Artificial Intelligence is rapidly transforming modern society, powering applications in healthcare, finance, smart cities, autonomous systems, cybersecurity, and generative AI platforms. However, as AI adoption increases, concerns related to security vulnerabilities, ethical risks, privacy violations, bias, and regulatory compliance have become critically important.
This comprehensive Udemy course, “Ethical and Regulation for Artificial Intelligence Security,” is designed to provide learners with a complete understanding of how to build, deploy, and govern secure, trustworthy, and regulation-compliant AI systems. This course contains the use of artificial intelligence.
The course uniquely integrates AI Security, Responsible AI Principles, Global Regulatory Frameworks, and Hands-on Python-based Implementations, enabling learners to move beyond theoretical understanding toward practical real-world AI governance and protection strategies.
What You Will Learn:
- Foundations of secure and ethical Artificial Intelligence
- Adversarial threats targeting Machine Learning and LLM systems
- End-to-end Machine Learning Security (MLSecOps) practices
- Responsible AI principles including fairness, transparency, and accountability
- Global AI regulations and compliance frameworks
- Bias detection, mitigation, and explainable AI techniques
- Practical simulations of AI attacks and defensive mechanisms using Python
- Secure deployment and monitoring of AI systems
Section 1: Foundations of Secure and Ethical Artificial Intelligence
This section introduces the fundamental relationship between AI Security, Ethics, and Regulation, forming the backbone of trustworthy AI systems. Learners will explore the AI lifecycle and associated risks while understanding core ethical principles such as *Privacy, Fairness, Accountability, and Transparency (FAT)**.
You will examine modern adversarial threats including:
- Evasion attacks
- Poisoning attacks
- Inference attacks
- Model extraction attacks
- LLM and Transformer vulnerabilities
- Prompt injection and jailbreaking
- Retrieval-Augmented Generation (RAG) security risks
