Move the security story into the product architecture with policy administration, controls, evidence, and oversight for AI interactions, agents, and MCP servers.
Governance, risk, compliance, policy administration, and security controls for adopting AI safely at enterprise scale.
Admins can define, update, and monitor AI policies as enterprise requirements change.
Source, dependency, and posture checks help teams review MCP servers before adoption.
Source and runtime observations help teams find agent activity that has not entered the governed catalog.
Find unmanaged agent activity across source code and runtime traffic, then bring useful discoveries into a governed review workflow.
Create a practical governance layer for how AI systems are approved and operated.
Give security and GRC teams a dedicated place to define, maintain, and review the policies that shape AI behavior.
Analyze the cost of AI usage across the organization so platform, finance, and governance teams can understand how adoption affects spend.
Give GRC teams a consistent way to reason about AI risk and evidence.
Apply enterprise controls where AI systems touch users, tools, and data.
Administer policies, review audit trails, monitor runtime health, and use MCP evidence to keep AI activity visible, reviewable, and controlled.
Create and manage policies to protect sensitive information
Talk with CorpAI about the product path, deployment model, and controls that fit your enterprise environment.