The Governance Gap in Enterprise AI
Governance for Autonomous AI Agents in IT Operations
The Challenge: Enterprises are deploying autonomous AI agents across IT operations—but without standardized controls. Traditional access management wasn't designed for agents that can read, modify, and delete data autonomously. The result? Compliance gaps, security blind spots, and operational risk that grows with every new agent deployment.
The Solution: RMACD provides the rigorous, structured governance framework IT teams need. Define granular permissions across five operations (Read, Move, Add, Change, Delete), four data classification levels, and configurable autonomy controls. Bring the same discipline ITIL brought to service management—now for the agentic era.
Three Dimensions of Control
RMACD extends traditional permission models with data classification and autonomy controls, creating comprehensive governance for AI agents.
Operations
Read, Move, Add, Change, Delete — the five fundamental actions an agent can perform on enterprise resources.
Data Classification
Public, Internal, Confidential, and Restricted — permissions vary based on the sensitivity level of the data being accessed.
Autonomy Level
From fully supervised to fully autonomous — define how independently an agent can act without human approval.
Five Core Operations
Each operation represents a distinct type of action that AI agents may need to perform within enterprise systems.
Read
View, query, and retrieve information without making changes.
Move
Relocate resources between locations, systems, or classifications.
Add
Create new resources, records, or configurations.
Change
Modify existing resources, update configurations or settings.
Delete
Remove resources, revoke access, or archive data permanently.
Get Started
Explore the framework documentation, create your first agent profile, or validate existing profiles against the schema.