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Mindgard – AI Security Platform

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Mindgard’s AI Security Platform combines academic research and offensive security expertise to help enterprises discover, assess, and defend AI systems. The platform operates as an autonomous red teamer that continuously…

Independently listed · not pay-to-rankVerified vendor site: mindgard.aiListing updated Aug 27, 2026

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PricingNot provided
DeploymentNot specified
SupportNot specified
Compliance
SOC 2 Type II
AI/model discovery & inventory Yes
Red teaming & adversarial testing Yes
Guardrails & policy enforcement Yes
Continuous monitoring & drift detection Not stated
Compliance & audit reporting Yes
Agent & MCP security Yes
Risk assessment & scoring Yes

Mindgard’s AI Security Platform combines academic research and offensive security expertise to help enterprises discover, assess, and defend AI systems. The platform operates as an autonomous red teamer that continuously maps, plans, and executes agentic attack workflows to reveal exploitable vulnerabilities across models, agents, tools, APIs, and connected data sources. Mindgard automates recon and intelligence gathering to identify instructions, tools, and shadow AI behaviors that increase attack surface exposure, then leverages those findings to design targeted attack chains and run adversarial evaluations. The platform supports continuous risk mapping and assessment, validation of defenses, and runtime threat detection and response that applies context-driven guardrails and hardens system prompts. Reports and remediation guidance integrate into existing development and security workflows, providing clear visibility for engineering, security teams, and auditors. Spun out of university research and compliant with enterprise expectations, Mindgard focuses on high-impact exploit discovery and prioritized remediation to reduce real-world AI risk across development and production environments.

Capabilities

Asset Discovery (models, agents, MCP/A2A servers, shadow AI)
Automated Reconnaissance and Behavioral Analysis
Agentic Red Teaming (autonomous attacker emulation)
Attack Chain Design and Exploitation Testing
Runtime Detection & Response (context-driven guardrails)
AI Security Posture Management (continuous mapping & assessment)
Risk Assessment and Prioritization
Model & Artifact Scanning
Governance and Compliance Reporting
Integrations into CI/CD, ticketing systems, and security tooling
Remediation Guidance and Hardening of System Prompts

Integrations

(duplicate field removed by schema)

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