AI AGENT SECURITY
Security built for AI agents
AI agents can access data, call tools, use credentials, and take actions across enterprise systems. Eyrane helps security teams discover those agents, understand what they can reach, and control risky behavior at runtime.
REQUESTED ACTION
Export customer recordsReview RequiredTHE DEFINITION
What is AI agent security?
AI agent security is the practice of protecting AI systems that can independently access data, use tools, call APIs, and take actions. It includes discovering agents, managing their identities and permissions, monitoring runtime behavior, testing for vulnerabilities, and controlling unsafe or unauthorized actions.
- Inventory agents and connected resources
- Understand agent identities and permissions
- Identify excessive access
- Monitor tool and API activity
- Prevent or review risky actions
WHY THE MODEL CHANGES
AI agents change the security model
Predefined workflows execute known logic.
- Fixed paths
- Explicit integrations
- Known action sequence
An agent may decide what to do next based on context.
- Select a tool or data source
- Chain multiple actions
- Act through user or service credentials
- Respond to untrusted external content
DISCOVER → UNDERSTAND → CONTROL
Secure the full agent lifecycle
Eyrane connects the inventory, access context, and runtime decision so security teams can see the whole action path.
THE AGENT ATTACK SURFACE
Protect the agent attack surface
Agentic security spans the instructions, identities, tools, connections, and data that shape an action.
IDENTITY & PERMISSIONS
Every agent needs an identity boundary
Agents may operate using API keys, OAuth tokens, service accounts, user-delegated credentials, cloud roles, and application permissions.
Security teams need to understand not only which identity an agent uses, but the effective permissions that identity gives the agent.
Explore Agent Identity & PermissionsMCP SECURITY
Secure how agents connect to tools
Model Context Protocol allows AI applications and agents to connect to external tools, data sources, and services.
MCP can expand an agent’s reachable attack surface through additional permissions, untrusted tool descriptions, sensitive data access, external actions, and third-party dependencies.
Explore MCP SecurityRUNTIME SECURITY
Security decisions at the moment of action
Evaluate the context around an action before a sensitive operation proceeds.
AI RED TEAMING
Test agents before production
Agent testing should cover more than generated responses. It should challenge prompt handling, tool use, permissions, data paths, and multi-step behavior.
- Prompt injection
- Tool abuse
- Privilege escalation
- Data leakage
- Manipulated MCP responses
ONE ACTION PATH
See the whole path an agent can take.
COMMON QUESTIONS
Questions security teams ask about AI agent security
What is AI agent security?
AI agent security is the practice of protecting AI systems that can independently access data, use tools, call APIs, and take actions. It includes discovering agents, managing identities and permissions, monitoring runtime behavior, testing for vulnerabilities, and controlling unsafe or unauthorized actions.
How is AI agent security different from LLM security?
LLM security focuses mainly on model inputs, outputs, and behavior. AI agent security also addresses the identities, permissions, tools, data, and actions that let an agent affect enterprise systems.
Why do AI agents create new security risks?
AI agents may dynamically select tools, retrieve data, chain actions, and operate with user or service credentials. That makes the agent’s effective access and runtime behavior part of the security model.
How do permissions affect AI agent security?
Permissions determine what an agent can reach and what actions it can attempt. Security teams need to understand the effective permissions behind each agent identity, not only the intended workflow.
What is excessive agency?
Excessive agency is when an AI agent has more authority, autonomy, or action range than its purpose requires. It increases the possible blast radius of mistakes or manipulation.
How does MCP affect AI agent security?
MCP lets AI applications connect to external tools, data sources, and services. Those connections can add permissions, tool descriptions, sensitive data access, and external actions to an agent’s reachable attack surface.
What is runtime security for AI agents?
Runtime security evaluates what an agent is attempting at the moment of action, including the agent identity, requested tool, target resource, data sensitivity, and permission context.
How should enterprises secure autonomous AI agents?
Enterprises should inventory agents, map identities and effective permissions, understand connected tools and data, test attack paths, monitor runtime behavior, and apply policy to sensitive actions.
SECURITY FOR AI AGENTS
Put agents to work without giving up control.
Build and deploy AI agents with visibility into what they can access and what they do.