Mindgard is an AI security platform that discovers, tests, and defends AI models and applications against adversarial attacks and emerging threats.
Mindgard covers the full AI security lifecycle. Its discovery module inventories AI assets across the organization, including shadow deployments teams forgot about. Automated testing then attacks models with adversarial techniques drawn from a continuously updated threat library — jailbreaks, prompt injection, evasion, and data-poisoning probes — producing risk scores with reproducible evidence. Runtime defense adds monitoring and blocking for deployed models, and integrations feed findings into existing security workflows. The platform works across model types and frameworks, including computer vision and language models, and its research team publishes new attack classes as the field evolves. For organizations shipping AI features quickly, Mindgard provides the security assurance layer that traditional application testing does not cover. Compliance reporting maps findings to emerging AI standards and regulations, which helps security leaders demonstrate due diligence as AI-specific governance requirements take effect worldwide.
Mindgard offers trial evaluations for initial asset scanning and testing. Paid plans scale by the number of models and applications covered, with enterprise agreements adding private deployment and support.
Organizations deploying AI models in production that need security testing purpose-built for machine learning, and security teams extending application security programs to cover AI assets. It fits companies shipping AI features on aggressive timelines who need assurance that grows with their model portfolio rather than one-off audits. Model developers get reproducible test reports they can use to prioritize hardening work before releases.