Virtue AI

What Virtue AI does

Virtue AI sets the standard for advanced AI security platforms. Built on decades of foundational and award-winning research in AI security, its AI-native architecture unifies automated red-teaming, real-time multimodal guardrails, and policy-driven governance for enterprise agents, models, and apps. Virtue AI safeguards every MCP, input, and output—spanning text, code, image, video, and audio—in over 100 languages, detecting risks in sub-10 ms. It deploys in minutes across cloud, on-prem, or SaaS environments, or integrates directly with tools like ChatGPT and VS Code. As an authenticated third-party partner, Virtue AI provides enterprises with unified visibility and assurance throughout the AI lifecycle, driving innovation without slowing performance. The platform is fueled by award-winning research and secures every agent, model, and app in an enterprise, with deployment possible in 48 hours. It offers sub-10ms, multimodal guardrails with audit-ready compliance, full observability, and Shadow AI detection, all running on a purpose-built inference framework. Virtue AI aims to be the trust layer for AI and agentic systems, ensuring security, governance, and compliance.

Products

• AgentSuite-RedThe industry's first enterprise-scale, security-focused testing ground for agentic systems, providing red-teaming built for how agents are exploited, including direct and indirect prompt injection. It offers 50+ sandboxed environments across 14 high-stakes domains and supports major frameworks. It includes an autonomous red-teaming agent with reusable attack skills and an Injection MCP Server.
• AgentSuite-BlueA scalable security, governance, and compliance suite for agentic systems, powered by purpose-built models and optimized for low latency. It includes MCP Guard to scan tools and source code for vulnerabilities, Action Guard to monitor and block malicious tool calls, and Shadow AI to surface unsanctioned agents and apps.
• VirtueRedContinuous, automated red-teaming for real-time API vulnerability assessment and pen-testing of multimodal AI systems, ensuring security testing evolves as AI models are updated.
• VirtueGuardVirtue AI's family of real-time AI safety models and policy guardrails. It provides multimodal, multilingual AI guardrails to block harmful content and enforce policy across text, code, image, video, and audio in 100+ languages, running on the Prelude inference framework for sub-10ms response times.
• PolicyGuardEnables complete control over how AI policy is defined and enforced across agents, models, and applications. Policies can be written in natural language or extracted automatically from existing documents.
• Shadow AIA feature within AgentSuite-Blue and a standalone capability that surfaces unsanctioned agents and apps running across cloud and endpoints, identifying who is using them, how often, and what they are doing. It's an endpoint-level discovery and monitoring layer built specifically for AI tools and AI agents.
• ActionGuardProvides stateful, real-time guardrails for every agent action, alerting teams about insecure or out-of-policy actions and reducing the risk of regulatory violations. It enables secure, scalable agent autonomy.
• MCPGuardPerforms continuous security analysis of MCP servers to validate them with low overhead, onboard them with confidence and speed, and reduce financial exposure from downstream remediation.
• Agent ForgingGroundSimulates 50+ enterprise environments across 14 high-stakes domains to test agents with real-world tasks before they interact with real data.
• PreludeVirtue AI's in-house inference framework built to accelerate prefill and minimize end-to-end latency for real-time guardrails.
• AutoRedTeamerA unified framework for fully automated, end-to-end red teaming against LLMs, comprising five specialized modules and a novel memory-based attack selection mechanism. It supports both seed prompt and risk category inputs.

Key capabilities

• AI-native architecture
• Automated red-teaming
• Real-time multimodal guardrails
• Policy-driven governance
• Sub-10ms risk detection
• Multilingual support (100+ languages)
• Cloud, on-prem, or SaaS deployment
• Unified visibility and assurance throughout the AI lifecycle
• End-to-End Agent Security
• Shadow AI detection
• Audit-ready compliance
• Full observability
• Purpose-built inference framework (Prelude)
• Integration with existing agent frameworks and apps (OpenAI, Google, LangChain, OpenClaw, Claude Code)
• 50+ sandboxed environments for red-teaming
• Autonomous red-teaming agent with reusable attack skills
• Injection MCP Server
• MCP Guard for tool and source code scanning
• Action Guard for real-time agent behavior monitoring
• Continuous, automated red-teaming
• Multimodal, multilingual AI guardrails (text, code, image, video, audio)
• Real-time speed with super-low latency
• Policy definition and enforcement (PolicyGuard)
• 50+ compliance frameworks (EU AI Act, GDPR, FINRA, ML Commons)
• Cloud & On-Prem Flexibility
• Agent Observability and Access Control
• Defense-in-depth model
• Tunable thresholds for sensitivity
• Comprehensive identification of potential vulnerabilities
• Automated, multimodal red-teaming for efficiency
• Comprehensive, audit-ready reporting
• Advanced, proprietary algorithms to identify corner cases across 1000+ risk categories

