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Security Deep Dive Guide

Secure your AI systems in production with the industry-standard OWASP LLM Top 10 framework. Master prompt injection defense (direct and indirect), enterprise-grade secrets management beyond .env (HashiCorp Vault, cloud KMS, rotation), input/output sanitization, PII protection, and AI-specific security testing. Designed for AI Engineers with deployed systems who need to harden them against AI-native threats. 8 modules, real breach case studies, and a fully secured AI system as your capstone project.

64
lessons
8
modules
English · Spanish
available in
Yes
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Included in the Club
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NIEVA

Outcomes

What you'll be able to do

  • Apply the OWASP LLM Top 10 2025 framework to your AI architecture and prioritize mitigations
  • Defend against direct and indirect prompt injection (including RAG and document poisoning)
  • Implement enterprise-grade secrets management with HashiCorp Vault or cloud KMS (AWS, GCP, Azure)
  • Configure API key rotation, audit trails, and least-privilege access for LLM credentials
  • Sanitize and validate AI inputs/outputs to prevent improper output handling (LLM05)
  • Detect and redact PII in LLM inputs and outputs to prevent sensitive information disclosure
  • Conduct AI-specific penetration testing with adversarial prompts and automated security checks
  • Integrate all defense layers into a fully secured AI system with security deployment checklist

Before you start

What you need to bring

It's for you if...

  • AI Engineers with production-deployed systems (chatbots, RAG, agents) who need to harden them against AI-native threats
  • Developers responsible for compliance and security in AI applications processing sensitive data
  • Tech leads preparing teams to apply OWASP LLM Top 10 and implement AI security processes
  • Security-conscious engineers who want to differentiate with AI-specific security expertise
  • Teams transitioning AI prototypes to production with enterprise security requirements

Requirements and materials

  • Production Best Practices Guide (#13) completed: guardrails basics, testing, structured logging
  • Python intermediate-advanced (OOP, type hints, Pydantic)
  • AI systems deployed in production (REST APIs, RAG, agents)
  • Familiarity with FastAPI or similar
  • Basic notion of threat modeling and security concepts

Content

The syllabus, module by module

Open any of them to see its lessons.

  • 1. Introduction: AI Security Landscape & Threat Model
  • 2. AI Threats vs. Traditional Web
  • 3. Threat Modeling for LLM Systems
  • 4. OWASP LLM Top 10 — Overview
  • 5. Real-World AI Breach Cases
  • 6. Security-by-Design for AI Systems
  • 7. Documenting Your Threat Model
  • 8. Project: Threat Model Document

  • 1. Introduction: OWASP LLM Top 10 Deep Dive
  • 2. LLM01: Prompt Injection
  • 3. LLM02: Sensitive Information Disclosure
  • 4. LLM03 and LLM04: Supply Chain and Data Poisoning
  • 5. LLM05 and LLM06: Improper Output Handling and Excessive Agency
  • 6. LLM07 and LLM08: System Prompt Leakage and Vector/Embedding Weaknesses
  • 7. LLM09 and LLM10: Misinformation and Unbounded Consumption
  • 8. Project: OWASP Mapping Audit

  • 1. Introduction: Prompt Injection — Attacks & Defenses
  • 2. Direct Prompt Injection
  • 3. Indirect Prompt Injection
  • 4. Defense Layer 1: Input Validation and Sanitization
  • 5. Defense Layer 2: Output Filtering and Validation
  • 6. Defense Layer 3: Instruction Hierarchy and System Prompt Hardening
  • 7. Defense Layers 4-5: Sandboxing, Isolation and Monitoring
  • 8. Project: Injection Defense Pipeline

  • 1. Introduction: Input & Output Sanitization
  • 2. Input Sanitization: Fundamentals
  • 3. Output Validation with Pydantic
  • 4. Content Filtering
  • 5. Deep Guardrails
  • 6. Complete Input → Output Pipeline
  • 7. Edge Cases and Production
  • 8. Project: Sanitization Pipeline

  • 1. Introduction: Secrets Management
  • 2. Beyond .env: Why It's Not Enough
  • 3. HashiCorp Vault: Concepts and Setup
  • 4. API Key Rotation Strategies
  • 5. Cloud KMS: AWS, GCP, Azure
  • 6. Token Lifecycle and Audit Trails
  • 7. Least Privilege and Python Integration
  • 8. Project: Secrets Management Setup

  • 1. Introduction: Data Privacy & PII Protection
  • 2. LLM02 in Detail: Sensitive Information Disclosure
  • 3. PII Detection: Patterns, Regex, and Presidio
  • 4. PII Redaction: Before and After the LLM
  • 5. Data Minimization
  • 6. Retention Policies and Encryption
  • 7. Compliance Basics: GDPR, CCPA, and AI
  • 8. Project: PII Protection Layer

  • 1. Introduction: Security Testing & Auditing
  • 2. AI-Specific Pen Testing
  • 3. Adversarial Prompts and Attack Datasets
  • 4. Automated Security Checks
  • 5. Red Team Exercises
  • 6. AI Security Tools
  • 7. Audit Checklist and Report
  • 8. Project: Security Audit Report

  • 1. Introduction: Capstone Project — Secured AI System
  • 2. Integration Architecture: The Complete Flow of a Secure Request
  • 3. Closing the Security Audit Gaps
  • 4. Security Deployment Checklist
  • 5. Incident Response Runbook
  • 6. Documenting Security Decisions
  • 7. Testing the Complete System
  • 8. Project: Secured AI System

Where it fits

This guide is part of something bigger

It's studied inside these programs, with support and dates.

AI Engineering Bootcamp [January 2026]AI Engineering Bootcamp [Agosto 2026]

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