AI-Powered Cybersecurity Training Course Online

Part 1: Understanding and Using AI
• Module 1: Introduction to AI and AI in Cybersecurity
  • AI and its Evolution
  • Components of an AI System
  • AI vs ML vs Data Science
  • Algorithm vs Model
  • Types of Learning – Supervised, Unsupervised, Semi-Supervised, Reinforcement, Federated
  • Types Of Models
  • AI Use Cases in Cybersecurity
  • Risks of AI
• Module 2: Python Basics for Using AI Frameworks
  • Python Fundamentals for AI
  • Lab Setup: Linux VM, Using Jupyter Notebook
  • Python Libraries for Data Analysis and Visualisation – Pandas, NumPy, Matplotlib
  • GenAI Platforms: Google Colab, Hugging Face, Ollama, LMStudio, OpenAI Platform, Google AI Studio, Anthropic, Groq
  • AI Model Development Lifecycle
  • Types of Data and using them for AI Model development
  • Secure and Responsible use of Data in AI models and Applications
• Module 3: Using AI for Cyber Offense
  • Using AI for:
    • Reconnaissance
    • Vulnerability Scanning
    • System Hacking
  • Hands On: ShellGPT, Open-Source Models for Offensive Security
  • Hands On: Generating Network Attacks Using AI
  • Hands On: Collecting Network Attack Traffic Data: Packets and Logs.
• Module 4: Building ML-based Security Controls using Generative AI
  • Data Collection
  • Data Processing (Including Feature Extraction and Normalization)
  • Model Training
  • TEVV: Test, Evaluation, Verification, Validation, and Fine Tuning
  • Model Deployment, Model Monitoring and Retraining
  • Hands On: Using Gen AI tools for Building AI models for Network Security
  • Hands On: Building Anomaly Detector using Unsupervised Learning using Gen AI Tools
  • User and Entity Behaviour Analytics (UEBA)
  • Hands On: Building Keystroke Behaviour Analysis Detector
• Module 5: Natural Language Processing (NLP)
  • Text Processing for NLP: Tokenization, Stop words removal
  • NLP Feature Engineering Types
  • Hands On: Converting Emails and Logs to Vectors
  • Hands On: Building a Phishing Mail Detector
• Module 6: Neural Networks and Deep Learning for Cybersecurity
  • Perceptron
  • Multi-Layer Perceptron (MLP)
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • How Deep Learning enables Building Models for Complex Cybersecurity Data
  • Hands On: Building Malware Detectors Using CNN
• Module 7: Generative AI and LLMs
  • Generative Adversarial Networks
  • Transformer Architecture
  • Prompt Engineering Techniques and System Prompts
  • Foundational model and fine-tuned model
  • Retrieval Augmented Generation (RAG)
  • Hands On: Building a Gen AI-Based Custom Chatbot
  • Hands On: Using Gen AI for Summarizing Threat Intelligence and Vulnerability Reports
• Module 8: SIEM-IDS-Integration, Deployment, and Monitoring of AI Models
  • Ingesting IDS Logs into SIEM (ELK Stack)
  • Data Parsing using AI
  • Setting up the Data and Model Pipelines
  • Hands On: FastAPI for Deploying and Serving Models
  • Monitoring and Observability
  • Dealing with Data Lag and Model Drift
  • Secure Retraining Mechanisms
  • Hands On: AI Observability with OpenLLMetry + Phoenix
• Module 9: Agentic AI for Security Operations
  • Components To Build Agentic AI
  • Agentic AI: Autonomous agents, Reasoning, Action: tool use, and multi-agent orchestration frameworks
  • Model Context Protocol (MCP): architecture, use cases, and security implications
  • Demo: Using Agentic AI for generating Triage Reports
  • Agent topology design principles.

Part 2: Attacking, Governing, and Securing AI

• Module 10: Adversarial Attacks on AI
  • Poisoning: Data and Model
  • Setting up Backdoor with Trigger-Based Poisoning
  • Evasion
  • Evading AI-Based Detectors
  • Theft: Data and Model
  • Inversion and Inference Attacks
  • OWASP Machine Learning Security Top 10
  • OWASP Top 10 for Large Language Model Applications
  • OWASP Top 10 for Agentic Applications
  • Hands On: Honeytokens in prompts and vector stores
  • Hands On: Automated LLM pentesting using Garak
• Module 11: Security Architecture Principles Applied to AI
  • Secure Infrastructure reference architecture for AI Workloads
  • Defense-in-depth for AI pipelines
  • Zero-trust architecture for AI services
  • Role of AI Gateway
  • Segmentation of data, model, and inference layers
  • Identity and Access Control for AI Systems
  • Hands On: Deploying AI Gateway
  • Hands On: Shadow AI Discovery using Proxy Server
• Module 12: Data Security in AI Systems
  • Data Confidentiality, Integrity, and Availability
  • The AI data leakage threat surface
  • Secure Data Collection and Ingestion
  • Data Sanitization and De-identification Techniques: Data Anonymization, Pseudo-anonymization, Minimization, Masking, Redaction
  • Secure Data Processing: Data Classification, Data Quality, and Validation
  • Dataset Watermarking
  • Privacy Preserving Learning Techniques
  • Secure Data Usage During Inference
  • Dealing with Data Lag after Model Deployment
  • Data Lineage and Provenance Tracking: Audit Logging, Versioning
  • Hands On: Using Microsoft Presidio
• Module 13: AI Governance
  • AI Ethics
  • Properties of a Responsible AI System
  • Data Governance
  • Model Governance
  • Mapping Infosec controls to NIST AI RMF functions (Govern, Map, Measure, Manage) and ISO 42001 Annex A controls
  • EU AI Act risk tiers and their implications for security controls.
  • Hands On: Governance control mapping exercise
• Module 14: Implementing Governance and Security Controls for AI
  • Adversarial Training and Testing
  • Techniques to check Sampling and Algorithmic Bias
  • Ensuring Explainability in AI Systems
  • Data and Model Versioning
  • Guardrails for LLMs: System Prompt Hardening, Prompt Firewalls (Input and Output Guards)
  • Integrating Prompt Injection Detector with LLMs
  • Proprietary and open-source guardrails: LLM-Guard, Nemo Guardrails, Guardrails-AI, LlamaGuard
  • Hands On: Deploying a pre-built Prompt Injection Detector and interpreting its alerts
  • Applying Rate Limiting and Cost Budgeting in the AI Gateway
  • Observability, Monitoring, Evaluation, and Logging Tools for LLMs
  • Hands On: Third-party AI Component Supply Chain Assessment
  • Building a Governance Evidence Pack for an AI Application
  • Threat Modelling Frameworks: MITRE ATLAS, MAESTRO Framework for Agentic AI systems
  • Security Roles, Responsibilities & Operating Model for AI

• Beginners in cybersecurity, with coding supported by Generative AI tools
• 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 are built as security controls
• Anyone who wants to transition to AI security roles
• Cybersecurity Professionals – Security Analysts, SOC Analysts, Security Engineers, Detection Engineers
• Offensive Security Professionals

Fundamental knowledge of core cybersecurity concepts is required.

You will be able to:

• Bridge the gap between traditional cybersecurity and modern AI systems security
• Understand how AI systems work under the hood beyond theoretical concepts
• Apply AI for both offensive and defensive cybersecurity use cases
• Secure AI systems across the full lifecycle: data, model, deployment, and operations
• Gain exposure to real-world attack techniques and defensive security controls
• Bridge the gap between traditional cybersecurity and modern AI systems security
• Bridge the gap between traditional cybersecurity and modern AI systems security