Mastering AI Security Boot Camp (TTAI2820)

Course Objectives

Learning Objectives

Throughout the course you’ll:

• Gain a clear understanding of AI and its integral role in the realm of cybersecurity, providing a solid foundation for the rest of the course.
• Learn to identify and understand various types of AI threats and vulnerabilities, improving your ability to predict and mitigate potential risks.
• Acquire the knowledge to design and implement robust AI defense mechanisms and AI Driven Intrusion Systems (IDS), equipping you to safeguard your systems effectively.
• Delve into the fascinating world of AI forensics and learn how to conduct basic forensic analyses on AI systems.
• Master the art of creating and executing incident response plans for AI systems, a vital skill for any security professional.
• Learn specific techniques to detect deepfakes and understand their potential security implications, equipping you to counter one of the emerging threats in the AI security landscape.
• Get hands-on experience with innovative open-source tools such as Python, Scikit-learn, and Suricata IDS, enhancing your ability to use these tools effectively in AI security.
• Get insights into future trends in AI security, ensuring that you're well-prepared for what's around the corner in this rapidly evolving field.

Course Prerequisites

Audience

This intermediate-level course is a fit for experienced cybersecurity professionals, system administrators, developers and IT managers seeking to enhance their understanding of artificial intelligence in the context of security. Individuals in roles responsible for threat analysis, incident response, and system defense will find the course particularly beneficial.

Pre-Requisites

To ensure a smooth learning experience and maximize the benefits of attending this course, you should have the following prerequisite skills:

• A foundational understanding of artificial intelligence, including the basic principles, applications, and types of AI.
• Familiarity with basic cybersecurity principles, understanding of threats, defense mechanisms, and incident response.
• Basic Python programming skills and / or a general comfort with coding
• Basic knowledge of computer networks, systems, and how they interact
• Some basic experience in data analysis or basic statistical concepts.

Take Before: Students should have incoming practical skills aligned with those in the course(s) below, or should have attended the following course(s) as a pre-requisite:

• TTML5502    Exploring AI & Machine Learning Overview / Hands-On (2 days)
• TTPS4800      Introduction to Python Programming Basics (3 days) (Helpful but not required)

Course Agenda

1. Introduction to AI in Security

• Understand the role of AI in the field of cybersecurity and the evolution of threats.
• The basics of AI and its relevance to security
• Cybersecurity landscape: traditional threats vs. AI-enabled threats
• Real world examples of AI in security
• Understanding the role of AI in Threat Intelligence
• Lab: Simulating AI-driven threat analysis using open-source threat intelligence tools

2. Playing Detective: Identifying AI Threats and Vulnerabilities

• Grasp the inherent threats and vulnerabilities of AI systems
• Understanding the different types of AI threats
• Learning about common AI vulnerabilities
• Exploring case studies of major AI-based security breaches
• AI and data privacy concerns
• Lab: Identifying vulnerabilities in an AI system (2:30 - 4:00)
• Tools Used in Lab: Python, Scikit-learn, OWASP Dependency-Check

3. Building the AI Fortress: Defense Mechanisms 101

• Gain knowledge on how to safeguard AI systems from security threats.
• Importance of AI Security Measures
• Learning about AI Defense Mechanisms
• AI in intrusion detection and prevention systems • AI in risk assessment and vulnerability management
• Lab: Designing a basic AI-driven Intrusion Detection System

4. CSI Cyber: A Foray into AI Forensics

• Understand how forensic techniques are applied in AI security.
• The role of forensics in AI Security
• Basics of AI Forensic Analysis
• Case studies of forensic analysis in AI security incidents
• AI in forensic data analysis
• Lab: Conducting a simple forensic analysis on an AI system

5. Crisis Averted: Crafting Your AI Incident Response Plan

• Learn how to respond to incidents in AI systems effectively.
• Basics of Incident Response (IR) in AI systems
• AI in IR: Automated and adaptive response
• Designing an incident response plan for AI systems
• Lab: Creating a mock incident response plan for an AI system

6. What's Next? Preparing for Future AI Security Challenges

• Get insights into the future trends of AI in cybersecurity.
• Future threats: Deepfakes, autonomous weapons, etc.
• AI in quantum computing security
• AI-driven Security Orchestration, Automation, and Response (SOAR)
• The role of AI in zero-trust architectures
• Lab: Simulating the detection of a deepfake

Course Wrap

• Next steps in becoming an AI Security Expert

Course Materials

Setup Made Simple! Learning Experience Platform (LXP) 

All applicable course software, digital courseware files or course notes, labs, data sets and solutions, live coaching support channels and rich extended learning and post training resources are provided for you in our “easy access, no install required” online Learning Experience Platform (LXP), remote lab and content environment. Access periods vary by course. We’ll collaborate with you to ensure your team is set up and ready to go well in advance of the class. Please inquire about set up details and options for your specific course of interest.