AI-Powered SOC Analyst Assistant

AI-Powered SOC Analyst Assistant

DEVARAPALLI SEKHAR BABU*1, SHAIK RUKSAR2, KADIYALA VISHNU GOPI3, N V N S MAHIMA4, P SIVA BRAHMA REDDY 5

1Assistant Professor, Department of CSE(CB), Bapatla Engineering College, Bapatla 522101, AP,India

2Student, Department of CSE(CB),Bapatla Engineering College, Bapatla 522101,AP, India

3Student, Department of CSE(CB), Bapatla Engineering College, Bapatla 522101, AP, India

4Student, Department of CSE(CB), Bapatla Engineering College, Bapatla 522101, AP, India

5Student, Department of CSE(CB), Bapatla Engineering College, Bapatla 522101, AP, India

Abstract— Security Operations Centers, or SOCs, basically keep watch over all the cybersecurity stuff in a company. They monitor events, check them out, and handle threats from different systems all the time. But with how fast everything digital is growing, theres just so many logs piling up. Its getting hard to go through them by hand, you know.

Traditional SIEM systems, those Security Information and Event Management ones, they mostly use fixed rules to spot attacks. That means they only catch the patterns someone already knows about. So, a lot of false alarms pop up, and they miss those new zero-day threats that no one has seen before. Analysts end up with way too much to think about, which slows everything down and makes responses take longer. It seems like that cognitive load thing is a big issue.

This research is trying to fix that with something called an AI-Powered SOC Analyst Assistant. Its like an automated helper that uses machine learning and these large language models for reasoning. The idea is to make threat detection and response happen in real time, more adaptively. First off, it pulls in logs from places like authentication setups, network gear, and firewalls. Then it cleans up the data, normalizes it so its consistent for analysis later.

One main part is the anomaly detection using Isolation Forest algorithm. Its unsupervised, which is cool because it doesnt need labeled data to find weird patterns. That helps spot unknown attacks that traditional stuff misses. After that, theres event correlation that links related logs together into actual incidents. It cuts down on repeats and gives better context, I think.

The system also has this LLM reasoning engine that explains anomalies in plain words. It turns alerts into useful summaries, rates how serious they are, and helps with decisions. Combining ML with natural language stuff like that should speed up analysis and make it easier to understand. Without it, things might stay pretty opaque.

In experiments, this setup did better than the old ways. Detection accuracy went up, false positives dropped, and time for checking alerts went from minutes to almost instant. It handles both known threats and the surprise ones, which seems effective for changing environments. That part stands out.

Overall, it looks like a solid way to scale up SOC operations. Automating the tough analysis parts and supporting analysts could reduce fatigue and improve visibility. Some challenges still linger, like integrating it everywhere, but it contributes to smarter cybersecurity down the line. I might be oversimplifying, though.

Key Words— Cybersecurity, SOC, Anomaly Detection, Isolation Forest, Machine Learning, LLM, Threat Detection, Log Analysis.