- Promoted by: Anonymous
- Platform: Udemy
- Category: Other IT & Software
- Language: English
- Instructor: Andrii Piatakha
- Duration: 8 hour(s) 3 minute(s)
- Student(s): 3,493
- Rate 4.8 Of 5 From 2 Votes
- Expires on: 2026/01/24
-
Price:
14.990
Learn how to defend generative AI systems using firewalls, SPM, and data governance tools
Unlock your potential with a Free coupon code
for the "Securing AI Applications: From Threats to Controls" course by Andrii Piatakha on Udemy.
This course, boasting a 4.8-star rating from 2 reviews
and with 3,493 enrolled students, provides comprehensive training in Other IT & Software.
Spanning approximately
8 hour(s)
3 minute(s)
, this course is delivered in English
and we updated the information on January 22, 2026.
To get your free access, find the coupon code at the end of this article. Happy learning!
AI systems introduce security challenges that are fundamentally different from anything traditional cybersecurity was built to handle. LLM applications, retrieval pipelines, vector databases, and agent based automations create new vulnerabilities that can expose sensitive data, enable unauthorized actions, and compromise entire workflows. This course gives you a complete and practical framework for securing GenAI systems in real engineering environments.
You will learn how modern AI threats operate, how attackers exploit prompts, tools, and connectors, and how data can leak through embeddings, retrieval layers, or model outputs. The course walks you through every layer of the AI stack and shows you how to apply the right defenses at the right places, using a structured and repeatable security approach.
What you will learn
The full AI Security Reference Architecture across model, prompt, data, tools, and monitoring layers
How GenAI attacks work, including injection, leakage, misuse, and unsafe tool execution
How to use AI firewalls, filtering engines, and policy controls for runtime protection
AI SDLC best practices for dataset security, evaluations, red teaming, and version management
Data governance strategies for RAG pipelines, ACLs, encryption, filtering, and secure embeddings
Identity and access patterns that protect AI endpoints and tool integrations
AI Security Posture Management for risk scoring, drift detection, and policy enforcement
Observability and evaluation workflows that track model behavior and reliability
What is included
Architecture diagrams and control maps
Model and RAG threat modeling worksheets
Governance templates and security policies
Checklists for AI SDLC, RAG security, and data protection
Evaluation and firewall comparison frameworks
A complete AI security control stack
A step by step 30, 60, 90 day rollout plan for teams
Why this course is essential
It focuses on practical security for real AI deployments
It covers every critical layer of modern LLM and RAG systems
It delivers ready to use tools and artifacts, not theory
It prepares you for one of the fastest growing and most demanded areas in tech
If you need a structured and actionable guide to protecting AI systems from modern threats, this course provides everything required to secure, govern, and operate GenAI at scale with confidence.