AI for Cybersecurity: Threat Detection & SOC Automation

Explore the three core security data types, logs, alerts, and telemetry, and how they form the foundation of rule-based and AI-driven detection systems across systems, applications, and networks.

Enhance threat detection by delivering high-quality security data for AI models through cleaning, normalization, filtering, and feature selection, while reducing noise and false positives to avert SOC fatigue.

Learn to build a basic ai threat detection model in Google Colab by loading a security log dataset, selecting bytes sent and duration, and using isolation forest for anomalies.

Explore how AI-driven classification models enable fast threat detection, malware family classification, and attack type prediction to strengthen cyber security operations and SOC automation.

Learn to use behavioural analytics and UEBA to detect insider threats by establishing baselines, identifying deviations in login times, access patterns, and potential data exfiltration.

Leverage AI-driven threat intelligence enrichment to automate IOC extraction, correlate multiple feeds, and provide contextualized insights (TTPs and attacker links) for faster, prioritized defense.

Understand incident response automation in a soc, from detection to automated action via playbooks, with phishing scenario, threat intel checks, data collection and enrichment.

AI-assisted query generation translates threat scenarios into working SIM queries for Splunk SPL and KQL in Microsoft Sentinel, boosting threat hunting efficiency and incident response.

Configure Elastic SIEM rules with a custom query, data view, and index pattern, enabling threat match, indicator match detections, and automated alerts via response actions.

Monitor model drift in cybersecurity systems to keep threat detection accurate as data distributions, feature drift, and concept drift evolve with changing user behaviours and network environments.