Securing AI Applications: From Threats to Controls

Name: Securing AI Applications: From Threats to Controls
Availability: InStock
Rating: 4.90 (416 reviews)

4.90

4,164 students

8h 3m

Updated Feb 2026

What you'll learn

Examine how GenAI systems expand the attack surface across models, data, and tools

Use an end to end AI security architecture to map protections onto each subsystem

Develop comprehensive threat scenarios for LLM based applications and choose fitting safeguards

Deploy guardrail frameworks and policy engines to control user inputs and model outputs

Integrate security gates into AI delivery processes, covering data validation and model assessments

Set up authentication flows, permission boundaries, and controlled tool capabilities for AI services

Apply data protection practices to RAG pipelines, including filtering, encryption, and structured access

Operate AI SPM solutions to track assets, detect misconfigurations, and monitor system drift

Build monitoring pipelines that capture queries, responses, tool usage, and evaluation metrics

Design a full AI security control map and plan actionable rollout steps for organizational adoption

Course Description

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.