AI RUNTIME SECURITY
Control risky agent actions at runtime
AI agents can make decisions and take actions across enterprise systems. Eyrane helps security teams evaluate those actions in context and apply policy before sensitive operations execute.
+ production
+ no approval↓REVIEW REQUIRED
THE DEFINITION
What is AI runtime security?
AI runtime security protects AI systems while they are operating by monitoring actions, evaluating context, and enforcing policy before risky behavior affects enterprise systems or data. For AI agents, runtime security can include identity, permissions, tool usage, data sensitivity, requested actions, and autonomy.
- Monitor agent actions
- Evaluate context before execution
- Identify risky tool calls
- Protect sensitive resources
- Require approval for high-risk actions
- Block unauthorized operations
- Record actions for investigation
THE ACTION BOUNDARY
Agents make security decisions dynamic
Agents may dynamically choose tools, data, APIs, sequences, and strategies. A permission check is important, but it does not explain whether the action fits the current task, resource, environment, and policy.
POLICY OUTCOMES
Apply policy at the moment of action
Read approved product documentation.
Issue a customer refund above a threshold.
Export restricted customer records externally.
RUNTIME CONTEXT
Make decisions with context
A runtime decision can consider more than an identity and an API request.
CONCEPTUAL POLICIES
Turn security intent into agent policy
Express decisions around context, sensitivity, identity, and action.
IDENTITY-AWARE ENFORCEMENT
Runtime policy starts with identity
Context matters because an action is only as safe as the identity, permissions, and environment behind it.
Explore Agent Identity & PermissionsTOOL-AWARE SECURITY
Know what happens when an agent calls a tool
Runtime security should evaluate the full path an action takes:
MCP RUNTIME SECURITY
Apply policy across MCP actions
MCP can connect agents to external tools and data. A runtime decision should account for the server, tool, credential, downstream system, and requested action.
Explore MCP SecurityPROMPT INJECTION → ACTION
Stop prompt injection from becoming an action
The goal is not only to detect suspicious input. It is to prevent unsafe consequences.
EXCESSIVE AGENCY
Control how much autonomy an agent has
An agent may have permission to perform an action while policy still requires human oversight.
Explore Excessive AgencyDATA-AWARE SECURITY
Protect sensitive data during agent actions
Reading data and moving data are different risks. Runtime policy can distinguish an approved internal read from an external transfer.
Explore Data ExfiltrationMULTI-STEP ACTIONS
Evaluate the full chain, not just one tool call
Individually reasonable steps can combine into a higher-risk workflow.
HUMAN-IN-THE-LOOP
Keep people in control of sensitive actions
Some actions should require approval instead of being blocked outright, preserving agent productivity while limiting high-impact autonomy.
OBSERVABILITY + AUDIT
Record what the agent did and why
Runtime security should preserve enough context for a security team to investigate what happened, which identity authorized it, and what changed.
AI RED TEAMING
Test runtime controls before deployment
Red teaming should test whether prompt injection, tool abuse, privilege escalation, data leakage, or multi-step workflows can bypass controls.
COMMON QUESTIONS
Questions security teams ask about runtime security
What is AI runtime security?
AI runtime security protects AI systems while they operate by monitoring actions, evaluating context, and enforcing policy before risky behavior affects enterprise systems or data.
Why do AI agents need runtime security?
AI agents need runtime security because they can dynamically choose tools, data, APIs, and action sequences that static controls may not evaluate in context.
How is AI runtime security different from LLM security?
LLM security often focuses on model inputs and outputs. AI runtime security also evaluates the identities, tools, resources, permissions, and actions around an agent.
What is runtime policy for an AI agent?
Runtime policy is a set of contextual rules used to decide whether an agent action should be allowed, reviewed, or blocked before execution.
How can enterprises control AI agent actions?
Enterprises can control AI agent actions by evaluating the agent, identity, tool, resource, data sensitivity, environment, autonomy, and policy context before an action executes.
What does allow, review, or block mean for AI agents?
Allow lets an expected low-risk action proceed. Review routes a sensitive action for approval. Block stops an action that violates policy or lacks required context.
How does identity affect runtime security?
Identity affects runtime security because the same action can have different risk depending on the permissions, ownership, and environment behind the identity.
How does prompt injection create runtime risk?
Prompt injection creates runtime risk when untrusted instructions influence an agent to attempt a privileged tool call or unsafe action.
How does MCP affect AI runtime security?
MCP can connect agents to tools and downstream systems, so runtime policy must account for the server, tool, credential, resource, and requested action.
What is human-in-the-loop security?
Human-in-the-loop security requires approval for selected high-impact actions while allowing lower-risk agent work to continue.
How can security teams monitor autonomous AI agents?
Security teams can monitor autonomous AI agents by recording the agent, identity, tool, resource, policy, decision, approval, timestamp, and outcome for each action.
AI RUNTIME SECURITY
Control what happens when agents act.
Apply security context and policy before sensitive AI agent actions affect enterprise systems or data.