When bringing lots of security information into Microsoft Sentinel, what are two smart ways to control how much it costs, while still making sure you catch all important threats?

When ingesting large volumes of security information into Microsoft Sentinel, two smart ways to control costs while ensuring important threats are still caught involve intelligent data filtering at ingestion and optimizing data retention policies. The primary cost driver in Microsoft Sentinel is the volume of data ingested into its underlying Log Analytics workspace. By managing this volume and how long data is stored in different tiers, organizations can significantly reduce expenses.

Firstly, implementing granular data filtering and prioritization at the point of ingestion significantly reduces costs without sacrificing critical threat detection. This means proactively deciding which specific logs and events are most relevant for security monitoring and ingesting only those, rather than all available data. Many Microsoft Sentinel data connectors, which are mechanisms to bring data into Sentinel, offer built-in filtering capabilities. For instance, when connecting Azure Activity Logs, you can choose to ingest only specific categories like 'Security' or 'Administrative' events, excluding high-volume, low-security-value informational events. Similarly, for logs collected from virtual machines using agents like the Azure Monitor Agent (AMA), Data Collection Rules (DCRs) allow you to specify exact Windows Event IDs or Linux syslog facilities and levels to collect. By filtering out noise and redundant data, you reduce the overall data volume stored in Sentinel, directly lowering ingestion costs. This approach ensures that high-fidelity security events, which are crucial for detecting important threats (like suspicious logins, malware activity, or unauthorized access attempts), are still ingested and available for real-time analysis, while less critical or verbose logs are excluded. For example, instead of collecting all successful authentication events which can be numerous, you might prioritize ingesting all failed authentication attempts and specific critical system events, as these are often direct indicators of potential security incidents.

Secondly, optimizing data retention policies by leveraging tiered storage is a cost-effective strategy. Not all security data requires immediate, high-performance query access for the same duration. Microsoft Sentinel, through Log Analytics, allows setting different retention periods and leveraging various storage tiers. The default interactive retention period, typically 30 or 90 days, keeps data readily available for real-time threat detection, interactive hunting, and incident response, which incurs the standard Log Analytics cost. For data that needs to be kept longer for compliance, forensic investigations, or long-term historical analysis but doesn't require immediate query performance, organizations can configure longer retention periods that automatically move older data into a cheaper 'archive' tier. This archival process significantly reduces storage costs for historical data. While querying archived data requires an additional 'restore' operation to bring it back to an interactive state for a temporary period, this cost is generally much lower than storing all data in the interactive tier for extended periods. This strategy ensures that all necessary data is retained to meet compliance requirements and support deep forensic analysis when needed, thereby still allowing the catching of threats even retrospectively, but it segregates data by its immediate operational value to optimize storage expenses. For instance, security events might be kept in interactive storage for 90 days to support active security operations, while compliance requirements dictate retaining them for seven years in cheaper archive storage.