Security for AI: Protecting Workloads, Agents, and Data

As organizations rapidly adopt artificial intelligence, securing AI models, data pipelines, and intelligent agents has become a critical priority. Protecting these systems requires a defense-in-depth strategy that spans identity, data governance, and real-time threat protection. This course guides you through the foundational concepts of AI security. You will understand how to safeguard sensitive training data, secure autonomous agent identities, and defend against emerging threats like prompt injection and data exfiltration. What you'll learn: - Understand core AI security principles, including zero-trust architecture and the OWASP Top 10 vulnerabilities for LLMs. - Configure data security posture management to protect sensitive information using Purview. - Manage and secure intelligent agent identities and access permissions within Entra. - Analyze and mitigate AI-specific identity risks using Defender XDR and Defender for Cloud. - Implement real-time guardrails and secure AI Gateway configurations to block malicious inputs. - Establish robust governance frameworks to monitor and audit autonomous AI agents. You will start with essential definitions of AI risk vectors before moving systematically through data security, identity management, gateway protection, and active threat monitoring. Each module provides clear, text-based explanations and conceptual walkthroughs to help you apply these security controls in your own environment. This course is designed for IT professionals, security analysts, and system administrators who are new to AI security and want to establish a strong foundational knowledge without needing advanced programming experience. Begin reading today to build a secure, resilient foundation for your organization's AI initiatives.