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

What you'll learn
Design secure and compliant AI systems by identifying AI-specific risks, threat models, and failure modes across the full AI lifecycle.
Apply AI governance frameworks in real enterprise environments, including defining ownership, approval workflows, documentation standards, and operating models
Secure Generative AI and LLM-based applications using guardrails, prompt isolation, retrieval validation, and human-in-the-loop controls.
Prepare AI systems for audits and regulatory review by producing audit-ready evidence, traceability, and documentation aligned with global regulations.
Manage privacy, consent, and data protection risks in AI systems, including PII handling, data retention, and cross-border data considerations.
Respond effectively to AI incidents and failures, including hallucinations, abuse, security breaches, and autonomous agent failures.
Evaluate and mitigate risks in autonomous and agentic AI systems, including kill-switch design, rollback strategies, and operational safeguards.
Communicate AI risk, governance, and compliance decisions confidently to technical teams, auditors, regulators, and leadership.

Requirements
A basic understanding of AI or machine learning concepts (such as models, prompts, or data)
Some familiarity with software systems, products, or operations in a professional environment
An interest in deploying AI in real-world or enterprise settings

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.

Who this course is for
AI Engineers, ML Engineers, and GenAI Developers who want to understand the security, governance, and risk implications of production AI systems.
Security, Risk, and GRC professionals who need practical insight into how AI systems introduce new threat models and compliance challenges.
AI Product Managers and Technical Product Owners responsible for launching AI-powered features safely and responsibly.
Platform, MLOps, and Infrastructure Engineers supporting AI systems in production.
Architects and Technical Leaders designing enterprise AI platforms and operating models.
Professionals working with LLMs, RAG systems, or autonomous agents who want to avoid common failures and audit issues