Security information and event management (SIEM) is a crucial tool that offers real-time monitoring and analysis of security-related events as well as tracking and logging of security data for compliance or auditing purposes. SIEM plays an important role in identifying security incidents and helping IT and security teams respond effectively.

However, as threats become more sophisticated, SIEM solutions must evolve to keep up. The future of SIEM lies in predictive analytics and machine learning, which can help organizations prevent attacks before they occur.

What is predictive analytics?

Predictive analytics is a type of advanced analytics that uses statistical modeling, data mining techniques and machine learning to forecast future outcomes based on historical data. Companies use it to identify risks and opportunities by finding patterns in data.

Predictive analytics is linked with big data and data science. Nowadays, organizations have a large amount of data in different repositories, and data scientists extract insights using deep learning and machine learning algorithms. Techniques such as logistic and linear regression models, neural networks and decision trees are used to make predictions. These modeling techniques use initial predictive learnings to make additional predictive insights.

SIEM with predictive analytics vs. traditional SIEM: The major differences

The largest application of security analytics lies in its crucial role in threat monitoring and incident investigations, according to a paper presented at the National Conference on Information Assurance (NCIA) in Pakistan.

Its primary focus is on the discovery and comprehension of both known and unknown cyberattack patterns. This capability is expected to have a significant impact on enhancing the efficiency of identifying concealed threats swiftly, enabling the tracking down of attackers and even predicting future attacks with greater accuracy and a reduced false positive rate. By harnessing the power of security analytics, organizations can bolster their cybersecurity defenses, ensuring a safer and more secure digital landscape.

The conventional cybersecurity approach focuses on identifying and catching malware through the scanning of incoming traffic using malware signatures. This traditional method has its limitations, as it can only detect threats that have been encountered and documented in the past. That leaves room for potential gaps in the defense against emerging and unknown threats. In today’s rapidly evolving digital landscape, relying solely on traditional methods may not be sufficient to safeguard against sophisticated and novel cyberattacks and threat actors.

What are the benefits of predictive analytics in SIEM?

There are several benefits of predictive analytics in SIEM over traditional SIEM solutions, including:

• Early detection of threats. By using machine learning algorithms to analyze data, predictive analytics can identify potential threats before they occur. This allows organizations to take proactive measures to not only prevent attacks but also minimize the impact of security incidents.
• Better accuracy. With predictive analytics, SIEM solutions can analyze large volumes of data and identify patterns that may be missed by human analysts or traditional SIEM solutions. This improves the accuracy of threat detection and reduces false positives significantly.
• Increased efficiency. By automating data science and data engineering tasks, predictive analytics can free up IT and security teams to focus on more strategic tasks, such as incident response planning and threat hunting.

What are some examples of predictive analytics in SIEM?

User and entity behavior analytics (UEBA) and network detection and response (NDR) are great examples of predictive analytics at work in SIEM solutions.

User and entity behavior analytics (UEBA)

UEBA is a type of security software designed to identify abnormal and potentially harmful user and device behavior using behavioral analytics, machine learning algorithms and automation. UEBA is especially effective at detecting insider threats, which might go unnoticed by other security tools since they mimic authorized network traffic.

SIEM solutions collect security event data from multiple internal security tools, aggregate it into a single log and analyze it to identify unusual behavior and potential threats. UEBA can enhance SIEM’s visibility into the network by detecting insider threats and analyzing user behavior.

Network detection and response (NDR)

NDR is a cybersecurity technology that employs non-signature-based methods, including artificial intelligence, machine learning and behavioral analytics, to detect and respond to suspicious or malicious activities on a network. NDR systems are particularly effective in identifying threats that might go unnoticed by traditional security tools that rely on signature-based detection.

By integrating NDR tools with SIEM, organizations can enhance their security and regulatory compliance workflows. NDR tools can stream network traffic data and analysis to a SIEM, providing valuable insights.

How can QRadar SIEM support your organization with predictive analytics?

The IBM QRadar SIEM User Behavior Analytics (UBA) app leverages a machine learning add-on, which augments the UBA app. It includes rules and tuning, allowing you to determine the QRadar SIEM’s parameters. Security teams can enhance the UBA capabilities and automate incident response, making it easier to detect and prevent insider threats.

QRadar SIEM has NDR built in, which augments traditional log data by monitoring key network flow data so you increase the scope of protection. Additionally, the QRadar Network Threat Analytics app analyzes the flow records on your system to determine normal traffic patterns and then compares all incoming flows to the latest network baseline created by the app.

The future of cybersecurity

Predictive analytics in SIEM represents the future of cybersecurity. By using machine learning algorithms to analyze data, organizations can detect potential threats before they occur and stay ahead of the game. SIEM solutions that utilize predictive analytics offer several benefits over traditional SIEM, including early detection of threats, better accuracy, increased efficiency and scalability. Are you ready for the future of cybersecurity? Stay ahead of the game by investing in a SIEM solution with predictive analytics today.

If you are interested in learning more about how QRadar SIEM uses predictive analytics, schedule a 1:1 demo with an IBM Security expert here.