AI Security
Mindgard Platform
Automated red teaming and runtime protection for AI models, agents, and applications, with findings mapped to the EU AI Act, NIST AI RMF, OWASP LLM Top 10, and MITRE ATLAS.
Mindgard Platform Overview
Mindgard is an AI security platform that tests and defends generative AI systems against adversarial attack. It operates as an autonomous red teamer, running attacker-style reconnaissance and exploitation against the models, agents, guardrails, and AI applications an organization builds or buys. The platform works across the full lifecycle, from discovering shadow AI and mapping the attack surface to validating exploitability and applying runtime protection, rather than scanning models in isolation.
Automated red teaming continuously maps, plans, and executes multi-step agentic attack workflows, emulating real adversary behavior across one-shot and multi-step interactions rather than single prompts. It evaluates complete AI systems, examining how agents, tools, connected services, and data sources interact, and surfaces high-impact vulnerabilities with attacker context and remediation guidance. Continuous assessment re-tests systems as models and configurations change. At runtime, context-driven guardrails harden system prompts and block prompt injection and agentic manipulation, with findings mapped to the MITRE ATLAS and OWASP LLM Top 10 frameworks.
Mindgard holds SOC 2 Type II compliance and supports enterprise governance through SAML single sign-on, SCIM provisioning, and role-based access control. Spun out of more than a decade of AI security research at Lancaster University and founded in 2022, the company has published over 100 vulnerability disclosures across major AI and big-technology systems, including flaws in OpenAI, Google, and xAI products. This research record anchors its position in the emerging market for AI red teaming.
Key Capabilities
mapped to solution categoriesAttacks deployed guardrails, system prompts, and content filters to measure how reliably they block adversarial inputs, quantifying bypass rates rather than assuming the controls work.
Autonomously plans and executes multi-step adversarial campaigns against AI systems, emulating real attacker workflows across reconnaissance, exploitation, and escalation rather than running a fixed checklist of tests.
Discovers AI assets, including shadow models, agents, and inference endpoints, and maps the reachable attack surface to scope and target red-team campaigns. Offensive reconnaissance, distinct from posture inventory.
Routes high-value automated findings to specialist AI red teamers for manual exploitation, chaining, and depth beyond automated coverage, blending platform testing with human expertise.
Generates adversarial inputs across text, image, and audio modalities to test model evasion and misclassification, extending red teaming beyond text-only prompt attacks.
Tests LLMs and AI applications against a library of direct and indirect prompt-injection and jailbreak techniques, reporting which payloads bypass system instructions and safety controls.
Re-runs red-team campaigns continuously and at release gates in the CI/CD pipeline as models, prompts, and configurations change, catching new exploit paths before and after deployment.
Reports validated AI vulnerabilities with reproduction evidence, attacker context, and remediation guidance, mapped to the OWASP LLM Top 10, MITRE ATLAS, EU AI Act, and NIST AI RMF for auditable AI risk reporting.
Attacks AI agents through their tools, memory, and connected services using multi-step techniques such as tool misuse, goal hijacking, and indirect injection, surfacing exploit paths unique to autonomous agents.
Automatically discovers AI models, LLM API connections, ML pipelines, and AI-enabled SaaS applications in use across the organization, including those deployed without IT authorization.
Analyzes AI runtime behavior to surface prompt injection, anomalous data access, and model extraction as posture findings, exporting scores and telemetry to SIEM and SOAR rather than blocking inline.
Maps the AI inventory and controls to EU AI Act risk classification, ISO/IEC 42001, and NIST AI RMF, generating auditable evidence for each framework.
Discovers AI model and inference endpoints and flags public exposure, weak authentication, default credentials, or excessive permissions as posture misconfigurations.
Compliance
certificationsIntegrations
compatible toolsImplementation & support
Info last updated on June 13, 2026