Design, deploy, and govern secure, compliant AI systems used in real enterprises
Sub Category
- Data Science
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Objectives
- 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.
Pre Requisites
- 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
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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