01 / Data flow
Audit every data path across tools.
Trace the data entering each agent session, classify what came from users, tools, memory, and connected systems, and stop sensitive or untrusted context from being reused where it does not belong.
Control what agents can do in production
Give agents room to work inside clear boundaries.Consequential actions clear your controls first.
Runtime controls for agent actions
Autonomous Action Runtime Management (AARM) turns proposed agent actions into explicit control points teams can inspect, constrain, and approve before MCP servers or tools execute.
Aligned with recognized security and AI assurance frameworks
Where agent actions get checked
02 / Tool calls
Stop sensitive data leakage before execution.
Evaluate the full chain of MCP and tool calls before execution, catching malicious combinations, cross-tool data movement, and attempts to turn harmless individual actions into an unsafe workflow.
03 / Policy
Enforce policy before agents act.
Apply runtime policies at the point of action so agents can only use approved capabilities, identities, data paths, and environments before requests reach production systems.