Automated Intake and Evidence Evaluator: governing AI with AI

Using AI to govern AI sounds contradictory. However, when done correctly, AI agents can read documents an organization already has, propose answers, cite sources, and wait for a human. Optro’s two new agentic features, Automated Intake and Evidence Evaluator, do just that. In the first, the agent finds answers already in the documents and drafts the application record. In the second, the agent finds existing evidence in your documents and proposes which controls it satisfies.

These agents aren't making governance decisions. They find information that the organization already has and propose where it belongs. A human still makes the decisions that matter, and the agent's work leaves a better paper trail than the manual process it replaces.

The problem both features solve

AI intake today is a transcription exercise. The answers live in vendor system cards, DPAs, security reviews, specs, and architecture docs. Someone reads those and retypes them into a form. Evidence mapping today means taking a pool of documents — from a vendor or from internal teams — and matching them to individual controls, for every application.

AI governance makes this process worse than other compliance work because controls and documentation are often bespoke to each AI system and use case. There's no generic answer to reuse. It’s one of the reasons why 78% of senior business leaders lack full confidence that their organization could pass an independent AI governance audit within 90 days.

This creates a sluggish system that blocks everything downstream. Nothing can be risk-assessed until the record exists, so intake is a bottleneck for every other governance activity. The record ends up hard to defend. The person filling in the form is usually an application owner, not a governance specialist. The governance lead inherits records they didn't create, with no visibility into where the answers came from.

These issues are exactly what Automated Intake and Evidence Evaluator are meant to solve. Here’s how each feature approaches the problem.

Automated Intake: the record drafts itself

Automated Intake starts when an application owner registers an AI application with a short set of questions and uploads the supporting documentation they already hold. Then:

• An agent reads those documents and proposes answers to the intake fields.
• Each proposal carries a confidence level — High, Medium, or Low — and the source documents it drew from.
• The owner can accept, edit, or dismiss the proposal. Edit works field by field, so they can keep some answers, change others, and leave others blank.

That leaves the application owner to review what the agent found, correct anything it got wrong, and fill in the gaps, rather than starting with a blank form.

Evidence Evaluator: what your documents already cover

Evidence Evaluator picks up once documentation is available and uploaded to the AI Governance platform. Then:

• The documents are assessed against the controls for every framework in scope for that application.
• The Evaluator returns an overview of how the documents satisfy each control description, including a confidence score and a description of any notable gaps.
• The feature identifies specific files as valid or partially valid evidence.
• For accepted proposals, the file is attached as Document Evidence for that control, with the agent's reasoning carried across as its description.

Most tools tell you what they found. Telling you what's missing — and distinguishing a gap you have to close from a document you only need to strengthen — is the harder and more useful output. Most teams are further along than they can currently prove, and partial credit and gap analysis show them where they need to optimize.

“The Evaluator was so good at bringing in all that stuff…Then it talks about the Optro recommendations, specifically also the things that it does not find documentation on, because that was one that I missed the first time around,” said one client who tested Evidence Evaluator while it was in beta. “Your tool did the work that my team did manually over a period of a month, and that’s now been solved. I can already see and show the value the evidence has brought.”

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Agent-assisted, not agent-unchecked

That brings us back to the question at the beginning: can you really use AI to govern AI?

Using AI in governance only works if there are clear limits on what the agent can do. Automated Intake and Evidence Evaluator are built around that distinction. The agent does the research and proposes an answer. The human decides whether that answer is good enough to use.

In Automated Intake, the agent reads existing documentation and proposes responses to intake questions. The application owner can accept, change, or dismiss each one. Evidence Evaluator works similarly: it assesses how well the available evidence satisfies a control and flags gaps, but nothing moves forward until a person reviews it.

A proposal doesn't become part of the record until someone reviews it. Reviewers can see the source material and confidence level behind each answer, and Optro logs what happened afterward. Optro logs every run, acceptance, and dismissal in a central place, including line edits a reviewer makes to the agent's recommendation. For internal audit, that creates a traceable path from the source evidence to the agent’s recommendation and the reviewer’s final decision. An auditor can see what the agent relied on, what the human changed, and what the record ultimately accepted.

That log is why an AI-assisted record is more, not less, defensible than a hand-typed one. Human oversight isn't simply “a person somewhere in the process”; the system is designed so the human has enough context to exercise meaningful judgment. A manually completed intake form has no provenance at all. Nobody can tell you which document an answer came from, how carefully it was researched, or who decided it was good enough. With the agent-assisted version, that history is part of the record.

If agents handle routine work, the human's role may become less frequent but more consequential. As the beta client put it, “The humans in the loop have to be really, really knowledgeable and know what they’re doing, because by the time they need it…they really need it.”

Why it works better on a governed record

And why do these features work especially well at Optro? Because both Automated Intake and Evidence Evaluator reason over a governed record.

An AI application in Optro isn't just a standalone record. It already has an owner and risk score, is scoped to the relevant frameworks, and is mapped to the controls those frameworks require. That gives the agents context they wouldn't have if they were simply reading a folder of documents.

That’s the GRC Intelligence model in practice: the same trusted system of record that gives agents the context to reason also becomes a system of action that puts that context to work.

For example, Evidence Evaluator can determine whether a document is relevant to a particular control and recognize evidence that may already satisfy the requirement. That existing governance context narrows the agent's job and makes its recommendations easier for a human to evaluate.

“That’s the value right there,” the beta test client concluded. “‘Do you have a handle on your AI?’ Yes, I do. Absolutely.”

The final call

Using AI to help govern AI doesn't mean handing governance decisions over to an agent.

Automated Intake and Evidence Evaluator take on work that doesn't require that judgment in the first place: finding information, comparing documentation against requirements, surfacing gaps, and showing where an answer came from. A person still makes the final call.

That's the more useful role for agents in AI governance. Don't ask them to replace the people accountable for the program. Give those people better information, with less manual work required to get it.