Overview What You Will Learn Curriculum Instructor

Course Overview

Artificial intelligence and large language models are transforming cybersecurity by enabling faster threat detection, automated analysis, intelligent decision-making, and adaptive security operations. This course explores how AI agents and LLMs can enhance cybersecurity capabilities across detection, analysis, response, and security management.

This course begins with the foundations of AI agents, LLM architectures, agent workflows, autonomy levels, multi-agent systems, memory, fine-tuning, prompt engineering, and cybersecurity datasets. It then covers AI-driven threat detection, vulnerability analysis, malware classification, intrusion detection, phishing defense, threat intelligence, log analysis, reverse engineering, incident response, patch generation, SOC automation, compliance, and adaptive decision-making. The course also examines AI-specific threats, including prompt injection, jailbreaking, data poisoning, supply-chain risks, bias, governance, human-in-the-loop approaches, multimodal AI, and proactive LLM defense.

By completing this course, you will gain a practical understanding of how AI agents can strengthen cybersecurity operations while addressing security, governance, ethical, and emerging risks.

What You Will Learn

  • Positioning yourself at the forefront of cybersecurity's AI revolution by understanding the technologies that will define the future of security operations and threat response.
  • Developing the unique combination of AI understanding and cybersecurity expertise that organizations desperately need as they navigate digital transformation and increasing threat sophistication.
  • Gaining the knowledge to evaluate AI agent solutions, understand their limitations, and make strategic decisions about implementation in your security infrastructure.
  • Building the foundation for working alongside AI agents as collaborative partners, understanding how to leverage their capabilities while maintaining human oversight and control.
  • Understand how AI agents can help you anticipate and prepare for next-generation cyber-attacks, including AI-powered threats and novel attack vectors.
  • Understanding the transformative potential of AI agents in addressing the scale and complexity of modern cyber threats.
  • Gaining practical knowledge of real-world AI agent implementations in threat detection, vulnerability management, and incident response.
  • Learning to leverage AI agents for enhanced SOC operations while maintaining human oversight and decision-making authority.
  • Building the foundation for making informed decisions about AI agent adoption and deployment in cybersecurity environments.

Program Curriculum

  • Course Introduction
  • Module Introduction
  • The Transformative Potential of AI and LLMs in Cybersecurity
  • Defining Large Language Models (LLMs) and Their Architectures
  • Evolution of AI in Cybersecurity: From ML to Agentic AI
  • What Are AI Agents? Definition, Characteristics, and Workflow
  • LLMs as the “Brain” of AI Agents: Capabilities and Limitations
  • Agent Autonomy Levels in Cybersecurity
  • Multi-agent Systems: Collaboration and Complexity
  • Memory and Learning in AI Agents
  • Adapting LLMs for Cybersecurity: Fine-tuning, Prompt Engineering, and Augmentation
  • Types of Datasets in LLM for Security
  • Data Preprocessing and Representation for Cybersecurity AI Models
  • Addressing Data Scarcity: LLMs for Data Augmentation
  • Chapter 1 Quiz

  • Module Introduction
  • Real-time Detection of Cyber Threats with AI
  • Automated Vulnerability Detection and Analysis
  • Malware Analysis and Classification with AI Agents
  • Network Intrusion Detection and Attack Classification
  • Detecting and Defending Against Phishing Attacks and Deceptive Language
  • Leveraging AI for Threat Intelligence and Attack Surface Management
  • AI for System Log Analysis and Anomaly Detection
  • Reverse Engineering and Binary Analysis with AI Assistance
  • AI for Understanding Security and Privacy Policies
  • Chapter 2 Quiz

  • Module Introduction
  • Automating Vulnerability Repair and Patch Generation
  • Streamlining Incident Response Workflows and Playbooks
  • Post-attack Analysis and Root Cause Identification with AI
  • The Transformative Role of Agentic AI in SOCs
  • Human-AI Co-teaming and Augmented SOC Capabilities
  • Optimizing Cybersecurity Investments and Compliance Automation
  • Adaptive Decision Making and Continuous Learning in SOC Agents
  • Chapter 3 Quiz

  • Module Introduction
  • Overview of AI Agent Security Challenges: The Four Knowledge Gaps
  • Inherent AI-related Vulnerabilities: Adversarial AI, Data Poisoning, and Misalignment
  • Agent-specific Threats: Prompt Injection, Jailbreaking, Supply Chain Vulnerabilities
  • Challenges in LLM Interpretability, Trustworthiness, and Ethical Usage
  • Addressing Bias and Fairness in AI Agents
  • Designing Responsible AI: Governance Models and Human-in-the-Loop
  • Expanding LLM Capabilities and Multimodal AI
  • Security for LLMs and Proactive Self-defense
  • The Roadmap for AI Agents in Cybersecurity
  • Course Wrap-up Video
  • Chapter 4 Quiz
Load more modules

Instructor

Team

Starweaver delivers 10x better-trained employees and students through scalable, activity-based online learning combined with live human-to-human instruction. With 70–85% course completion rates, we go beyond passive content libraries by focusing on real skill-building and professional competency. Our mission is to transform technologists into world-class experts and business professionals into tech-savvy leaders. Starweaver connects learners with a global network of live instructors and peers, driving higher engagement, satisfaction, and achievement. Our proprietary tools blend guided self-learning with real-time collaboration, ensuring learners stay motivated, capable, and truly job-ready.

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