This module explores essential security considerations and best practices for developing AI applications, with a focus on Amazon Bedrock Guardrails and secure coding patterns. Students learn about critical security concepts including prompt injection attacks, content filtering, PII protection, and how to implement robust safeguards using AWS tools like Bedrock Guardrails, Amazon CloudWatch monitoring, and Amazon Q Developer. Through hands-on demonstrations and practical examples, learners gain the skills to identify security vulnerabilities, implement protective measures, and develop AI applications that maintain data privacy while following industry best practices for secure AI development.

This advanced module explores cutting-edge AI development techniques and enterprise integration patterns, focusing on Model Context Protocol (MCP) servers, Amazon Kiro's spec-driven development, and AI agent orchestration with knowledge bases. Students learn how to implement real-time data integration through MCP, structure large-scale AI projects using spec-driven methodologies, and build sophisticated AI agents capable of complex decision-making and multi-step task execution. Through hands-on exposure to production deployment strategies and knowledge base implementation, learners gain practical skills for developing scalable, enterprise-grade AI applications while mastering best practices for AI system architecture and integration.