GRACE™GRACE™ AI ControlCONTROL BEFORE CONSEQUENCE

Use Cases

Apply the same two authorization boundaries to different consequential AI workflows.

The use case changes. The control pattern stays consistent: authorize the information before AI reasoning, authorize the proposed action before execution, and preserve linked evidence for the transaction.

Illustrative scenarios

Financial services.
AI platforms / OEM.
Bank Secrecy Act / Anti-Money Laundering.
Healthcare administration.

These examples illustrate the GRACE architecture pattern across different workflows.

Financial services

Regulated lending

Information authorization: permit only task-defined borrower and transaction fields.

Action authorization: evaluate proposed underwriting, workflow, or delegated-authority actions before downstream execution.

Evidence: Context PAP, Action PAP, and Chain of Confidence for the controlled transaction.

Financial crime operations

Bank Secrecy Act / Anti-Money Laundering (BSA/AML)

Information authorization: govern which case, customer, transaction, and investigative fields are available for the declared task.

Action authorization: evaluate proposed case-routing or workflow actions before execution.

Evidence: preserve authorization decisions for review alongside the institution's existing BSA/AML process.

All scenarios on this page are illustrative. GRACE policy definitions and workflow boundaries are configured to the institution's own requirements and systems.

A practical starting point

Choose the workflow where accountability matters most.

Start where an AI agent can receive consequential information, propose a consequential action, or both. The evaluation can then make the two authorization boundaries visible before any decision to scale.

Bring the actual workflow.

Define the task, information fields, proposed action, delegated authority, human-review requirements, and the evidence the organization wants to inspect.

Evaluate One Workflow