Ladon
AI Governance for IT and Security Teams: A Practical Checklist
AI governance sounds like a committee and a 40-page document. For IT and security teams it comes down to something much more concrete: knowing which AI tools are used, deciding what data may go where, and enforcing that decision without slowing people down. This checklist walks through the steps in the order that tends to work.
1. Build an inventory of AI usage
You cannot govern what you cannot see. Start by discovering which AI services are being used, on which devices and how often. Include browser-based chat tools, coding assistants in editors, and API usage from scripts. Expect surprises — the long tail of lesser-known AI sites is usually where unmanaged risk sits.
2. Classify the data that matters
Do not try to protect "everything". Pick the data types whose exposure would genuinely hurt: personal identifiers, payment card data, credentials and secrets, source code and confidential business documents. Each one becomes something a policy can name.
3. Write policies that can be enforced
A policy people cannot follow, or tools cannot enforce, is a document, not a control. Express each rule as an action on a data type for an AI app:
- Allow — approved tool, low-risk use.
- Alert — let it through but record it and notify security.
- Redact — remove the sensitive value and forward the rest.
- Block — stop the request when no safe version exists.
Order rules by priority and let the first match decide, so exceptions (for example, a team that may use a specific coding assistant) sit above the general rules.
4. Deploy without asking users to do anything
Governance that depends on employees installing and configuring software will have gaps. Push the endpoint agent through the MDM you already run — Ladon ships a ready script for JumpCloud and a package that works with other MDM tools — and enrol the fleet with a single enrolment token. The agent is inert without the token, and an expiry and seat limit keep enrolment under control.
5. Alert the right people, in the right place
Findings that sit in a dashboard nobody opens do not reduce risk. Send new incidents to the channel your team already watches. Ladon opens an incident when a flagged request is detected and can post to a Slack webhook, with the detected data type, the AI app and the device involved.
6. Tell employees what is monitored and why
Transparency is both good practice and, in many jurisdictions, a legal requirement. Publish a clear notice describing what is inspected, what is recorded and who can see it. Review the wording with your legal counsel; Ladon's Compliance page includes templates for an employee notice and a data processing agreement that you can use as a starting point.
7. Review, tune and repeat
New AI tools appear constantly and policies drift. Schedule a recurring review of shadow AI discoveries, incident trends and false positives. Promote tools you decide to approve, tighten rules where incidents cluster, and retire rules nobody triggers.
Checklist summary
- Inventory AI usage across endpoints.
- Define the data types you must protect.
- Turn each into an allow / alert / redact / block rule.
- Roll out silently through MDM.
- Route incidents to Slack or your on-call channel.
- Publish an employee notice.
- Review monthly and adjust.
Governance done this way is not a brake on AI adoption. It is what lets a company say yes to AI with confidence.