A Continuous Control Plane for Enterprise AI and Agentic Workflows
Governing Scope, Memory, and Identity for AI Systems That Act, Learn, and Remember.
Abstract
AI agents are becoming long-running digital employees embedded in enterprise workflows, operating across systems of record with tools, memory, and varying degrees of autonomy. Traditional role-based access control (RBAC) and periodic GRC reviews were not designed to govern systems whose scope, behavior, and memory evolve continuously.
This paper introduces the Governor Model: a continuous control-plane that jointly manages an agent's scope, its memory, and the organizational identities and policies that constrain both. The Governor treats governance as an ongoing control loop — ingesting drift, access, provenance, and policy signals to adjust agent privileges in real time.
"Autonomy is increasing faster than governance primitives, and the Governor Model is designed to close that gap."
What this paper addresses
Unbounded scope
Agent scopes are loosely defined, allowing AI systems to read and act across datasets and tools with unclear limits on what they may access, copy, or propagate.
Drift and shifting objectives
As policies and business goals change, agents continue to act on outdated context and implicit objectives — misalignment that is hard to detect in real time.
High-speed, multi-system impact
Agentic workflows perform read/write operations across many systems of record in seconds, making human-centered approval workflows too slow to prevent harmful actions.
Opaque memory
AI systems accumulate and reuse context over long periods with limited visibility into what they remember, how long they retain it, and under which policy classifications.
Long-horizon traceability gaps
When something goes wrong, it is difficult to reconstruct why an AI-driven workflow behaved a certain way over weeks or months of evolving scopes and changing policies.
Static, human-centric controls
Existing RBAC and GRC frameworks assume relatively static roles and rely on humans as the primary enforcers — they provide no continuous runtime mechanism to adjust agent privileges.
Inside the paper
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