Use cases

• Securing enterprise agents, models, and apps
• Automated red-teaming for agentic systems
• Real-time multimodal guardrails for AI outputs
• Policy-driven governance for AI systems
• Detecting Shadow AI (unsanctioned agents and apps)
• Preventing drift in AI agents
• AI risk assessment
• Accelerating AI adoption without increasing risk
• Securing agentic systems in dynamic, stateful environments
• Testing agents against direct and indirect prompt injection
• Runtime protection for agents
• Monitoring agent behavior and blocking malicious tool calls
• Analyzing agent source code and capturing agent trajectory
• Securing core AI assets with multilingual, multimodal guardrails
• Centralizing context, control, and compliance for AI
• Ensuring alignment with global standards (GDPR, FINRA, HIPAA, EU AI Act)
• Continuous, adaptive red-teaming across agents, models, and apps
• Preventing unsafe, malicious, or out-of-policy agent actions
• Finding breaking points in agents before attackers do
• Blocking harmful content and enforcing policy across various modalities and languages
• Defining and enforcing AI policies enterprise-wide
• Simplifying audits with centralized, traceable compliance
• Prompt Injection Defense in RAG Systems (financial services)
• Toxic Response Filtering in Banking Assistants
• PII & Sensitive Data Leak Prevention (financial services)
• Hallucination Detection in Financial Advice
• Red Team Scanning for Finance Chatbot Compliance
• Unauthorized Financial Advice Prevention
• Real-time text guardrails for generative AI systems (finance)
• API vulnerability assessment and pen-testing of multimodal AI systems
• Real-time moderation for chatbots and dialogue (Uber)
• Driver-passenger chat moderation
• Merchandise-related communications moderation
• Customer service dialogues moderation
• Evaluating safety trade-offs between different models
• Validating MCPs with low overhead
• Onboarding MCPs with confidence and speed
• Reducing financial exposure from downstream remediation
• Creating a comprehensive inventory of AI usage
• Strengthening governance and audit readiness for agents
• Visibility into conversations, actions, tool calls, and token traces for agents
• Detecting vulnerabilities and security issues in agent configuration
• Benchmarking agent performance

AI approach

Virtue AI provides an AI-native security platform built on foundational research in AI security. It unifies automated red-teaming, real-time multimodal guardrails, and policy-driven governance for enterprise agents, models, and applications. The platform uses proprietary models and algorithms for its guardrails and red-teaming capabilities, detecting risks in sub-10 ms across various modalities and languages. They translate academic breakthroughs into enterprise defenses rapidly.

Tech named: AI-native architecture, automated red-teaming, real-time multimodal guardrails, policy-driven governance, purpose-built inference framework, proprietary models, proprietary red-teaming algorithms, Prelude (in-house inference framework), MMLU (Massive Multitask Language Understanding), DecodingTrust, DecodingTrust-Agent Platform (DTap), AutoRedTeamer

Industries served

• Financial Services
• Healthcare
• Insurance
• Government
• Fortune 500
• IT / Tech

What it says sets it apart

• Built on decades of foundational and award-winning research in AI security
• AI-native architecture
• Unifies automated red-teaming, real-time multimodal guardrails, and policy-driven governance
• Safeguards every MCP, input, and output across text, code, image, video, and audio
• Detects risks in sub-10 ms
• Supports over 100 languages
• Deploys in minutes across cloud, on-prem, or SaaS
• Integrates directly with tools like ChatGPT and VS Code
• Authenticated third-party partner for unified visibility and assurance
• Research-to-product loop turns academic breakthroughs into enterprise defenses in days
• Purpose-built inference framework (Prelude) for enterprise speed and compliance
• First end-to-end security platform purpose-built for agentic AI
• Proprietary, purpose-built models for real-time guardrails
• 50+ compliance frameworks can be layered with custom policies
• Pioneered the first adversarial machine learning frameworks (2014)
• Developed MMLU (Massive Multitask Language Understanding), the industry's most popular benchmark
• Open-sourced Decoding Trust, a unified platform for LLM risk assessment, winning awards
• Launched the first end-to-end security and governance platform for the autonomous enterprise (2024)
• 100+ proprietary red-teaming algorithms and skills, built in-house and exclusive
• Less than 48 hours from research to product deployment
• Team of award-winning AI security researchers with decades of experience
• Superior performance in identifying vulnerabilities compared to existing manual and optimization-based approaches (AutoRedTeamer)
• Higher attack success rates by 20% on HarmBench against Llama-3.1-70B while reducing computational costs by 46% (AutoRedTeamer)

Funding rounds we track

From the AI funding tracker — rounds as reported by the linked publications.