AI Security Course Syllabus

Overview: This course provides a comprehensive introduction to AI security, focusing on protecting AI systems from emerging threats and vulnerabilities. Designed for beginners with foundational IT knowledge, it covers core concepts in AI, neural networks, natural language processing, and secure deployment practices. The course spans approximately 18-20 hours of content, divided into six modules featuring quizzes, hands-on exercises, case studies, and peer-reviewed assignments. Participants will gain practical insights into securing AI systems, evaluating risks, and applying industry best practices—preparing them for roles in AI-focused cybersecurity.

Module 1: Foundations of Computing & Algorithms

Estimated time: 2 hours

• Review of computing fundamentals and algorithm design
• Discussion of best practices and industry standards in AI security
• Introduction to tools and frameworks used in AI development
• Case study analysis of real-world AI security challenges

Module 2: Neural Networks & Deep Learning

Estimated time: 4 hours

• Introduction to neural networks and deep learning concepts
• Understanding security risks in deep learning models
• Interactive lab: Building basic neural network solutions
• Assessment of model vulnerabilities and trustworthiness

Module 3: AI System Design & Architecture

Estimated time: 2 hours

• Principles of secure AI system design
• Industry standards for AI architecture and scalability
• Guided project work with instructor feedback
• Peer-reviewed assignment on secure design practices

Module 4: Natural Language Processing

Estimated time: 3 hours

• Key concepts in natural language processing (NLP)
• Security implications of NLP in AI systems
• Hands-on exercises applying NLP techniques securely
• Review of frameworks and tools for secure NLP deployment

Module 5: Computer Vision & Pattern Recognition

Estimated time: 3 hours

• Introduction to computer vision and pattern recognition
• Case study analysis of adversarial attacks on vision models
• Best practices for securing image-based AI systems
• Discussion of real-world examples and mitigation strategies

Module 6: Deployment & Production Systems

Estimated time: 4 hours

• Secure deployment of AI models in production environments
• Hands-on exercises on securing data pipelines and APIs
• Interactive lab: Building secure end-to-end AI systems
• Review of tools and frameworks for monitoring and protection

Prerequisites

• Basic understanding of cybersecurity or IT concepts
• Familiarity with fundamental computing and algorithms
• Interest in artificial intelligence and security applications

What You'll Be Able to Do After

• Identify and mitigate security risks in AI systems
• Apply best practices for securing neural networks and deep learning models
• Implement secure AI architectures and deployment strategies
• Evaluate vulnerabilities in natural language processing and computer vision systems
• Design robust, production-ready AI systems with built-in security measures