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Reducing false positives in Microsoft Sentinel

In the ever-evolving landscape of cyber security, false positives in security analytics can be a thorn in the side of businesses and IT professionals alike. Microsoft Sentinel, a cutting-edge cloud-native SIEM, promises to revolutionise the way we approach security, but even the most advanced systems can fall victim to the dreaded false positive.

That’s why we’ve compiled expert strategies to help you tackle these pesky security alerts and enhance your organisation’s security analytics. Get ready to supercharge your security analytics and wave goodbye to those time-consuming and resource-draining false alarms.

What is Microsoft Sentinel?

Microsoft Sentinel is a cloud-native Security Information and Event Management (SIEM) system that provides a comprehensive, intelligent, and scalable solution for security teams to manage and analyse security events. It enables organisations to collect, correlate, and analyse security data from various sources, helping security teams detect cyber threats, investigate incidents, and respond to security breaches more efficiently.

One of the key strengths of Microsoft Sentinel lies in its use of advanced analytics and artificial intelligence (AI) capabilities. These technologies allow the platform to automatically detect patterns and anomalies in large volumes of security data, enabling security teams to identify potential threats quickly and accurately.

Furthermore, Microsoft Sentinel’s built-in automation and orchestration features allow organisations to automate routine tasks and streamline the incident response process, reducing the workload on security teams and enabling them to focus on more critical issues.

What are false positives?

In the context of security analytics, false positives refer to security alerts that are triggered by benign or non-malicious activity, rather than by genuine cyber threats or vulnerabilities. These false alarms can be caused by a variety of factors, such as misconfigured security rules, overly broad detection criteria, or simply human error.

Regardless of the cause, false positives can have a significant impact on the effectiveness and efficiency of your organisation’s security operations, as they divert valuable time and resources away from investigating and responding to genuine security incidents.

False positives causes

There are several factors that can contribute to the generation of false positives in Microsoft Sentinel. Some of these factors include:

Minimising the occurrence of false positives in Microsoft Sentinel

To minimise the occurrence of false positives in Microsoft Sentinel, organisations can take several steps to address these root causes. Microsoft recommends modifying rule queries, creating exceptions with automation rules, and using watchlists to centralise exception management.

Modify rule queries

One of the most effective ways to reduce false positives in Microsoft Sentinel is to modify the queries used by your security rules. By refining the criteria used to detect and generate security alerts, you can reduce the likelihood of false positives caused by overly broad detection criteria or misconfigured security rules.

Create exceptions with automation rules

Automation rules allow you to automate specific actions based on the output of your security rule queries, such as suppressing alerts for known benign activities or modifying the severity of certain alerts. This can help you minimise the impact of false positives on your security team’s workload and ensure that only genuine security incidents require manual investigation and response.

Use watchlists to centralise exceptions management

Watchlists are a powerful feature in Microsoft Sentinel that can help you centralise and streamline the management of exceptions and false positives. A watchlist is a custom list of data that can be used to enrich your security rule queries, providing additional context and information to help you differentiate between benign and malicious activities.

For example, you might create a watchlist containing the IP addresses of trusted external partners, or a list of known safe file hashes. You can then reference this watchlist in your security rule queries to filter out benign activities and reduce the likelihood of false positives.

Subnet-based exceptions

In some cases, you may need to create exceptions for entire subnets or IP ranges, rather than specific IP addresses or other individual data points. This can be useful for organisations with large or complex networks, where it may be necessary to exclude entire segments of the network from certain security rules in order to minimise false positives.

By incorporating subnet-based exceptions into your rule queries, you can further refine your detection criteria and reduce the likelihood of false positives caused by benign activities within your network.

Reduce false positives to the bare minimum with expert help

By embracing these strategies and continuously refining your Microsoft Sentinel configuration, you can transform your organisation’s security analytics capabilities, ensuring that your security team can focus on detecting, investigating, and responding to genuine cyber threats, rather than being bogged down by time-consuming and resource-draining false alarms.

The Microsoft Sentinel SIEM team at Steadfast Solutions can implement expert strategies, rules, and exceptions tailored to your organisation’s specific infrastructure, operations, and needs to minimise false positives. Talk to them today about ensuring the efficiency and effectiveness of your security environment.