Security Analysis with AI

Learning Objective - Gain comprehensive proficiency in various aspects of cybersecurity, including cybersecurity data analysis, anomaly detection, and malware analysis. Explore advanced techniques such as threat hunting with AI and integration of AI technologies with Security Information and Event Management (SIEM) systems for enhanced threat detection and response. Additionally, delve into endpoint security with AI-driven solutions for effective threat monitoring, detection, and prevention. Finally, acquire skills in cyber forensics leveraging AI tools for evidence correlation, incident reconstruction, and comprehensive investigations.

Audience – Any intermediate-senior level professional in cybersecurity

Pre-Requisite: Understanding AI for building secure designs involves gathering security requirements, determining organization assets, and constructing secure designs with AI's assistance. Utilizing AI for secure implementation entails learning about common insecure code practices, employing AI to secure written code, and adopting secure technologies. User authentication and authorization involve implementing robust mechanisms like multi-factor authentication, role-based access control, and the least privilege principle. Employing AI for secure testing involves understanding common insecure code practices, using AI to secure code, and adopting secure technologies. Integrating AI into DevSecOps entails automated code analysis, ensuring secure pipelines and repositories, and effective access management. Leveraging AI-powered security solutions includes deploying AI-powered web application firewalls, monitoring cloud environments, and utilizing AI-powered endpoint protection solutions.

Cyber Threat Intelligence (4 hours)

· Cybersecurity Data Analysis

· Anomaly Detection in Networks

· Intrusion Detection Systems (IDS)

· Intrusion Prevention Systems (IPS)

Malware Analysis and Detection (4 hours)

· Behavioural Analysis for Cybersecurity

· Threat Hunting with AI

· Cybersecurity Operations and Automation

Security Information and Event Management (SIEM) with AI (8 hours)

· Enhancing SIEM with ML for advanced threat detection, correlation, and analysis.

· Using AI-driven anomaly detection to spot suspicious activities in log and event data.

· Applying NLP to categorize events for prioritization and response.

· Integrating AI predictive modelling for forecasting threats based on historical data.

Endpoint Security with AI (16 hours)

· Deploying AI-driven EDR to monitor, detect, and respond to threats on endpoints.

· Using ML to analyse telemetry data, logs, and user behaviour for early threat detection.

· Employing AI-based EPP to detect and prevent malware and advanced attacks.

· Integrating AI-powered endpoint security with threat intel feeds for coordinated response.

Cybersecurity Forensics with AI (8 hours)

· Utilizing ML for digital forensics, including disk imaging and memory forensics.

· Employing AI tools to correlate evidence and reconstruct incident timelines.

· Applying NLP for analysing forensic reports and legal documents.

· Integrating AI forensics with incident response and threat hunting for comprehensive investigations.