This module introduces the core principles of cybersecurity, covering the threat landscape, essential terminologies, IT and networking essentials, ethical guidelines, and security controls, with hands-on labs to reinforce foundational concepts.

This module builds foundational knowledge in artificial intelligence, covering the principles of machine learning and deep learning, supported by practical lab sessions to apply these concepts in real-world security contexts.

This module explores the application of generative AI in cybersecurity, examining earlier GenAI methods, vision-based generative models such as diffusion models, and vision-language models, with lab sessions for hands-on experimentation.

This module addresses enterprise risk management and threat modeling, covering business continuity planning, risk identification, assessment, response, and monitoring, alongside structured approaches to threat modeling processes.

This module provides a comprehensive grounding in cryptographic principles and systems, spanning the CIA triad, symmetric and asymmetric cryptography, key management, certificate authorities, hash functions, post-quantum algorithms, and specialized government cryptography organisations in India.

This module examines real-world cryptographic protocols and their vulnerabilities, including secure communication standards such as SSH, TLS, and VPN, mobile and IoT protocols, end-to-end encryption systems used in messaging apps, and security considerations in widely-used meeting platforms, with lab exercises.

This module covers advanced cryptographic applications and the regulatory landscape governing them, including blockchain technology, differential privacy, federated learning, and key international standards bodies and frameworks such as NIST, ISP, and IRTF.

This module equips learners to operate AI-enhanced security operations centres, covering the SOC analyst role, AI-integrated SIEM and SOAR platforms, monitoring and tuning AI-generated alerts, anomaly and malware detection using AI models, and AI-powered endpoint and network intrusion detection, with practical labs.

This module focuses on building AI-driven threat intelligence capabilities, covering the cyber threat intelligence lifecycle, phishing analysis, NLP-based threat report analysis, and the preparation and labelling of security datasets for AI/ML model training, reinforced by hands-on labs.

This module addresses securing cloud environments using AI-driven techniques, covering cloud fundamentals, security principles, AI-driven misconfiguration detection, and automated cloud response and remediation, complemented by practical labs.

This module trains learners to automate and orchestrate security workflows, covering cybersecurity automation concepts, container orchestration and zero-trust implementation, SOAR tool usage, and Python scripting for automated incident response, with applied labs.

This module develops offensive and defensive security skills through ethical hacking fundamentals, phishing and social engineering analysis, AI-driven malware analysis, red team vs blue team exercises, adversarial AI attack generation and defense, and evaluation of AI cyber technologies, supported by comprehensive labs.

This module covers the fundamentals of penetration testing, including automated and AI-assisted approaches, as well as techniques for firewall evasion and securing web servers and web applications against vulnerabilities.

This module addresses the ethical and regulatory dimensions of AI in cybersecurity, covering bias, transparency, and privacy concerns, major compliance frameworks including GDPR, NIST, and HIPAA, and the design of security policies that incorporate AI governance principles.