Cybersecurity
Advanced
Building Secure AI Products
Design AI product boundaries that protect users, data, and operations.
Course Overview
Map trust boundaries, constrain tool access, validate inputs, and plan incident responses for AI-enabled products. The material connects familiar application security practices to new model-driven risks.
What You Will Learn
Map trust boundaries between users, client apps, LLM models, and external tools
Mitigate direct and indirect prompt injection attacks
Enforce least-privilege tool access controls and input validation layers
Prevent sensitive data leakage (PII, system credentials) in model responses
Develop incident response plans for AI safety and security events
Tools & Technologies Used
Python
OWASP LLM Top 10
Pydantic
FastAPI
Bandit
Structured Curriculum
2 Modules · 6 Lessons · 240 Minutes Total
Module 1
Module 1: AI Threat Modeling & Attack Vectors
3 lessons
Module 1: AI Threat Modeling & Attack Vectors
Understand OWASP LLM top risks including prompt injection and data poisoning.
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OWASP Top 10 for LLM Applications
Analyze real-world exploits involving prompt injection, data leakage, and insecure output.
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Indirect Prompt Injection in Retrieval Contexts
Simulate malicious instruction injection inside indexed documents and craft sanitization wrappers.
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Privilege Escalation via Unconstrained Tools
Enforce strict scope checking and argument verification on database mutation tools.
Module 2
Module 2: Defensive Architecture & Data Protection
3 lessons
Module 2: Defensive Architecture & Data Protection
Implement input filtering, output sanitization, and PII masking.
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PII Masking & Confidential Data Scrubbing
Build regex and NER-based data redaction middleware for incoming user prompts.
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Sandboxing Tool Execution Environments
Isolate code execution tools inside restricted containers with resource quotas.
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Security Incident Response for Model Exploits
Formulate a response runbook for compromised prompts or ungrounded data disclosure.
Practical Project & Capstone Outcome
🚀 Capstone Project
Hardened Enterprise Assistant Gateway
Build a security gateway microservice that filters prompt injection attempts, scrubs PII, validates tool parameters, and logs security audit events.
Prerequisites
- Basic cybersecurity awareness and backend development experience
Intended Audience
- Security Engineers auditing LLM applications
- Software Architects designing secure enterprise AI services
Instructor Information
E
Elena Torres
Course Author & Industry Expert
Elena Torres is a Senior Cybersecurity Researcher specializing in AI product security and defensive application architecture.
Frequently Asked Questions
Does this cover cloud security or model weight stealing?
The course focuses primarily on application-layer security: inputs, outputs, tool safety, and data privacy.