Transforming Log Analytics: How We Saved a Company Over $100,000

Introduction

In today’s data-driven world, organizations heavily rely on log analytics to monitor and secure their IT infrastructure. Microsoft Sentinel is a powerful tool that provides advanced security analytics and threat detection. However, as data continues to grow exponentially, managing and analyzing these logs efficiently can become a significant challenge.

In this blog post, we’ll explore how our team leveraged data transformation techniques in Microsoft Sentinel to save a company over $100,000 a year. We’ll take a closer look at the data ingestion process, compare it before and after the transformation, and highlight the cost-saving and performance-improvement benefits.

How we went from this:

To this:

The Challenge

Our client, a medium-sized enterprise, was facing two major challenges with their log analytics environment in MicrosoftSentinel:

1. High Costs: The company was incurring substantial monthly costs due to the sheer volume of log data ingested into Microsoft Sentinel. The expense was becoming unsustainable and required immediate attention.
2. Inefficient Queries: With a vast amount of raw log data, query performance was sluggish and overwhelming, impacting their ability to detect and respond to security threats in real-time. The client needed a solution to optimize query performance.

The Solution

Our team recognized that the key to addressing both challenges lay in optimizing the data ingestion process through data transformation. We implemented the following steps:

1. Data Source Assessment: We thoroughly assessed the types of logs being ingested and their relevance to the client’s security and operational needs. This involved identifying unnecessary data sources that were contributing to high costs.
2. Data Transformation: We implemented data transformations in Microsoft Sentinel to preprocess the raw log data. This involved cleaning, filtering, and aggregating data to reduce its volume while retaining critical information for security analysis.
3. Query Optimization: With transformed and optimized data, query performance improved dramatically, allowing security analysts to query and analyze logs more efficiently.

Before and After: Data Ingestion Comparison

Let’s take a closer look at the transformation process by comparing data ingestion before and after our intervention:

Before Transformation:

• Raw log data was ingested directly into Microsoft Sentinel.
• Ingestion cost is calculated based on the bytes.
• The average byte size per entry was 1,053.

After Transformation:

• We identified some columns filled with redundant or unneeded information.
• Storage costs were reduced significantly as less data was retained for longer durations.
• Query performance improved, reducing filtering needed to see relevant data.

The Results

By implementing data transformation techniques in Microsoft Sentinel, we achieved remarkable results for our client:

1. Cost Savings: The company saved over $100,000 annually by reducing redundant or useless data ingested.
2. Improved Performance: Query performance improved, enabling faster threat detection and response.
3. Enhanced Security: With more efficient log analysis, the client could better identify and respond to security threats, reducing potential security breaches and associated costs.

Conclusion

In today’s data-driven landscape, optimizing log analytics environments is essential for both cost-saving and efficient threat detection. Microsoft Sentinel’s data transformation capabilities proved to be a game-changer for our client, helping them save over $100,000 a year while significantly improving query performance and security.

If your organization is grappling with high log analytics costs or inefficient query performance, consider implementing data transformation in your log analytics environment. It could be the key to unlocking substantial cost savings and enhancing your security posture.

Contact us today at contactsales@concurrency.com to learn more about how we can help you transform your log analytics environment and achieve similar results!