Introduction to Agentic AI:

1. Definition and Core Concepts – Understanding Agentic AI as autonomous systems capable of decision-making and task execution without human intervention. – Differentiating Agentic AI from traditional AI models.
2. Historical Context and Evolution – Tracing the progression from traditional AI to Agentic AI. – Key milestones and technological advancements leading to autonomous AI agents.
3. Key Characteristics of Agentic AI – Autonomy in decision-making and action. – Continuous learning and adaptation to dynamic environments. – Integration of AI techniques such as natural language processing and machine learning.

Agentic AI vs. Traditional AI Solutions:

1. Traditional AI Solutions – Rule-based systems with predefined responses. – Dependence on human input for decision-making processes.
2. Agentic AI Solutions – Proactive and goal-oriented behavior. – Ability to operate independently in complex and unstructured environments.
3. Comparative Analysis – Flexibility and scalability of Agentic AI compared to the rigidity of traditional AI. – Enhanced efficiency and effectiveness in handling complex tasks.

III. Applications of Agentic AI in Cybersecurity:

1. Enhancing Security Operations – Automated threat detection and response systems. – Real-time analysis of network traffic to identify anomalies. – Streamlining alert enrichment and data contextualization.
2. Advancements in Offensive Security – Utilizing Agentic AI for automated vulnerability assessments. – Simulating sophisticated attack scenarios to test system defenses. – Continuous adaptation of attack strategies based on system responses.
3. Case Studies and Real-World Implementations – Review of organizations successfully integrating Agentic AI into their security frameworks. – Lessons learned and best practices from these implementations.

IV. Hands-On Workshop: Implementing Agentic AI with n8n and Other Platforms:

1. Introduction to Agentic AI Platforms – Overview of n8n as a workflow automation tool. – Introduction to other platforms such as Beam AI, AutoGen, and AutoGPT
2. Setting Up the Environment – Guided setup of n8n and other selected platforms for Agentic AI applications.
3. Building Autonomous Workflows – Creating workflows that simulate autonomous threat detection and response. – Integrating external data sources for real-time decision-making.
4. Practical Exercises – Participants will design and implement workflows that autonomously identify and mitigate security threats. – Testing and refining workflows to enhance efficiency and accuracy.
5. E. Discussion and Q&A – Addressing challenges faced during the exercises. – Exploring potential applications and extensions of the developed workflows.

Conclusion and Future Directions:

1. Recap of Key Takeaways – Summarizing the benefits and applications of Agentic AI in cybersecurity.
2. Emerging Trends and Future Outlook – Discussing the evolving landscape of Agentic AI technologies. – Potential future applications and research areas.
3. Open Discussion – Encouraging participants to share insights, experiences, and questions.

This comprehensive training session aims to equip cybersecurity professionals with a thorough understanding of Agentic AI, its distinctions from traditional AI, and practical skills to implement autonomous workflows using platforms like n8n and others.