LSET’s AI in Digital Forensics & SOC Automation Course provides a deep dive into the convergence of artificial intelligence and cyber defence. This course is thoughtfully structured to equip you with both foundational and advanced knowledge in automating Security Operations Centre (SOC) processes and performing intelligent digital forensics. Through a hands-on, practical approach, you’ll gain the skills to leverage AI for detecting, analysing, and responding to complex cyber threats with precision and speed. From log classification using NLP to automated alert correlation, this course empowers you to streamline Tier-1 triage and boost overall SOC efficiency.
Technologies & Tools Covered in This Course
Elastic Stack (ELK)
A powerful set of tools – Elasticsearch, Logstash, and Kibana – used for centralised logging, visualisation, and forensic timeline reconstruction, enabling deep insights into security events.
Splunk
A leading platform for machine data analysis, Splunk helps in parsing, indexing, and correlating logs from various sources to detect anomalies and generate real-time alerts.
MITRE ATT&CK Framework
An industry-standard matrix for understanding attacker behaviour, mapping tactics and techniques to improve threat detection and SOC automation strategies.
Python & Scikit-learn
Python’s machine learning libraries are used to build intelligent models for log analysis, behaviour profiling, and automated threat classification.
spaCy & NLTK
Natural Language Processing libraries for classifying and extracting meaningful patterns from logs and incident reports using AI.
SOAR (Security Orchestration, Automation, and Response)
Tools like Palo Alto Cortex XSOAR or IBM Resilient to automate security workflows, integrate threat intelligence, and accelerate response times.
Zeek (formerly Bro)
An open-source network analysis tool that provides rich protocol-level insights, enhancing detection and investigation of suspicious traffic.
ELMo/BERT (NLP Models)
Advanced language models used for interpreting log data contextually, enabling more accurate classification and clustering of security events.
OpenCV & Anomaly Detection Models
Used for user behaviour analysis, visual threat recognition, and profiling suspicious activities across endpoints and networks.
Career Accelerator Program
Premium Career-Ready Track
Note: Please note that all prices listed are exclusive of VAT. VAT will be charged separately and added to the total amount payable.
