AI Ethics and Safety: Building Secure Generative AI Applications

As generative AI and large language models become central to modern software, building applications that are safe, ethical, and secure is no longer optional. Developers must proactively address challenges like bias, hallucination, data privacy, and adversarial exploits. This written course equips you with the foundational knowledge and practical strategies needed to design and deploy responsible AI applications. You will understand the core principles of AI alignment and learn how to implement robust safety measures directly into your software development lifecycle. What you'll learn: Understand foundational AI ethics principles, including fairness, transparency, accountability, and alignment; Identify and mitigate common vulnerabilities like prompt injection, jailbreaking, and data poisoning; Implement modern guardrail frameworks and system instructions to control model outputs; Analyze data privacy requirements and establish secure data-handling practices; Evaluate model bias and toxicity using standard testing methodologies; Configure monitoring systems to detect and respond to unsafe interactions in production. The course begins with essential terminology and the philosophical foundations of AI safety before moving into practical mitigation techniques. You will progress through written explanations, conceptual breakdowns, and code-based implementation strategies for securing generative AI pipelines. This course is designed for software developers, technical product builders, and engineers who are new to AI safety and want to build responsible applications. No prior experience with ethics or advanced machine learning is required. Start reading today to build AI systems that users can trust.