AI and LLMs for Security Professionals

This comprehensive training program is tailored to equip security professionals with the knowledge and skills to leverage AI and LLMs in enhancing cybersecurity, threat management, and operational efficiency. The course provides a deep dive into the latest advancements in AI, focusing on practical applications for security-related challenges.

Participants can expect hands-on exercises, real-world examples, and actionable insights to immediately apply AI in their security operations.

The following topics will be covered:

1. Understanding Models, Parameter Sizes, Fine-tunes, and Unlocked LLMs

Gain an in-depth understanding of various LLMs, their architectures, parameter sizes, and the impact on performance. Learn the nuances of fine-tuning models for specific security tasks and explore unlocked LLMs for tailored use cases.

2. Open-Source Models and Infrastructure for Running Models

Explore the landscape of open-source LLMs and tools, and learn how to set up the infrastructure necessary for running and deploying models securely within your organization.

3. Retrieval-Augmented Generation (RAGs) and Document Querying

Discover how to integrate RAGs into your workflow to enhance document querying capabilities, enabling real-time access to critical security information from large datasets and knowledge bases.

4. Infrastructure as Code and Running LLMs

Learn to implement Infrastructure as Code (IaC) to efficiently deploy and manage the systems required for LLMs. This includes provisioning resources, scaling deployments, and automating infrastructure updates.

5. AI-Managed Kubernetes, Certificate Authorities, Credential Rotation, and Database Management

Understand how AI tools can streamline Kubernetes management, automate certificate authority processes, and ensure secure credential rotation and database management to reduce human error and improve operational security.

6. AI-Assisted Development, Including Rewriting Legacy Code

Explore how LLMs can accelerate software development by generating code snippets, refactoring legacy code, and identifying vulnerabilities, thus enabling faster and more secure development cycles.

7. LLM-Assisted Binary Reverse Engineering (RE)

Learn how to utilize LLMs for binary reverse engineering tasks, helping to analyze malware, extract critical insights, and identify vulnerabilities in compiled code more efficiently.

8. LLMs for Threat Modeling

Discover how LLMs can support threat modeling by identifying potential attack vectors, assessing system vulnerabilities, and generating actionable mitigation strategies.

9. AI-Assisted Code Analysis

Utilize AI tools to perform in-depth code analysis, identifying vulnerabilities, improving code quality, and enhancing security review processes.

10. AI-Assisted Cryptographic Implementations

Learn to apply AI for cryptographic tasks, such as developing and analyzing secure algorithms, identifying potential weaknesses, and automating cryptographic processes.

11. LLMs and AI Agents

Delve into the use of autonomous AI agents powered by LLMs, capable of performing complex security operations, responding to incidents, and augmenting human decision-making.