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Security at RedactSure

Control what AI sees.
Keep people in charge.

Sensitive work needs protected inputs, explicit authority, and a record of what happened. Start with the boundary, then inspect the workflow.

01 / Least Exposure

Privacy before model access.

Render-layer tokenization replaces sensitive values with consistent tokens at the point where an agent reads the screen. The configured original values remain outside model context and resolve only where task policy allows.

Your protected workspaceJamie Rivera
Account 7492
Before model accessReplace protected values
What the AI receivesPERSON_001
ACCOUNT_001
Simplified tokenization example using fictional data.

The workflow keeps useful context while configured names, account details, and other protected values stay outside the model’s view.

Read about render-layer tokenization ↗

02 / Supervised Delegation

A named person stays responsible.

Your team reviews the applications, protected inputs, allowed actions, and approval points before work begins. Operators can watch, pause, help, or take over. Consequential decisions remain with a named person.

  1. Review the scopeSystems, data, and allowed actions.
  2. Supervise the runProgress, exceptions, and interventions.
  3. Approve the decisionA named person and a recorded action.
Read about Supervised Delegation ↗

03 / AI Control Record

Evidence travels with the work.

The task retains its plan, actions, approvals, interventions, failures, outputs, and artifacts as an accountable record. Review what the model received, what the agent did, and who approved the consequential steps.

Read about the AI Control Record ↗

Bring your hardest questions

Inspect the architecture with us.

A twenty-five-minute security review covers the threat model, token design, egress paths, and audit schema. Bring your security lead and a workflow that matters.

Book an AI Agent Security Review ↗Read the twenty-question checklist ↗Try the synthetic-data attack demo ↗