Cybersecurity: The Role of AI in Enhancing SOC Capabilities
2024-08-26
The challenge of maintaining full control over IT infrastructure in cybersecurity is substantial. The need for specialized skills often makes building a Security Operations Center (SOC) team is a costly and time-consuming endeavor. Additionally, the requirement for a 24/7 operation further complicates the task of creating an efficient SOC.
Energy Logserver has addressed these challenges by implementing an AI engine designed to improve SOC operations. The primary goal of this AI engine is to uncover hidden knowledge within the vast amounts of data processed by the system. This data can reach hundreds of gigabytes of incoming text per day. Moreover, the AI aims to eliminate the need for specialized knowledge in mathematics and data analysis. With each new version of Energy Logserver, new rules are delivered based on an already implemented mathematical stack.
Beyond Static Rules: AI-Driven Anomaly Detection
While SIEM systems are primarily intended to assist in detecting unwanted situations, determining which ones are critical can be complex. SIEM systems often rely on static rules that directly observe user behavior, assess login errors, or search for attack patterns. Energy Logserver’s AI module extends static analysis by detecting unknown and unique behaviors, both numeric and textual.
To develop the AI module, the team led by Artur Bicki (CEO) at Energy Logserver sought to emulate the thought process of a SOC operator. Experts can easily categorize log entries based on their familiarity with the source. They understand which logs are typical, indicative of attacks, or require further investigation. The AI module aims to replicate this ability by analyzing text and identifying unusual or suspicious patterns.
One of the key features of the AI module is its ability to create dedicated dictionaries that describe the source of the logs. These dictionaries contain the probability distribution of words specific to the given vendor, allowing SOC staff to create their own text analyses and fine-tune dictionaries for optimal effectiveness. This results in the immediate detection of unique entries, hidden logs, and randomized attacks through the risk assessment and anomaly scoring process.
The Importance of Data Quality and Human Expertise
“It is important to note that AI is not a magic bullet. While it accelerates analysis and detection, it is not a black box that produces spectacular results without effort. To effectively use AI, organizations must understand the underlying mathematical computations involved. For example, the anomaly detection implemented in Energy Logserver uses a set of algorithms tailored to different data types. – says Artur Bicki.
Moreover, data quality significantly impacts the outcome of AI analysis. The effective use of AI requires a new profile for IT professionals. SOC teams will need to be supplemented by Security Data Analysts, whose expertise in data analysis will be crucial for maintaining a secure IT infrastructure.
Conclusion
AI algorithms are a necessity and a clear direction for the development of cybersecurity protection. They reduce time spent analysing time and allow operators to focus on the most critical areas. By leveraging AI, organizations can enhance their ability to detect and respond to threats, ultimately improving their overall cybersecurity posture.
Schedule a visit to the “RIGA COMM 2024” Cybersecurity conference, which will take place at the Executive stage on October 4. Where Artur Bicki the CEO of Energy Logserver will share listeners more details on the topic: “The Role of AI in Enhancing SOC Capabilities”
Get your pass here.