AI Security, Governance & Compliance

Design, deploy, and govern secure, compliant AI systems used in real enterprises

Description

“This course contains the use of artificial intelligence”

AI systems are no longer experimental tools — they are production systems making real decisions at scale. As organizations deploy Generative AI, LLMs, RAG pipelines, and autonomous agents, the biggest challenges are no longer accuracy or performance, but security, governance, privacy, and regulatory compliance.

This course is a practical, enterprise-focused guide to building secure, compliant, and trustworthy AI systems that can operate safely in real-world environments. You’ll learn why AI security is fundamentally different from traditional application security, how AI systems fail in production, and what organizations must do to manage risk, accountability, and oversight across the AI lifecycle.

Rather than abstract ethics or policy theory, this course focuses on how AI is actually governed inside enterprises today. You’ll understand AI threat modeling, prompt injection and data leakage risks, guardrails and safety layers, and how to design human-in-the-loop controls that scale. The course also demystifies AI governance frameworks, showing how teams define ownership, approvals, documentation, and decision rights without slowing innovation.

You’ll gain a clear understanding of the global AI regulatory landscape, including EU AI Act principles, US governance approaches, and industry standards, and learn how these translate into real controls, audits, and evidence. Privacy, consent, data retention, and cross-border data handling are addressed from a practical, audit-ready perspective — not legal jargon.

Through realistic enterprise case studies and hands-on design exercises, you’ll learn how to secure internal AI assistants, customer-facing GenAI applications, and autonomous operational agents, including how to handle failures, design kill-switches, and implement safe rollback strategies.

By the end of this course, you’ll be able to design AI systems that pass audits, survive incidents, and earn trust — and confidently speak the language of AI security, governance, and compliance in technical, product, and leadership settings.

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