AI Risk Management: Guardrails for Real-World AI
This Course Includes:
What you'll learn
How to apply the CIA triad to AI systems to manage confidentiality, integrity, and availability risks
Practical controls to prevent data leaks, hallucinations, bias, and deepfake-related risks
How to assess AI vendors, third-party dependencies, and supply chain resilience
The differences between generative and agentic AI and how to safely grant autonomy
How to design human-in-the-loop workflows, rollback plans, and escalation paths
How to build transparency, accountability, and trust into AI systems at scale
What's in this course?
This course shows you how to control real AI risks using practical, decision-ready guidance. You will learn how to apply the CIA triad to AI workflows, map risks across prompts, logs, datasets, embeddings, tools, and APIs, and set measurable controls that keep data private, outputs checkable, and systems online. The material uses people, process, and technology patterns to place the right guardrails in the right place at the right time.
By the end, you will know how to prevent leaks, reduce hallucinations, detect deepfakes, address bias, and maintain information integrity separate from data security. You will also design human-in-the-loop checkpoints, rollback plans, and escalation paths, plus operational playbooks for outages, vendor changes, and agent behavior. Expect clear ownership models, audit-ready logging, and metrics that show risk, exposure, value, and time to mitigation.
You will work with modern AI building blocks such as ChatGPT or Claude, retrieval augmented generation with citations, vector databases for grounding, DLP and access controls, SBOMs for your stack, and MCP or API-based tool integrations. You will create checklists, approval ladders, fallback modes, and vendor review templates that you can use immediately in your organization.
Who This Course is For
Business and functional leaders who need clear guardrails to scale AI without increasing risk
Product and engineering managers deploying generative or agentic AI in customer-facing or internal workflows
Security, privacy, compliance, and legal teams aligning controls across data use, logging, and oversight
Technical teams building RAG, tool-using agents, or integrations that require monitoring, rollback, and audit trails
What's included in AI Risk Management: Guardrails for Real-World AI ?
Lifetime access to all lessons
Downloadable checklists and templates for CIA controls, data classification, vendor reviews, and agent guardrails
Step-by-step walkthroughs for human-in-the-loop, rollback, and escalation design
Prompt and policy patterns for cite-or-decline and grounded outputs
Production-ready playbooks for dependency mapping, failover, and chaos drills
Certificate of completion
Requirements
Basic familiarity with AI concepts such as prompts, models, or automation is helpful but not required
Access to an AI tool such as ChatGPT or Claude for practice
Willingness to test prompts, review outputs, and apply controls in your environment