Cybersecurity AI Foundation Program

Part 1 – AI Fundamentals
• Module 1: Introduction to AI
  • What is Artificial Intelligence: AI, Machine Learning, Deep Learning
  • Components of an AI System
  • Brief history and evolution: from rule-based systems to modern deep neural networks
  • How Machines Learn: Supervised learning, Unsupervised learning, Reinforcement learning
  • Types of AI Models and What They Do: Classification models, Regression models, Clustering models, Language models
  • AI Model development lifecycle
  • AI Tools Landscape: Huggingface, OpenWebUI, GenAI platforms, Open-source model platforms for running models locally: Ollama, LM Studio
  • Prompt Engineering Techniques
• Module 2: Python Basics for Using AI Frameworks
  • Python Fundamentals for AI
  • Lab Setup: Linux VM, Using Jupyter Notebook
  • Google Colab: free cloud compute for coding and AI/ML experiments
  • Python Libraries for Data Analysis and Visualisation: Pandas, NumPy, Matplotlib
  • Types of Data and using them for AI Model development
  • Secure and Responsible use of Data in AI models and Applications
• Module 3: Machine Learning for Security
  • Libraries and frameworks for Machine Learning and Deep Learning
  • Data Collection
  • Data Processing (Including Feature Extraction and Normalization)
  • Algorithm selection and Model Training
  • TEVV: Test, Evaluation, Verification, and Validation and Fine Tuning
  • Model Deployment, Model Monitoring and Retraining
  • Practical Lab: Building AI (ML and DL) models for Network Security by using GenAI
• Module 4: Natural Language Processing (NLP)
  • Libraries and frameworks for NLP
  • Text Processing for NLP: Tokenization, Stop words removal, Embeddings
  • NLP Feature Engineering Types
  • Converting Emails and Logs to Vectors
  • Practical Lab: Building a Phishing Mail Detector
• Module 5: Generative AI, LLMs and Agentic AI
  • Generative Adversarial Networks
  • Transformer Architecture
  • System Prompts and User Prompts
  • Prompt Engineering Techniques
  • Foundational model and fine-tuned model
  • Retrieval Augmented Generation (RAG)
  • Agentic AI: Autonomous agents, Planning, Reasoning, Action: tool use, and multi-agent orchestration frameworks
  • Agentic AI Frameworks: Langchain, CrewAI
  • Model Context Protocol (MCP)
  • Practical Lab: Building a Gen AI Based Custom Chatbot and Agent

Part 2 – AI Security and Governance

• Module 6: Attacking AI
  • Poisoning: Data and Model Poisoning
  • Backdoor Attacks with Trigger-Based Poisoning
  • Evasion Attacks: Evading AI-Based Detectors
  • Model and Data Theft
  • Inversion and Membership Inference Attacks
  • OWASP Machine Learning Security Top 10
  • Prompt Injection: Direct and Indirect
  • Jailbreaking Techniques
  • OWASP Top 10 for Large Language Model Applications
  • OWASP Top 10 for Agentic Applications
  • Practical Lab: Automated LLM Pentesting using Garak
• Module 7: Securing AI
  • Threat Modelling: MITRE ATLAS, MAESTRO Framework
  • Defense-in-Depth for AI
  • Secure Infrastructure Architecture for AI Systems
  • Data Security in AI Systems
  • Adversarial Training and Bias Checking
  • Guardrails for LLMs: System Prompt Hardening, Input and Output Guards
  • Open-Source Guardrail Tools: LLM-Guard, Guardrails-AI, LlamaGuard
  • AI Gateway: LiteLLM
  • Monitoring and Observability for AI Systems: MLFlow, Arize Phoenix
  • Rate Limiting & Cost Budgeting
  • Data and Model Versioning
  • Access control for AI
  • Data and Model Integrity
  • AI Supply Chain Security
  • Practical Lab: Integrating guardrails with AI models
• Module 8: AI Governance
  • AI Ethics & Properties of a Responsible AI System
  • Data Governance & Model Governance
  • NIST AI RMF (Govern/Map/Measure/Manage) and ISO 42001 Annex A Controls
  • EU AI Act risk tiers
  • Security Roles & Operating Model for AI
  • Practical Lab: Governance control mapping exercise

Part 3 – Security Operations and Offensive Security using AI

• Module 9: Offensive Security using AI
  • AI in the Penetration Testing Lifecycle: Reconnaissance, Scanning, Exploitation, Reporting
  • Reconnaissance using AI: OSINT Tools, AI-Generated Recon Scripts
  • Intelligent Scanning and Enumeration: Nmap with AI, AI-Generated Scan Profiles
  • AI-Enhanced Password Attacks: Wordlist Generation, Brute Force Automation
  • Web Application Pentesting with AI
  • Social Engineering with AI: AI-Generated Phishing Emails
  • Covering Tracks and Maintaining Access with AI-Generated Scripts
  • Practical Lab: AI-Assisted Reconnaissance, Network Scan, Exploitation, and Pentest Report Writing
• Module 10: Security Operations using AI
  • Security Operations: Structure, Roles, and Core Functions
  • Signature based detection principles
  • AI Integration in Security Operations
  • Using ML+Signatures for best results
  • AI for Log Analysis: Windows Event Logs, Network Logs
  • AI for Alert Triage and False Positive Reduction
  • Threat Intelligence: IOCs, MITRE ATT&CK, AI-Assisted IOC Correlation
  • Vulnerability Management with AI: CVE Explanation, Prioritization, Remediation
  • Incident Response Lifecycle with AI-Guided Playbooks
  • User and Entity Behaviour Analytics (UEBA)
  • Malware detection using AI
  • Using AI with SIEM tools (ELK Stack)
  • Practical Lab: Analysis of IDS Logs using Generative AI and Agentic AI

• Cybersecurity Professionals – Security Analysts, SOC Analysts, Security Engineers, Detection Engineers
• Offensive Security Professionals
• GRC Professionals and Security Auditors who want to understand how AI systems work
• Security Professionals who want to learn how to leverage Open-source AI tools and LLMs securely in their workflow
• Anyone who wants to understand how AI models as a security control are built
• Anyone who wants to transition to AI Security Roles

• Fundamental Knowledge of Security Concepts
• No coding or AI Knowledge required

After completion of this course, you will be able to:

• Understand how AI systems work and how they apply to security
• Build AI-based Security Systems
• Identify and Exploit AI Vulnerabilities
• Secure AI Systems
• Implement Governance and Compliance for AI
• Use AI for Defensive (SOC) and Offensive Security