AI Application Security: Protecting Deployed Models

Deploying AI applications comes with unique security challenges that traditional methods often overlook. Understanding these risks and implementing robust protection measures is crucial for safeguarding your models and data. This course provides a foundational understanding of AI application security, equipping you with the knowledge to identify vulnerabilities, assess risks, and apply essential strategies to protect your AI systems from common threats. What you'll learn: Understand fundamental security concepts specific to AI and machine learning applications. Identify and analyze common security vulnerabilities and attack vectors against AI models, including large language models. Apply best practices for securing the entire AI lifecycle, from data input to model deployment. Implement strategies to protect against data poisoning, model evasion, and adversarial attacks. Learn about basic principles of secure prompt engineering to mitigate risks in large language models. Configure and maintain secure deployment environments for AI applications. The course begins with core AI security terminology and concepts, progressing through common threats and practical mitigation techniques. You'll then explore secure deployment strategies and essential practices for safeguarding large language models. This course is designed for beginners in AI development, MLOps, or cybersecurity who want to understand and implement foundational security measures for AI applications. No prior experience in AI security is required. Start building more secure and resilient AI systems today.