Group mining: do not start your ABAC model from zero

The biggest hurdle with attribute-based access (ABAC) is not the technology, it is the start. Your tenant is full of groups that were made by hand over the years, and no one remembers exactly which group belongs to which job title or department. Group mining solves that starting problem: it reads your existing patterns and proposes how to base them on attributes.

TL;DR

• The hard part of ABAC is not the rules, it is knowing where to start in a grown tenant.
• Group mining analyzes your existing groups, memberships, and requests and proposes links.
• Every recommendation comes with numbers: how many users it affects and what percentage already shares the attribute.
• Recommendations fall into four categories: Suggestions, Cleanup, Drift, and Quality.
• You apply nothing blindly: you test every recommendation on a small group first before rolling it out wider.

The problem: a grown tenant

Almost no Entra ID tenant is neatly designed. Groups appeared when someone needed them, got names that made sense at the time, and their membership was maintained by hand. The result is a layer of access that no one fully oversees anymore.

When you then want to move to rules instead of manual work, you hit the question: which group actually belongs to which attribute? Figuring that out by hand takes months, and you are never sure you have not missed something. That is where group mining starts.

What group mining does

Group mining analyzes your existing groups, memberships, and requests and proposes how to base them better on attributes. Instead of puzzling out which group belongs to which job title yourself, you get concrete proposals with reasoning.

Every recommendation comes with the numbers: how many users it affects, what percentage of the attribute already has the group, and the expected impact if you apply it. That way you see at once whether a proposal is a real pattern or coincidental overlap.

The four categories

ServiceChanger groups the recommendations into four categories.

Category	What it signals
Suggestions	Link a group to an attribute so membership runs automatically
Cleanup	Empty or unused groups, duplicate links, loose memberships
Drift	Reality has diverged from your rules (user in a group without the attribute)
Quality	Quality signals about your attribute and group model

Suggestions are the engine: a group without a link whose members mostly share one attribute, a group often requested through the Self-Service Portal by people with the same attribute, or a group repeatedly granted as temporary access to the same kind of people. All candidates to link permanently.

Cleanup, Drift, and Quality keep your model healthy afterward, so it does not get cluttered again.

How a recommendation is formed

Group mining looks at the overlap between group membership and attributes, and applies thresholds before anything appears as a recommendation. A link proposal only surfaces with enough users and enough coverage, so you do not get noise from coincidental overlap.

Consolidation proposals, where one broader attribute already covers almost all members of a group, explicitly show who would gain access and who would lose it. That way you never link too broadly by accident.

Example recommendation (Suggestion):
  Group:        Finance-Apps
  Proposal:     link to user.department = "Finance"
  Affects:      48 users
  Coverage:     44 of 48 members already have this attribute (92%)
  Gains access: 3 users with the attribute not yet in the group
  Loses:        0

Applying a recommendation

Every recommendation has three possible actions:

• Apply. ServiceChanger makes the proposed link or change.
• Dismiss. You hide the recommendation. It does not return unless the situation changes.
• Ignore. You leave it for later.

An applied recommendation you test, like any other rule, on a small test group first before rolling it out wider. That keeps the move to rules controllable: you decide per proposal, with the numbers next to it.

How ServiceChanger fits in

Group mining is how ServiceChanger gets you from a grown tenant to an attribute-driven model without starting from zero. It reads what you already have, proposes links, and then lets the ABAC engine maintain it automatically.

ServiceChanger works on the attributes already in your directory, within Entra ID and on-prem AD. Group mining suggests the links; the ABAC engine keeps the membership correct afterward. The License module tracks license usage based on Entra sign-in activity; the assignment of licenses itself stays with Microsoft.

FAQ

Does group mining change things itself? No, not on its own. It makes proposals with reasoning. You decide whether to apply, dismiss, or ignore.

How do I prevent a proposal from linking too broadly? Consolidation proposals explicitly show who gains and loses access, and you test every applied rule on a small group first.

Does this work on on-prem Active Directory too? Yes. ServiceChanger works on both Entra ID and on-prem AD, so a hybrid environment falls under one model.

What if my group names make no sense? That does not matter. Group mining looks at membership and attributes, not the name. Bad names do not disturb the analysis.

Further reading

• What is ABAC in Entra ID for the rules group mining proposes.
• Automating Entra ID groups for the step from manual to rule-driven.
• Dynamic groups vs static in Entra ID for the difference with the built-in dynamic groups.

Next step

Want to know which groups in your tenant are candidates to link to an attribute? ServiceChanger scans your environment and makes the proposals with the numbers attached. Book a demo or read the ABAC docs.