3 Considerations as You Begin Your Advanced (Security) Analytics Program


The secret to using data’s full potential for security and remaining competitive in today’s data-driven environment? Advanced analytics.

The volume of data that enterprises need to analyze these days just keeps increasing. IDC’s Global DataSphere research vice president, John Rydning, made this astounding prediction: between 2022 and 2026, the global datasphere is predicted to more than double. There is an Everest-sized mountain of data to analyze, and there aren’t enough people to do it. Thus, the importance of having an advanced analytics program in place.

Why? Even just in the security function alone, there’s a vast amount of data being generated by the various tools and solutions being used there. It’s easy to understand why organizations and teams can become overwhelmed by it. When you employ sophisticated analytics in a useful way, you can filter out irrelevant information, generate insights, and ultimately boost productivity. Starting small and working your way up to more complex use cases is a good plan of action.

The three areas to think about while planning your advanced analytics program are discussed below, along with four recommended practices.

What are the assets on your infrastructure?

Companies today are trying to account for the components of their tech stack while also attempting to stop shadow IT from rogue behavior that could pose security threats. Data can show you the relationships between these components or endpoints, which can help you identify the gaps. But a dependence on manual processes increases the possibility of human mistakes, and a dearth of real-time asset management slows down or prevents efficient workflows.

Because data tends toward disorder and cloud-based services can be transient, not having real-time insights into the interactions between your assets and configurations might lengthen response time or hinder security operations and the delivery of IT services.

“Simple” queries add value

Security information and event management (SIEM) solutions can be expensive from a computational standpoint as well as laborious. Security analysts often prefer to hand off complicated analytics for SIEMs to handle, but doing so diminishes the usefulness of what you would consider to be “simple” queries.

Advanced analytics are enhanced by simple queries. When added to the results of more complex queries, simple queries like “How many failed login attempts happened in the last hour?” can help create a more accurate view of what is actually occurring in your environment. A search for certain CVEs (common vulnerabilities and exposures) to find vulnerabilities in the organization would be an example. A better and more thorough answer to the issue you’re attempting to solve is the outcome of these related queries.

See also: Cybersecurity Will Shift in 2023 Thanks to AI

Gaining clarity with advanced analytics

The thousands of notifications generated by security solutions create a problem for modern enterprises. The average security operations center (SOC) is thought to get anywhere from 4,000 to 11,000 alerts every day. However, as the volume of alerts increases, the number of SOC team members who can respond to alerts isn’t growing (as indicated by the estimated 3.4 million cybersecurity skills gap.) These days, conventional methods of managing these notifications won’t work.

Using advanced analytics, you can significantly reduce the number of threats that your human analysts must deal with. You can use these analytics, for instance, to assist in recognizing and removing false positives so that only reliable warnings are delivered to human analysts.

You can also use advanced analytics to intelligently group and catalog these warnings in a useful way via user analytics and computer analytics. User analytics allows you to tell what normal versus abnormal user behavior looks like. What does a typical day for a user look like? What kinds of work tasks do they carry out? If these components are combined, it’s practically possible to reproduce a typical day at work. So, profiling baseline user habits is one method of applying advanced analytics.

As for computer analytics, consider the computer as a user. What does normal look like when observing big server farms, for example? What are the typical tasks performed by each server? What normally don’t they do?

See also: Optimizing Incident Response Through Secure Collaboration

Where to begin: Four recommendations

Here are some suggestions to bear in mind as you start your adventure with advanced analytics.

  • More data should be easily accessible for analytics. You might begin by approaching the departmental data leads and requesting that they share data. This might mean cooperating with the desktop team to receive all their endpoint detection and response (EDR) data. Or you could collaborate with the Configuration Management Database (CMBD) team to acquire all asset inventory data.
  • Use a data lake for storage so that data may be accessed without having to be rehydrated (that is, the process in which data that’s become compressed becomes decompressed and readily accessible).
  • Use a data fabric to improve the automation and integration of data management and access.
  • Reorganize low-importance data to make it more economical to keep it or combine it with other data pieces to bring value where it can.

Advanced analytics shifts the balance

For enterprises dealing with resource constraints and an overwhelming amount of data, launching an advanced analytics program is essential. Understanding your network assets, employing “simple” queries, and minimizing low-value information are the three areas to concentrate on in order to gain insights, improve performance, and reduce risks. You can eliminate false positives, properly categorize alerts, and present human analysts with legitimate and pertinent threats by implementing advanced analytics approaches. You can also overcome obstacles and enhance analytics skills by implementing these tactics. The secret to using data’s full potential and remaining competitive in today’s data-driven environment? Advanced analytics.

Amish Amin

About Amish Amin

Amish Amin is the Executive Director, Security and Analytics, at Comcast. Amish leads the end-to-end data pipeline for cyber threat detection within Comcast. From developing high-value datasets to fine-tuning AI techniques, Amish’s team wears many hats. During his tenure at Comcast, he has been awarded “Data Science Manager of the Year” by Snowflake. Prior to this role, Amish was the Director of Advanced Analytics at Nationwide Insurance. He holds a bachelor’s degree in Accounting and Economics from The Ohio State University.

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