Microsoft Sentinel in different environments: cloud, on-premises, and hybrid

In today's IT environments, security events do not occur in one place: they occur simultaneously in cloud services, corporate endpoints, network devices, and business applications. This is why the role of a SIEM (security information and event management) system is so crucial: it provides a common "collection point" for events, helps identify correlations, and enables incident-level visibility based on alerts. There are several SIEM approaches on the market (cloud-based and on-premises), but the goal is the same in all cases: transparent, traceable, and automatable security operations—with reduced manual workload.

Microsoft Sentinel is already "turned on" at many organizations, yet the value it provides varies greatly. In most cases, this is not due to Sentinel's capabilities, but rather to the starting point of the environment and the quality of the data: the situation is different in a Microsoft-centric cloud, in an on-premises-dominant infrastructure, and in a hybrid setup.

Sentinel itself is a cloud-based security information and event management (SIEM) system running on Azure, with automation capabilities. It collects events from multiple sources, searches for correlations, generates alerts, and then organizes the alerts into incidents. The crucial question is: what sources are receiving, how consistent are the identifiers, and how easy is it to build context from them?

1) Microsoft-centric cloud (or hybrid) environment

The starting point here is typically more favorable because many elements of the Microsoft ecosystem "speak the same language." Typical sources:

• Microsoft 365 (e.g., Exchange, SharePoint, Teams events)
• Entra ID (Azure AD) logins and access events
• Microsoft Defender alerts (endpoint, identity, cloud application)
• Azure activity logs and cloud operations

Thanks to shared identifiers (user, device, tenant) and ready-made integrations, the context often comes together more quickly. Even so, it can still happen that the "factory" detection rules generate too many alerts—but it is usually easier to identify where the noise is coming from.

2) On-prem dominant environment

In an on-prem environment, Sentinel still has value, but the "work" is preceded by tasks of a different nature. Typical sources:

• Windows event logs (servers, workstations)
• local directory and authentication events
• network devices (firewall, VPN, proxy)
• application logs and infrastructure events

The challenge is usually that telemetry integration and standardization require more preparation: different formats, incomplete fields, time synchronization issues, and inconsistent identifiers. All of these factors impair correlation and thus the ability to quickly compile an incident-level picture from a signal.

3) Hybrid environment

In a hybrid setup, signals come from both the cloud and local systems. The greatest value (and also the greatest challenge) here is the correlation of events: bringing together the user–device–IP–application picture for the same case. A typical difficulty is that the same process is "visible" in several places (cloud access, endpoint signaling, VPN event, firewall log), and without consistent classification, the signals remain fragmented.

4) Non-factory (non-Microsoft) data sources

Sentinel can be built on more than just Microsoft sources. Third-party tools and SaaS systems can also be connected (for example, based on syslog/CEF or API connections). In practice, this means three things:

1. Connection and data collection: the channel must be selected (syslog/CEF, API, custom log format).
2. Interpretation and standardization of event fields (normalization): the same information from multiple systems can only be "put together" if the identifier fields and names are consistent.
3. Tuning detection rules to the environment: this is the only way to ensure that correlation and alert logic are relevant (exceptions, organizational characteristics, internal systems).

5) Why is a "connected Sentinel" not enough?

Sentinel provides data and alerts, but it does not guarantee that the company will be able to make quick and consistent decisions on its own. The following are generally required for stable operation:

• continuous detection rule fine-tuning (to reduce noise)
• consistent triage (pre-screening) and transfer order
• data source monitoring (detection of dropouts, fluctuations)
• automation scenarios where there are repetitive steps (tagging, notification, basic enrichment)

In the next article, we will look at how Sentinel's alerts are turned into decision-support incident images: what steps are involved in triage (pre-screening), what "evidence package" is required for transfer, and where automation can reduce the human workload.