Cybersecurity Analytics Definition

What is Cybersecurity Analytics?

Cybersecurity analytics is an essential cybersecurity method that uses data analysis and advanced statistical and machine learning techniques to extract useful threat intelligence from data. This information enables an organization to develop a proactive cybersecurity strategy that identifies, analyzes, and deters cybersecurity threats.

The Cybersecurity Data Problem

One of the most common challenges that cybersecurity teams face is data overload. Companies have a variety of IT systems and numerous cybersecurity tools designed to monitor and secure these systems. All of these computers, programs, and security tools have the ability to generate log files and alerts to keep their operators aware of what is going on with them and potential threats to the organization.

With so many potential data sources, most security teams end up with more security data than they can effectively review, analyze, and use. As a result, cyberattacks might slip through the cracks because the security team doesn’t have the resources to look at and effectively analyze the right data.

How Cybersecurity Analytics Works

Cybersecurity analytics leverages automated processes and statistical analysis to weed through the mountain of security data and extract useful insights for a security team. Some of the core tools and techniques used for this include:

• SIEM: A security information and event management (SIEM) solution collects an organization’s security data in a central location, normalizes it, and performs analytics on it. This provides a more comprehensive view of the state of the organization’s security program and can provide more accurate alerts based on contextual data.
• Data Mining: Various statistical techniques can be used to identify trends, anomalies, and other data of interest. These unusual data points may indicate potential cyberattacks or other issues within an organization’s systems.
• Machine Learning: Machine learning and artificial intelligence (ML/AI) algorithms are designed to process large data sets and build models based on underlying trends and other features. This makes them ideally suited to processing large volumes of security data and extracting useful threat intelligence that an organization’s security team can use.

Benefits of Cybersecurity Analytics

Cybersecurity analytics provides numerous benefits to the organization, including:

• Reducing Data Overload: Cybersecurity analytics leverages automated tools and processes to distill data down into useful insights and threat intelligence. This helps security teams to manage large volumes of security data.
• Improved Visibility: Security analytics looks for the trends, anomalies, and other “interesting” data in a dataset. With access to this information, an organization can better understand what is going on and going wrong within its IT environment.
• Faster Threat Detection and Response: Security analytics can identify the warning signs of a cyberattack against an organization or a threat in its environment. By creating high-value security alerts, security analytics enables analysts to find and respond to threats more quickly.
• Enhanced Security: Security analytics provides data on how an organization’s security architecture is doing. This enables the company to identify and fix shortcomings or focus on what is working.

Conclusion

Security analytics is designed to convert large volumes of data into useful intelligence that an organization can use. A mature security analytics program enables a company to be more proactive by finding and fixing threats faster and designing defenses against impending security risks.