CONTROL BEFORE CONSEQUENCE
AI agents are moving from assistance to action. Accountability still belongs to the institution.
GRACE helps organizations control what an AI agent is allowed to receive and what it is allowed to do—while creating signed, tamper-evident records of each authorization decision.
Why now: NIST describes AI agents as systems capable of autonomous actions and highlights identity, authorization, and secure interaction with data, tools, and applications. NIST AI Agent Standards Initiative · NIST agent identity and authorization work.
THE BUYER PROBLEM
The agent can receive information.
The agent can propose actions.
The organization remains accountable.
GRACE creates explicit authorization points around those two moments.
What can go wrong
Consequential AI creates risk at the information boundary and the action boundary.
Illustrative workflow risks that show where authorization boundaries matter.
More information reaches the agent than the task requires.
Example: sensitive health, financial, or identity data is included even though the declared task does not require it.
An AI-proposed action exceeds delegated authority.
Example: a workflow proposes a decision, lock, update, payment, or system-of-record change outside the user's approved authority.
A consequential action reaches execution before required review occurs.
Example: an institution requires human approval, but the workflow lacks a clear pre-execution authorization boundary.
Business value
Make consequential AI easier to govern, evaluate, and explain.
Safer deployment
Apply institution-defined rules before information reaches the agent and before proposed actions reach execution.
Stronger evidence
Create signed authorization records that show what information and action were approved for the transaction.
Earlier exception visibility
Surface ALLOW, MODIFY, and ABSTAIN decisions before a proposed action reaches downstream execution.
More disciplined evaluation
Use Shadow mode to observe and record decisions before introducing enforcement into live operations.
How GRACE fits
GRACE complements the controls you already have.
Identity, model, governance, and platform controls remain important. GRACE adds an independent authorization and evidence layer at the information and action boundaries of an agent workflow.
| Control category | Primary role |
|---|---|
| Model guardrails | Constrain model behavior, prompts, outputs, or content according to the model/application design. |
| Identity and Access Management (IAM) | Identify actors and govern access to systems, resources, and permissions. |
| Governance, Risk, and Compliance (GRC) | Manage policies, controls, oversight, risk processes, and evidence across the organization. |
| Platform-native controls | Apply controls available inside a specific platform or workflow environment. |
| GRACE™ AI Control | Evaluate information before AI reasoning and proposed actions before execution, then link the two authorization records for the transaction. |
Reference context: NIST AI Risk Management Framework and NIST software and AI agent identity/authorization work The comparison describes complementary control roles for architectural context.
GRACE in plain English
Control the information. Control the action. Prove the connection.
Information authorization: decide what the agent is allowed to receive for the declared task.
Action authorization: decide what the agent is allowed to do before execution.
Chain of Confidence: verify that the information authorization and action authorization belong to the same controlled transaction.
Then introduce the GRACE mechanism.
Context Path, Action Path, Policy Action Packets (PAPs), ALLOW / MODIFY / ABSTAIN, Shadow mode, and the Chain of Confidence are defined in one place on the How It Works page.
Low-friction next step
Start with one AI workflow before scaling.
Choose a bounded workflow, define the two authorization boundaries, run the evaluation in Shadow mode, review the resulting decisions and records, and decide what should move forward.
Observe and record decisions without affecting live operations.
Shadow mode lets the organization inspect policy decisions and authorization evidence before introducing enforcement.