Three Steps to Reduce False Positives and Alert Fatigue in Your SIEM

siem false positive

Three Steps to Reduce False Positives and Alert Fatigue in Your SIEM

In the realm of cybersecurity, Security Information and Event Management (SIEM) systems are indispensable tools for monitoring and analyzing an organization’s security posture in real-time. However, one of the hurdles that security professionals often encounter is the prevalence of false positives which can overwhelm analysts and obscure genuine threats. This article explores strategies to minimize false positives in SIEM systems, using UTMStack as an illustrative example, while also delving into the modification of correlation rules to better hone accuracy.

1. Utilizing Tag Rules to Filter Noise

A pragmatic approach to reduce false positives is by employing tag rules which help in filtering out the noise and focusing on the relevant alerts. UTMStack, for example, offers false positive tag rules, which can be leveraged to tag and categorize alerts based on predefined or custom criteria. By fine-tuning these rules, analysts can significantly cut down the number of false positives, ensuring that only substantial alerts are escalated for review.

Steps to Implement Tag Rules:

1. Define Criteria: Establish the criteria for tagging based on historical data, known false positives, or other relevant parameters.
2. Configure Tag Rules: In UTMStack, navigate to the false positive tag rules section and create or modify existing rules as per the defined criteria.
3. Monitor & Refine: Continuously monitor the effectiveness of the tag rules and refine them to ensure optimum performance over time.

The following resource outlines the steps to define false positive tag rules in the UTMStack SIEM as an example:

2. Modifying Correlation Rules for Better Accuracy

Correlation rules are at the heart of a SIEM system’s ability to detect and alert on suspicious activities. However, overly broad or misconfigured correlation rules can trigger a barrage of false positives. Modifying these rules for better precision is essential. In UTMStack, for instance, correlation rules can be tailored to meet the specific needs of an organization.

Steps to Modify Correlation Rules:

1. Identify Ineffective Rules: Start by identifying the rules that are generating a high number of false positives.
2. Adjust Thresholds & Parameters: Modify the thresholds, conditions, or other parameters to make the rules more stringent or relaxed based on the requirement.
3. Test Modifications: Before deploying the modified rules, test them in a controlled environment to assess their effectiveness.
4. Deploy & Monitor: Once satisfied with the modifications, deploy the updated rules, and continue monitoring for any necessary further tweaks.

Modifying correlation rules can be complex. The following resource provides step by step instructions to personalizing and creating your own rules using YML files.

siem correlation rule

3. Use AI powered by RAG and Statistical Analysis

While a simple AI integration won’t effectively differentiate a false positive from a real incident, retrieval augmented generation (RAG) and statistical analysis can provide enough context information to a Large Language Model (LLM) such as GPT4 or Llama to be effective at facilitating SOC analysts job.

UTMStack native Open Source AI handles the identification and investigation of incidents and the handling of false positives. This AI is a 24/7 Security Analyst that helps cut through the noise of event-heavy systems.

While AI has not been integrated with all SIEM systems in the market yet, this is definitely a trend that more vendors are adopting. SIEM users should also be aware of the risks of using pure AI without RAG or Statistical analysis. Evaluating vendors that use AI in a proper manner is essential for success.

Vendor Agnostic Approach

While UTMStack serves as a useful example, the principles and steps outlined here are vendor agnostic. By understanding and applying these techniques, organizations can significantly improve their SIEM systems’ accuracy, regardless of the platform in use.

By embracing a systematic and iterative approach towards configuring tag and correlation rules, organizations can markedly reduce the number of false positives in their SIEM systems, enabling a more focused and effective security monitoring strategy.

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