The Missing Layer in Enterprise AI: Decision Authority
AI Systems Have a Structural Authorization Gap
Enterprise AI adoption is accelerating. Models are embedded into workflows. Applications call LLMs in production. AI drafts legal text, scores risk, assists decisions.
We have: • Model validation • Guardrails and filters • Monitoring and logs • Compliance policies
But most organizations cannot answer one critical question:
Who authorized this AI to act?
Not who trained the model. Not who deployed the app.
Who authorized this specific AI action — under what policy, with what oversight, and with what evidence?
In most systems, the model simply runs.
⸻
This Is Not an AI Safety Problem
The governance conversation focuses on: • Safety (“Is it harmful?”) • Alignment (“Does it behave?”) • Guardrails (“Can we filter outputs?”) • Monitoring (“What happened?”)
All important.
But they address model behavior — not decision authorization.
There is a difference between filtering outputs and authorizing actions.
Most AI systems filter. Very few authorize.
⸻
The Authorization Gap
In regulated industries, decisions follow a chain: • A policy exists • The policy is approved • An action is permitted under that policy • Evidence is retained • Liability is defined
A trade cannot execute without compliance approval. A loan cannot be issued without underwriting authority.
Yet AI systems can: • Draft legal analysis • Score credit • Trigger workflows • Access data • Initiate actions
Often without a per-decision authorization record tied to a specific policy version.
Organizations may have governance documents. They rarely have an authorization trace for each AI action.
This is the authorization gap.
⸻
The Gap Widens with Agentic AI
As AI becomes operational, not just generative, the risk changes.
When AI systems: • Call APIs • Modify files • Trigger workflows • Execute commands
They are performing actions.
An output can be filtered. An executed action cannot always be reversed.
Once AI enters execution paths, authorization must precede action.
Otherwise, the organization is running autonomous execution without formal authority.
⸻
A Missing Infrastructure Layer: Decision Authority
This gap points to a distinct infrastructure layer:
Decision Authority.
Decision Authority is the control layer that: • Determines whether an AI action is permitted under policy • Evaluates contextual risk before execution • Enforces human review when required • Produces a traceable decision record • Links actions to versioned policy • Preserves evidence for audit
It does not make the model smarter. It does not filter outputs. It establishes authority.
⸻
Infrastructure Precedent
Every major shift required a control layer: • Electricity required circuit breakers • The internet required firewalls • Cloud computing required IAM • Financial systems required transaction authorization
AI systems require Decision Authority.
The model is the engine. Decision Authority is the control plane.
Without it, AI operates on implicit trust in autonomous execution.
⸻
The Question That Matters
In regulated environments, the audit question will not be:
“Is your model safe?”
It will be:
“Who authorized this AI decision, under what policy, and where is the evidence?”
If an organization cannot produce: • A policy reference • A versioned rule • A decision trace • A defined authority
Then governance exists only on paper — not at execution.
As AI systems become operational actors, this gap becomes structural.
The model does not decide alone.
The organization does or should.
Decision Authority is the missing layer.