Model Online / On-Site
Duration
Modules 5 modules

Course Objective & Scope

As adversaries rapidly adopt automation and generative AI, defensive teams must deploy both classical machine learning and LLMs in the right places to cut alert fatigue, catch threats earlier and compress investigation times. This course demonstrates which cybersecurity problems are best solved by classification, clustering, graph analytics, RAG or LLM agents.

Participants build working prototypes for URL/email phishing detection, automated IOC and MITRE ATT&CK TTP extraction from threat intelligence reports, and contextual CVE prioritisation, concluding with a SecOps AI roadmap for their organisation.

Who Should Attend?

  • Cybersecurity engineers, SOC analysts and security architects
  • Data scientists and developers building security products and internal SecOps automations
  • Cyber threat intelligence (CTI) and vulnerability management teams
  • Prerequisite: core cybersecurity fundamentals and beginner-to-intermediate Python skills

Key Takeaways

  • Select the right paradigm—classical ML, deep learning or LLMs—for specific cybersecurity problems
  • Build a phishing and malicious domain (DGA) classifier from URL, header and content features
  • Extract STIX/TAXII-ready IOCs and MITRE ATT&CK TTPs from unstructured CTI reports using LLMs
  • Enrich SAST findings and CVE advisories with asset context to prioritise remediation
  • Manage false-positive and hallucination risks in AI models deployed across security operations

Curriculum

Mapping AI in Cybersecurity: Offense and Defense
  • Adversarial use of AI (polymorphic phishing, automated recon) and corresponding defensive capabilities
  • Choosing between deterministic rules, supervised/unsupervised ML and LLMs/RAG
Phishing, DGA and Malicious Content Classification
  • Feature engineering and transformer classification across URLs, DNS/DGA queries and email bodies
  • Optimising for high precision and low false-positive rates on heavily imbalanced security datasets
Cyber Threat Intelligence (CTI) and MITRE ATT&CK Automation
  • Extracting IOCs, threat actors and MITRE ATT&CK techniques from unstructured CTI reports with LLMs
  • Querying internal threat intelligence archives via a permissioned RAG knowledge base
Vulnerability Management, Code Analysis and Patch Prioritisation
  • Risk scoring models combining CVSS, EPSS, CISA KEV and internal asset criticality
  • Triaging SAST/DAST false positives and generating developer-ready secure code fixes
SecOps AI Governance, Data Privacy and Reliability
  • On-prem and hybrid deployment patterns that keep sensitive incident logs inside the enterprise boundary
  • Human-in-the-loop safeguards, model explainability (SHAP/LIME) and operational SecOps KPIs

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