Overview

This program equips cybersecurity professionals, AI security practitioners, SOC leaders, and governance specialists with the expertise required to integrate Artificial Intelligence and Generative AI into security operations responsibly and securely. You will begin by exploring AI fundamentals, comparing traditional detection approaches with AI-driven analytics, and understanding how Large Language Models enhance SOC workflows. Through guided demonstrations, you will examine real-world applications such as AI-based malware detection, automated triage, and intelligent threat analysis. Building on AI foundations, you will explore transformer architectures, evaluate LLM capabilities and limitations, and apply AI systems to cybersecurity use cases. Emphasis is placed on identifying output risks, implementing guardrails, and maintaining human oversight in AI-assisted workflows. Next, the program advances into secure prompt engineering and AI system defense. You will learn how prompt injection attacks occur, how adversarial machine learning manipulates models, and how AI pipelines can be hardened against misuse. Structured exercises demonstrate how robust model training, monitoring, and validation reduce AI-specific security risks. The course then expands into governance, ethics, and compliance frameworks. You will analyze bias, fairness, transparency, and accountability challenges in AI systems, and align AI deployment with recognized standards such as NIST and regulatory compliance frameworks. Practical examples demonstrate how to audit AI systems and establish responsible oversight mechanisms. Finally, you will integrate AI security, adversarial defense, and governance strategies in a structured practice project, designing a secure AI-enabled SOC framework aligned with enterprise risk management principles. By the end of this program, you will be able to: -Explain AI, GenAI, and LLM concepts in cybersecurity contexts. -Apply AI and LLMs to enhance SOC detection and triage workflows. -Design secure prompt engineering and guardrail controls. -Identify vulnerabilities across AI pipelines and system architectures. -Defend against adversarial machine learning attacks. -Implement ethical, transparent, and compliant AI governance frameworks. -Audit AI-assisted decisions for bias, risk, and misuse. -Design a secure AI-driven security operations strategy. This course is designed for SOC professionals, cybersecurity engineers, AI security practitioners, governance officers, and security leaders seeking to responsibly integrate AI into enterprise defense strategies. Join us to build the technical insight, defensive resilience, and governance expertise required to secure AI-powered cybersecurity operations in modern enterprises.

Syllabus

Artificial Intelligence and Large Language Models in Cybersecurity

Understand how artificial intelligence, generative AI, and large language models are reshaping modern cybersecurity operations. Learn how AI-driven systems enhance traditional security controls, improve threat detection accuracy, and accelerate SOC workflows. Explore real-world applications of AI in malware detection, password security, and threat analysis, while examining the core architectures behind generative AI systems, including transformers, GANs, VAEs, and LLMs.

Prompt Engineering and AI System Security

Develop a strong foundation in prompt engineering and AI system security to ensure safe and reliable use of large language models in cybersecurity environments.Explore AI system architectures to understand security vulnerabilities across data pipelines, model training, and deployment layers. Gain practical insight into adversarial machine learning attacks and defensive strategies, while learning to critically evaluate AI and LLM outputs for security risks, reliability issues, and potential misuse in real-world operations.

Advanced Security, Ethics, and Governance for Generative AI

Secure enterprise environments by implementing AI-aware operating system and network defense mechanisms. Learn how to protect AI-enabled systems by hardening configurations, enforcing access controls, and monitoring AI-driven workloads for misuse and anomalous behavior. Design and secure infrastructures that support AI applications using layered defenses, continuous monitoring, and traffic analysis. Gain hands-on experience evaluating AI system interactions and operational telemetry to ensure integrity, visibility, and rapid detection of security risks across modern enterprise environments.