reduces AI risk assessments from weeks to hours, and adds continuous testing, risk prioritization, remediation guidance, workflow integrations, executive reporting, and GRC evidence.

These companies offer overlapping capabilities across AI discovery, adversarial testing, safety evaluation, guardrails, runtime protection, and posture management. Mindgard differentiates through offensive security built specifically for AI systems and agents. Its attacker-style reconnaissance, adaptive attack chaining, proprietary vulnerability intelligence, and focus on proven exploitability help teams identify the vulnerabilities most likely to cause breaches and take evidence-based action to close them.

AI guardrails and firewalls apply policies to prompts, responses, or actions at runtime. Mindgard tests whether those controls actually work. It conducts adaptive attacks to identify where guardrails hold, degrade, or fail as AI systems change. Mindgard also goes beyond the prompt layer by assessing agents, tools, permissions, APIs, data, and infrastructure, providing evidence teams can use to strengthen and verify their defenses.

Mindgard begins by examining AI systems the way an attacker would. Its agent-native reconnaissance maps models, agents, tools, instructions, and behaviors before attacks are planned and executed. Intelligence from more than 150 publicly disclosed AI vulnerabilities continuously strengthens its proprietary knowledge base. This helps Mindgard prioritize proven attack paths and exploitable defensive gaps instead of producing large volumes of generic test results.

Mindgard follows a continuous discover, recon, attack, and defend process. It identifies AI assets and attack surfaces, performs reconnaissance to understand system behavior, and uses adaptive attacks to find and validate exploitable vulnerabilities. Mindgard then provides evidence and remediation guidance, verifies whether defenses work, and continuously reassesses systems as models, prompts, tools, configurations, and policies change.

Mindgard secures AI applications, agents, models, chatbots, agentic workflows, APIs, connected tools, and supporting infrastructure. It can assess systems built with open-source or managed models, including RAG applications, tool-using agents, and systems connected through MCP or A2A servers. Mindgard examines the complete AI system because attackers exploit interactions between models, agents, tools, data, and infrastructure, not just the model in isolation.

Mindgard is built for security and engineering teams accountable for AI systems in development and production. This includes application security, product security, red team, AI and ML engineering, governance, risk, and compliance teams. It is particularly valuable for organizations that need greater visibility into AI risk but lack the specialist knowledge or resources required to assess rapidly changing AI systems manually.

Mindgard is an AI security platform with advanced AI red teaming at its core. It goes beyond running predefined attacks by discovering AI assets, performing attacker-style reconnaissance, planning adaptive attack paths, validating defensive gaps, and helping teams remediate them. Mindgard also supports runtime protection, security reporting, and continuous policy compliance across the AI lifecycle.

Mindgard is an offensive security platform purpose-built for AI systems and agents. It helps security and engineering teams discover the vulnerabilities most likely to cause breaches, validate where defenses fail, and understand how to close those gaps. Mindgard provides continuous visibility into AI risk across development and production as applications, agents, models, tools, and policies change.