AI & Agent Workflow Security
Your AI agents are autonomous actors with real access to real systems. They can take actions, call APIs, read data, and execute code — and they can be manipulated, compromised, or simply over-permissioned. AgentShield is the first purpose-built security platform for the AI-native enterprise.
AgentShield™ Protection Scope
AgentShield runs a continuous security lifecycle for every AI agent in your enterprise — from the moment it’s deployed to the moment it’s decommissioned, and every action in between.
AgentShield automatically discovers every AI agent, LLM application, MCP server, autonomous workflow, and agent framework across your enterprise — including the ones your security team didn’t know existed. Shadow AI is a real and growing problem; AgentShield solves it at the discovery layer before anything else can happen.
Every agent is issued a formal identity with authentication, authorization, and lifecycle management. AgentShield inventories every tool, API, and data source each agent can access — and continuously evaluates whether those permissions are appropriate, enforcing least-privilege at the agent layer.
AgentShield monitors every agent in real time — observing tool invocations, API calls, data access, and workflow execution. Every prompt is analyzed for injection attempts, jailbreak patterns, context manipulation, and indirect injection before it reaches the model.
AgentShield continuously computes dynamic trust scores for every agent based on identity verification, behavioral history, tool access patterns, data access risk, and current activity. Trust scores feed into TrustAnchor’s enterprise trust model and Vanguard’s response decisions.
When trust thresholds are violated or compromise is detected, AgentShield automatically suspends agents, restricts tool access, revokes credentials, blocks workflow execution, and isolates MCP servers — stopping malicious AI activity before it completes its intended action.
Continuously discovers AI agents, LLM applications, MCP servers, autonomous workflows, and agent frameworks — including those deployed without security team awareness.
Protects Model Context Protocol infrastructure by discovering servers, inventorying exposed tools, analyzing permissions, governing access controls, and evaluating trust across the entire MCP environment.
Detects prompt injection, indirect prompt injection, jailbreak attempts, context manipulation, and prompt poisoning attacks in real time — before they reach the model and before malicious instructions can execute.
Continuously observes agent behavior, tool usage patterns, API access frequency, data access scope, and workflow execution — detecting anomalies and policy violations as they emerge.
Provides formal identity, authentication, authorization, and lifecycle management for every AI agent — plus dynamic trust scoring that continuously reflects actual agent behavior and risk.
Validates every tool invocation request before execution — checking authorization level, data sensitivity, risk impact, and policy compliance in real time, blocking unauthorized actions before they complete.
A SaaS company running AgentShield discovered 140 AI agents deployed by engineering teams that had never been registered with security — including 23 with direct production database access and 8 with customer data read permissions obtained through OAuth grants.
AgentShield detected a sophisticated indirect prompt injection attack targeting an internal customer service AI agent through manipulated content in a customer email. The injected instruction would have instructed the agent to initiate an unauthorized funds transfer via a connected banking API.
A healthcare system deployed AgentShield to govern AI agents accessing patient data systems — enforcing least-privilege permissions, monitoring every data access event, and producing continuous audit evidence of agent behavior for HIPAA compliance.
AgentShield discovered that three internally deployed MCP servers were exposing sensitive operational tools to any authenticated agent — including tools that could modify network configurations and access SCADA system APIs. None of these exposures had been identified by existing security tooling.
AgentShield feeds agent discovery data to Atlas, agent risk signals to Overwatch AI, trust scores to TrustAnchor, and containment actions to Vanguard — making the entire platform AI-aware.
AgentShield governs, monitors, and protects every AI agent across your enterprise — so you can deploy AI without introducing uncontrolled risk.
