Virtual Workshop AI-Powered Cyber Threat Intelligence: Dark Web Analysis & Proactive Defense

Introduction to the Course

Course Objectives

• Understand the application of AI in the detection, analysis, and prediction of cyber threats in the context of dark web operations.
• Learn how to harvest and analyze dark web data using AI methods for the identification of cyber crimes and new threats.
• Acquire knowledge of frameworks for cyber threat intelligence and their application in proactive defense strategies.
• Learn the application of AI-powered tools for anomaly detection, threat hunting, and response in real-time scenarios.
• Learn how to apply AI-powered cyber threat intelligence to organizational cybersecurity strategies and risk management practices.
• Acquire skills in the application of automated threat monitoring and predictive defense through AI for early detection and response.

What Will You Learn (Modules)

Module 1 — Introduction to Cyber Threat Intelligence (CTI) and Threat Actors

• CTI basics: Lifecycle, types of intelligence, and its importance
• Who’s out there: understanding threat actors, attack vectors, and cybercrime tactics (phishing, malware, APTs)
• Open-Source Intelligence (OSINT) tools: WHOIS, Shodan, and Maltego—how to collect, analyze, and interpret data from public sources

Module 2 — Collection and Processing of Threat Data

• Types of data: open-source, closed-source, and internal data sources
• Data collection techniques: passive vs active, ethical and legal considerations
• Using tools like MISP and ThreatConnect for IOC analysis (Indicators of Compromise)

Module 3 — Advanced Threat Analysis and Attribution

• Introduction to AI/ML techniques in CTI for automated detection
• Building and training AI models for threat identification
• Hands-on Lab: Apply AI models for real-time threat monitoring

Module 4 — AI-Powered Threat Detection and Monitoring

• Introduction to AI/ML techniques in CTI for automated detection
• Building and training AI models for threat identification
• Hands-on Lab: Apply AI models for real-time threat monitoring

Module 5 — Operational Integration and Threat Intelligence Sharing

• Integrating CTI into SIEM and SOAR platforms for seamless security operations
• Automating the threat detection and response process
• Intelligence sharing standards: ISACs, STIX/TAXII—how to collaborate with the cybersecurity community for better defense

Who Should Take This Course?

This course is ideal for:

• Cybersecurity professionals looking to enhance their skills in AI-powered threat intelligence
• Incident responders and security analysts who want to integrate AI into their threat monitoring workflows
• Data scientists interested in applying AI to cybersecurity use cases
• Security researchers focused on dark web activities and cybercrime trends
• Students or junior professionals in the cybersecurity or data science fields looking to enter AI-driven threat intelligence

Job Opportunities

After completing this course, learners can pursue roles such as:

• Bioinformatics Analyst (RNA-Seq)
• Computational Biologist
• Genomics Researcher
• Transcriptomics Specialist

Why Learn With Nanoschool?

At NanoSchool, we focus on career-relevant learning that builds real capability—not just theory.

• Expert-led training: Learn from instructors with real-world experience in applying skills to industry and research problems.
• Practical & hands-on approach: Build skills through guided activities, templates, and task-based learning you can apply immediately.
• Industry-aligned curriculum: Course content is designed around current tools, workflows, and expectations from employers.
• Portfolio-ready outcomes: Create outputs you can showcase in interviews, academic profiles, proposals, or real work.
• Learner support: Get structured guidance, clear learning paths, and support to stay consistent and finish strong.

Key outcomes of the course

Upon completion, learners will be able to:

• In-depth knowledge of AI-powered threat intelligence and its use in dark web monitoring
• Hands-on skills in automated threat detection, risk analysis, and proactive defense
• Comfort level in using AI-powered cybersecurity tools for incident response and threat mitigation
• Ability to incorporate machine learning models in cyber defense strategies and frameworks
• Capstone project that showcases your skills in designing and developing an AI-powered cyber threat intelligence solution