Overview
In today’s digital age, the use of Artificial Intelligence (AI) has proliferated across various industries, from healthcare and finance to transportation and e-commerce. However, with the increased adoption of AI, there has also been a rise in AI-targeted attacks. According to a report by Gartner, by 2025, 50% of organizations will have suffered from at least one AI-related security incident. These attacks can range from data poisoning and model inversion to adversarial examples and model extraction. It’s crucial to understand these exploits to help companies secure their AI assets effectively.
The sophistication of AI-driven threats is escalating at an alarming rate, posing a significant risk of substantial damage. Consider this: a nefarious attacker could exploit an AI model to craft adversarial exploits, cunningly designed to slip past a company’s AI-fortified security defenses. In a similar vein, an attacker could deploy a model extraction attack, effectively pilfering a company’s proprietary AI models to use them for malevolent purposes.
The repercussions of failing to safeguard AI from such exploitation or attacks are grave. Companies stand to incur hefty financial losses, suffer damage to their reputation, and potentially face legal liabilities. Furthermore, incidents related to AI security can undermine public trust in AI technologies, thereby impeding their adoption and advancement.
The Certified DefenAI Professional course is designed to equip professionals with the knowledge and skills to identify and mitigate the risks associated with AI exploitation and adversarial AI attacks. This course will delve into the world of AI exploitation, exploring the techniques and tools used to compromise AI systems, as well as the strategies and best practices for protecting AI modules from attacks by other AI modules.
Skills Covered
The objective of this course is to empower professionals with the requisite knowledge and skills to safeguard AI modules from attacks by Cybercriminals or other AI modules. This course delves deep into the complex world of AI-driven threats, providing a comprehensive understanding of the techniques and strategies used to counteract them.
The primary objective of this course is to equip learners with a deep understanding of Adversarial AI and its different techniques and how they can leverage AI to protect AI models. This includes learning how to:
- Understand the concepts and techniques used to exploit AI modules, including adversarial attacks, data poisoning, and model inversion attacks, with permission from the system owners.
- Identify potential vulnerabilities in AI-powered systems and develop strategies to prevent exploitation by malicious actors.
- Implement effective defence mechanisms to protect AI modules from attacks launched by other AI systems.
- Develop a comprehensive understanding of the AI security landscape, including the latest
Prerequisites
- Students enrolling in this course should have a basic understanding of cybersecurity principles and AI concepts. Familiarity with machine learning algorithms and Python will be beneficial but not mandatory.
Target Audience
- Data Science Analysts / Profesisonals
- AI Engineers
- AI Developers (LLM, GenAI, etc)
- AI Architects
- AI designer
- AI ethics specialists
- Pentesters
- Security Analysts
- Bug Bountry Hunters
- Security Consultants
- Blue Team members, Defenders, and Forensic Analyst
Module 1: Introduction to AI and Machine Learning
- Overview of AI and Machine Learning Concepts
- Types of AI Models and Architectures
- AI Development Lifecycle and Workflows
- AI Ethics and Responsible AI Principles
Module 2: Introduction to AI Security and Attack Vectors
- Overview of AI Security Landscape
- Common Attack Vectors on AI Models
- Threat Modelling for AI Systems
- AI Security Best Practices and Frameworks
Module 3: Attacks on AI Models and Data Sources
- Attacks on Large Language Models (LLMs)
- Attacks on Deep Learning Models
- Attacks on Tree-Ensemble Models and Forecasting
- Data Poisoning and Manipulation Attacks
Module 4: Attacks on AI Infrastructure, APIs, and Jailbreaking LLMs
- Reconnaissance and Vulnerability Scanning
- Exploiting Vulnerabilities in AI Infrastructure
- Attacks on AI APIs and Interfaces
- Jailbreaking LLMs and Diffusion Models
Module 5: Advanced AI Attack Techniques and Defenses
- Membership Inference Attacks
- Model Inversion and Extraction Attacks
- Adversarial Defenses and Robustness
- Course Recap
- Assessment
Exam & Certification
Cybertronium Certified DefenAI Professional Exam.
Training & Certification Guide
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