The reading your evaluators ask for

How the controls on the market compare, by where they sit and what they can see. Acceptable-use policy, network blocking, API gateways, closed assistants, browser plugins, DLP and CASB, and provider-side controls, and the blind spot each one leaves, including shadow AI.

A component-by-component walk through the platform: the path a request takes, what crosses each boundary, what Verillian never receives, and the cryptographic properties behind encryption, tamper-evidence, policy integrity, and licensing.

How the platform governs AI usage without taking custody of your data: where your data lives, how content is protected with a key only you hold, and the answers security teams ask first.

Enable AI without becoming the cautionary tale. Coverage that includes the tools you never approved, and a record you can stand behind years later.

A control your teams already know how to buy: per seat, annual, deployed on your own infrastructure. No vendor data-custody to diligence, no integration project to scope.

A practical checklist for security and procurement teams: the questions that separate a control that reports cleanly from one that holds up under a regulator, an auditor, or a court.

What a pilot looks like: scope, timeline, and what your team walks away able to prove, on your own endpoints, against your own policies.

Almost every system can show you a log. A log you, an insider, or an attacker could quietly edit is not evidence. What it takes to make a record hold up as proof.

A control can only catch what it can see. Most controls sit downstream of the device, so traffic that never takes a sanctioned path stays invisible. Where a control sits decides what it can catch.

An AI governance control can do three jobs. Most tools do the first two competently. Regulated work is decided by the third, and almost no one builds for it first.

Your staff adopted AI faster than your institution could govern it. The exposure is real and mostly invisible, because the tools that create the risk are the ones no one approved.

The HIPAA exposure of ungoverned AI, and the controls that keep PHI inside the boundary with a tamper-evident record.

Where criminal justice information leaks when analysts adopt AI, and how Verillian governs it with logging built to CJIS 6.0.

The risk of ungoverned AI across an agency, and the evidence an inspector general can verify.

ITAR and CUI exposure when AI tools touch controlled data, and on-device enforcement that keeps it inside your boundary.

Model-risk and data exposure under SR 11-7, SOX, and GLBA, and tamper-evident evidence across every AI tool in use.

FERPA and COPPA exposure when student data meets AI, and the controls that keep it inside district and university systems.