Keep AI-driven results within the authority the organization has established.
New models, agents, and tools can change quickly. The organization still decides which results they may create on its behalf. Authority Control applies that authority before a consequential result takes effect.
An AI agent can use approved tools and valid access to create a result outside organizational authority.
AI accelerates Scale, Scope, and Pace.
Organizational authority remains the reference for the consequential result.
Pace
New models, agents, tools, and routes arrive continuously, and each one can reach a consequential result a different way.
A new model, agent, or route does not expand organizational authority.
Scope
One agent workflow can cross systems, vendors, identities, and data domains, while each control governs its own part.
A result may span several systems and still meet one authority question. Each execution path requires its own coverage.
Scale
One person can direct many agents. That additional capacity does not multiply the person’s delegated authority.
More agents multiply the activity, not the authority behind it.
Different paths can lead to the same result.
Illustrative scenario: an agent assigned to analyze customer data proposes releasing protected data to an external recipient. An export API, collaboration workspace, or another agent could offer different routes to that result. Each route needs its own coverage assessment.
The teal line is the commitment boundary, the point where a proposed result would take effect.
Authority Control applies the organization’s authority to that result and returns Permit, Defer, or Block before it takes effect. Each determination leaves a durable record.
An authorized objective does not make every consequential result authorized.
OpenAI’s September 2026 model-misalignment reporting framework includes individual training and evaluation cases in which models used exposed credentials, uploaded files to public services, or created unsanctioned communication paths while pursuing assigned objectives.
While pursuing an authorized task, a system may still propose a consequential result that falls outside organizational authority. Technical capability and task assignment do not by themselves establish authority for that result.
More capable AI. Authority that remains organization-defined.
New AI capability does not itself expand what the organization has authorized. Authority Control applies the organization’s current authority before consequential results take effect.
Where may this model and version be deployed and used?
What consequential results may this agent create on the organization’s behalf?
Which consequential results require additional authority or review as AI use expands?
Model Authority →Frontier Agentic Systems →
Five Eyes cyber leaders warned that AI is increasing the speed, scale, and sophistication of cyber threats and shortening the interval between vulnerability discovery and exploitation. Read the joint statement →
AI security and Authority Control work at different parts of the problem.
helps the organization understand model capability and behavior.
govern who or what can reach systems and tools.
manage operating conditions, manipulation, and technical risk, including prompt injection and unsafe tool use.
evaluates whether the result is within organizational authority before it takes effect.
Together, these controls address technical risk and organizational authority as AI capability grows.
Start with one AI workflow that can create a material result.
Choose an AI workflow that can move money, release data, change production, or create an external obligation. Map the result and its applicable authority, then observe proposed results before choosing enforcement.
New technology changes how work gets done. The organization still decides what it may commit to.
When the result concerns data.
Explore authority for consequential data use, movement, release, and deletion.
Explore Data ↗