How should security teams use Azure AD and Sentinel data to spot risky identity relationships early?

Why Azure AD and Sentinel relationships matter for identity risk detection

Security teams use Azure AD and Sentinel together because access risk is rarely visible in a single event. A user may look low risk in a table, yet still sit one group membership away from a privileged role, an application permission, or a stale account with broad reach. That relationship layer is where hidden exposure appears first, before it becomes misuse, lateral movement, or an audit surprise.

Microsoft Sentinel helps analysts correlate identity telemetry at speed, but the value comes from joining sign-in patterns, directory objects, and role context into one view of how access is actually shaped. This matters most when teams need to distinguish normal delegation from privilege creep, dormant entitlements, or access paths created by app consent and group nesting. The result is earlier detection of relationships that are technically valid but operationally unsafe.

In practice, many teams only notice those paths after an incident review shows that the access was always there, just spread across systems that were never examined together.

How to use Azure AD and Sentinel in practice

The most useful approach is to treat Azure AD as the source of relationship truth and Sentinel as the detection layer that turns those relationships into investigations. Start by collecting the entities that matter for access exposure: users, groups, service principals, applications, privileged roles, conditional access context, and sign-in activity. Then correlate them so you can see not just who logged in, but what each identity can reach through inheritance, assignment, or consent.

A graph view is valuable because it exposes indirect relationships that tables hide. For example, a user may not hold a privileged role directly, but may belong to a group that grants role eligibility, or may have approved an application that can act across multiple resources. Sentinel queries can then filter for high-risk combinations such as recent privilege assignment, unusual group expansion, legacy authentication, repeated failures followed by success, or identities that have not been reviewed but still retain access. The point is not simply to detect alerts; it is to surface relationship patterns that deserve validation before they become standing risk.

For NHI-heavy environments, this is especially important because many risky relationships are not human-driven at all. The same graph logic can reveal over-permissioned service principals, long-lived app permissions, or cross-tenant relationships that were created for convenience and never revisited. NHIMG’s Ultimate Guide to NHIs is useful background when your Azure AD inventory includes service accounts, tokens, and application identities that behave like persistent access paths.

A practical workflow is to pivot from an identity of interest to its direct and inherited memberships, then to the applications and roles those memberships unlock, and finally to the sign-in telemetry that confirms whether the access is being used as expected. When that sequence is automated in Sentinel, analysts can prioritise the few relationships that are both privileged and active, rather than reviewing every relationship as if it carried equal exposure. Microsoft’s NIST Cybersecurity Framework 2.0 remains relevant here because the control problem is really about identifying, governing, and monitoring access paths continuously.

These controls tend to break down when identity data is fragmented across tenants, legacy authentication is still allowed, or group and app ownership is so inconsistent that the graph reflects stale administration rather than live authority.

Common relationship patterns that create hidden exposure

Tighter identity monitoring often increases review volume, so teams have to balance early detection against analyst fatigue. The relationships that matter most are usually the ones that combine privilege with weak visibility, not the ones that simply look unusual in isolation.

One common pattern is indirect privilege through nested groups or inherited role eligibility. Another is application consent that quietly grants broad access without any human logging in interactively. A third is stale access that survives role changes, project moves, or offboarding because the identity still belongs to a group that was never revalidated. In Azure AD and Sentinel, those patterns become visible only when sign-in telemetry is joined to entitlement structure and change history, rather than viewed as separate data sets.

• Prioritise identities that combine privilege, recent change, and active use.
• Review group nesting and app permissions together, not as separate hygiene tasks.
• Escalate any identity that can reach sensitive resources through indirect assignment and has no clear business owner.
• Use Sentinel to confirm whether the access path is actually exercised before deciding it is harmless.

Current guidance suggests treating relationship drift as a standing detection problem, not a periodic cleanup exercise. The highest-value alerts are usually not “new user” or “failed login” on their own, but the identity that suddenly gains a path to a protected resource and then starts using it. For teams that manage service principals or automation accounts, NHIMG’s Top 10 NHI Issues offers a useful complement because many of the same relationship failures apply to machine identities as to people.

Practitioner Guidance

What to prioritise: Start with identities that are both reachable and actionable: privileged users, group owners, application owners, and any account with inherited access into sensitive workloads. That shortlist usually produces better early warning than broad hunts across all identities.

What to verify: Confirm that every high-risk relationship has a current business owner, a reason for existence, and evidence that the access path is still required. If the relationship exists only because of historical delegation, treat it as exposure until proven otherwise.

Decision rule: If Sentinel shows active use of an indirect path to sensitive access, do not wait for a second signal before reviewing it. Validate the entitlement chain first, then decide whether the access is justified, time-bound, or should be removed.

Practitioner takeaway: The best Azure AD and Sentinel programmes do not try to watch every identity equally; they focus on the small set of relationships where privilege, inheritance, and real activity intersect.

Related resources from NHI Mgmt Group

• How should security teams determine access privileges in Azure AD before assigning users to roles and groups?
• How should identity security teams use access analytics when peer groups go stale as organisations reorganise and users change roles?
• How should security teams run Azure AD access reviews to reduce excessive permissions and dormant account risk?
• How should security teams use data minimisation to reduce storage waste without weakening governance?