Security Analytics

No organization has a crystal ball and thus cannot predict the future, particularly where security threats are involved. However, by using security analytics tools, your organization can better analyze security events and potentially detect a threat before it impacts your revenues or infrastructure.

In this page, we will walk through the term security analytics, define the benefits of this approach, examine use cases, and see how security big data analytics works.

In this page:

What is Security Analytics?

Security analytics is a proactive security approach that uses big data analytics and machine learning to gather, categorize and analyze data collected from network devices to detect advanced threats.

These solutions aggregate data from a myriad of sources like endpoint and user behavior data, business applications, external threat intelligence sources, and non-IT contextual data.

Machine learning technology plays an important role in modern cybersecurity, allowing for near real-time data and threat analysis. Analysis of inputs including asset metadata, geolocation, threat intelligence, and IP content, can be used for rapid threat response and for forensic investigation.

The need for security analytics
The cybersecurity sector is growing due to advances in the techniques and tactics of cyber attackers, which can compromise a system in seconds and sometimes go undetected for months. Attacks are often difficult to detect because they happen quickly and the indicators can be dispersed across different data sources, such as network servers, endpoints, and applications.

Security analytics provide organizations with visibility into complex attack techniques such as compromised credentials, lateral movement, and data exfiltration. Unlike traditional security tools, it helps with early detection of attackers through the analysis of user account activities for insider threat behaviors. Security analytics can also provide information back to the organization’s security ecosystem, allowing other systems to act on suspicious activities.

Benefits of data analytics for security

The ability to analyze large amounts of data from various sources near real-time gives security analytics an advantage over traditional security approaches.

Proactive security
Security analytics correlates events based on combined data from logs and other sources, in near real-time, detecting indicators of suspicious activity. The result is proactive security that helps organizations detect threats earlier in the attack chain and significantly improve response times.

Maintaining regulatory compliance
Security analytics assists with adherence to industry and government regulations such as PCI-DSS, HIPAA, and GDPR. It monitors access, authentication, and user behavior, helping with insider threat detection, and collects log data for auditing.

The platform reporting capabilities provide companies with a unified view of all data events helping compliance managers identify potential non-compliance.

Improved forensic capabilities
Security big data analytics are especially useful for forensic investigation. This tool provides information about the origin of an attack, how it happened, and the extent of the damage. By exposing the existing compromise, security teams can pinpoint when an attack may have started and build an accurate attack timeline.

Security analytics use cases

Cybersecurity analytics have a multitude of use cases, from network monitoring to forensic investigation. Some of the most common use cases include:

  • Monitor employees to detect insider threats—monitors critical and sensitive systems and analyzes user actions for suspicious behavior, including monitoring privileged users through metadata, keystrokes, and forensics capabilities.
  • Analyze network traffic—correlates events and detects patterns that may indicate a potential attack.
  • Monitor user behavior to detect threats—the analytics platform utilizes user and entity behavior analytics (UEBA) to profile suspicious behavior by using algorithms to uncover patterns and identify indicators of malicious activity in user behavior.
  • Identify endpoint threats—the platform uses endpoint threat detection to reveal attackers targeting the endpoints of an organization.
  • Detects data exfiltration—security analytics prevents the unauthorized download or copying of data by blocking unauthorized communication channels and stopping users from submitting their credentials to non-enterprise sites, thus preventing credential theft from phishing attacks.
  • Help you stay within compliance regulations—the platform helps your organization automate compliance requirements, such as log data collection, managing personal data flow, monitoring data activity, and compiling reports, enabling the compliance team to identify compliance violations.

 

Security big data analytics

Today, an organization’s data exists across a diverse set of assets, applications, and operating systems. Data volumes continue to increase, so security big data analytics are critical to understanding organizational risk.

The role of big data analytics in security
Big data analytics uses advanced statistical and data science models to detect anomalies in real time for threat analysis. These solutions generate security alerts and combine them with additional forensic data to detect and respond to cyber threats. Big data analytics allow organizations to monitor for insider threats, triggering automated workflows should risky behaviors be detected.

To expose suspicious behavior, these tools use machine learning and data analysis to predict attacks and create baselines for what is a normal activity. Security teams are alerted to anomalous user behavior and can create timelines to fully understand the end-to-end impact.

Machine learning in cybersecurity
Machine learning involves training computers against data sets, allowing them to identify patterns. Security analytics uses machine learning, combined with additional data science-driven statistical modeling capabilities to identify both known and unknown patterns with a high degree of accuracy.

Through machine learning, advanced threat analysis can:

  • Create a baseline of normal activity to model anomalies. This applies both for user activity or for network traffic.
  • Analyze malware activities that escape antivirus detection to define if they are a threat or not.
  • Correlate historic data of intrusions and attacks to identify patterns and detect intrusions.

 

Stream data processing for real-time threat analysis
Stream data processing enables organizations to perform threat analysis near real-time, analyzing data as soon as it is available to identify threats earlier in the attack chain. Stream data processing integrates data from external sources, continuously generating dynamic data.

Conclusion

The use of security big data analytics and machine learning tools helps organizations identify suspicious activities, detect insider threats, and monitor user behavior near real-time. Security teams can thus keep up with the increasing sophistication of cyber threats, predict attacks, and stop cyber threats before they cause damage.