Security Information and Event Management (SIEM) systems play a critical role in modern cybersecurity operations. They centralize logs, monitor environments, and generate alerts that help security teams detect threats. However, one of the biggest challenges with SIEMs is the high rate of false positives—benign events that are mistakenly flagged as malicious. These false positives drain time and resources, overwhelm analysts, and can lead to alert fatigue. To ensure SIEMs are an asset rather than a burden, effective tuning is essential. This blog explores how organizations can reduce false positives using Elastic Security, guided by the MITRE ATT&CK Framework.
Understanding the Problem with False Positives –
False positives are a natural byproduct of broad detection logic. Most SIEMs come with prebuilt rules that aim to cast a wide net. While this ensures coverage, it also leads to excessive alerting. Security teams often find themselves sifting through hundreds of alerts daily, many of which are triggered by normal user behavior or authorized activities. This volume of noise makes it easy to miss genuine threats and can significantly slow down incident response. Therefore, tuning the SIEM to reduce these false alarms without compromising visibility is crucial.
Elastic Security: A Powerful and Open SIEM –
Elastic Security, built on the Elastic Stack, offers an open and flexible SIEM solution. It uses Elasticsearch for fast data searches, Kibana for visualization, and Elastic Agents or Beats for data ingestion. One of its strongest features is the ability to customize detection rules extensively. Security analysts can modify thresholds, create rule exceptions, suppress repetitive alerts, and align detection strategies with their unique environment. Elastic Security also supports mapping each rule to the MITRE ATT&CK framework, helping teams to understand the adversary behaviors they are detecting.
Using the MITRE ATT&CK Framework for Contextual Tuning –
The MITRE ATT&CK Framework is a widely adopted reference that categorizes cyber adversary tactics and techniques based on real-world observations. By leveraging this framework, security teams can better understand what each detection rule is looking for. For example, a rule tied to the ATT&CK technique T1059.001 (PowerShell) indicates a focus on potentially malicious scripting behavior. With this mapping, analysts can evaluate whether the rule applies to their environment and whether any adjustments are needed. This approach promotes more strategic and threat-informed tuning, rather than reactive alert suppression.
Best Practices for Tuning with Elastic Security and ATT&CK –
The first step in tuning is to establish a baseline of what normal behavior looks like in your environment. This includes analyzing log data, reviewing regular user activities, and identifying legitimate applications and processes. Once this baseline is set, analysts can begin modifying rules to better suit their organization.
A practical tuning method involves prioritizing detection rules based on business relevance and threat severity. Rules that detect high-impact behaviors such as credential dumping (T1003) or lateral movement (T1021) should be kept active and tuned for precision. On the other hand, rules that constantly trigger due to routine IT activity can be refined or have exceptions added.
Elastic Security allows for rule exceptions, such as excluding known IP addresses, users, or processes from triggering alerts. Thresholds can also be applied—for example, alert only when a command is run 10 times in a short span. Additionally, enriching alerts with threat intelligence or endpoint context helps analysts make faster and more informed decisions during triage.
A Real-World Example of Rule Tuning –
Consider a rule that detects suspicious PowerShell commands. This rule might generate frequent alerts if your IT team regularly uses automation scripts. Instead of disabling the rule, you can fine-tune it. Start by reviewing the script content and excluding known good commands. Next, apply conditions to only trigger alerts if certain flags like -EncodedCommand or -NoProfile are present—indicators often used by attackers. Finally, add rule exceptions for the specific hosts or users that routinely run the scripts. This results in a focused alerting system that catches real threats while ignoring routine activity.
Conclusion –
Tuning a SIEM like Elastic Security is not a one-time task—it is an ongoing process that evolves with your environment and threat landscape. By using the MITRE ATT&CK Framework as a guide, organizations can make smarter decisions about which detection rules to enable, modify, or suppress. The result is a more accurate, context-aware SIEM that empowers analysts to focus on real threats instead of chasing false alarms. As cyber threats become more sophisticated, proactive SIEM tuning becomes not just beneficial, but essential for maintaining effective security operations.