Course
AI and Security
30 hours 47 minutes
Credits: Optional Learning
Description
In this course, learners will develop a cutting-edge understanding of artificial intelligence as both a tool for security and a source of emerging risk. Learners will explore AI auditing principles and frameworks, the role of data in risk assessment, and AI model evaluation in high-risk environments such as banking. They will then examine how generative AI and AI-powered tools are transforming enterprise security – covering identity security, email security, user data protection, authentication, intrusion detection, and threat mitigation. Learners will gain leadership-level awareness of AI-generated attacks including phishing and social engineering, and develop robust detection and defence strategies. The course concludes with a comprehensive exploration of prompt engineering for ethical hacking, applying generative AI across the full ethical hacking lifecycle including reconnaissance, scanning, enumeration, system hacking, malware, network and web application attacks, cloud and IoT hacking, mobile platform security, and covering tracks.
What Students Will Learn
- The Role of AI Auditing
- The Role of Data in AI Auditing
- Principles and Frameworks for AI Auditing
- AI Model Auditing and the Future
- AI Model Evaluation in High-Risk Banking
- AI Auditing: Data Fundamentals for Risk Assessment
- Final Exam: AI Auditing Essentials
- Enterprise Security: Artificial Intelligence, Generative AI, & Cybersecurity
- Enterprise Security: Leveraging Generative AI with Common Security Tools
- Enterprise Security: Leveraging AI in Identity Security
- Enterprise Security: Leveraging AI to Enhance Email Security
- Enterprise Security: Leveraging AI to Protect & Validate User Data
- Enterprise Security: AI in Authentication & Detection of Security Threats
- Enterprise Security: Using AI for Intrusion Detection & Prevention
- Final Exam: Enhancing Enterprise Security with Generative AI and AI
- Teach An AI to Phish
- Combating AI-Generated Attacks
- Types of AI Generated Attacks
- Robust Defenses
- Robust Detections
- Prompt Engineering: Ethical Hacking & Generative AI Fusion
- Prompt Engineering: Generative AI for Reconnaissance
- Prompt Engineering: Generative AI for Scanning & Enumeration
- Prompt Engineering: Generative AI for System Hacking
- Prompt Engineering: Generative AI for Malware & Social Engineering
- Prompt Engineering: GenAI's Impact on Network & Perimeter Ethical Hacking
- Prompt Engineering: Web Application & Database Hacking in the Age of GenAI
- Prompt Engineering: Cloud Computing & IoT Hacking in the Era of GenAI
- Prompt Engineering: Mobile Platform Security in the GenAI Era
- Prompt Engineering: Covering Tracks with GenAI
- Final Exam: Prompt Engineering for Ethical Hacking
Overall Learning Outcomes
- Explain the role of AI auditing, data fundamentals, and principles and frameworks used to assess AI model risk
- Evaluate AI models in high-risk environments and apply auditing processes to ensure accountability and compliance
- Apply generative AI and AI-powered tools to strengthen enterprise security across identity, email, and user data protection
- Leverage AI for authentication, threat detection, and intrusion detection and prevention in enterprise environments
- Identify and describe types of AI-generated attacks including phishing and social engineering tactics
- Develop and apply robust detection and defence strategies against AI-generated cybersecurity threats
- Apply prompt engineering techniques to support ethical hacking across reconnaissance, scanning, and enumeration phases
- Use generative AI to assist in system hacking, malware analysis, and social engineering assessments
- Apply generative AI to network, web application, database, cloud, IoT, and mobile platform ethical hacking scenarios
- Manage post-exploitation activities including covering tracks using generative AI tools and techniques

