AI+ Security Expert™

Struktura kurzu

Tato část není lokalizována

Module 1: AI Security Context, Scope and Opportunities

• 1.1 AI Security Scope and Enterprise Context
• 1.2 AI Security Roles and Responsibilities
• 1.3 AI Security Use Cases and Opportunities
• 1.4 Use Cases
• 1.5 Case Studies

Module 2: AI Application Architecture and Threat Modelling

• 2.1 AI Application Components
• 2.2 Assets, Trust Boundaries and Data Flows
• 2.3 Modern Cybersecurity Architecture
• 2.4 Threat Modelling for AI Applications
• 2.5 Use Cases
• 2.6 Case Studies

Module 3: Applied Python Automation for AI Security Evidence

• 3.1 Python for AI Security Tasks
• 3.2 Python Libraries for Security Engineering
• 3.3 Working with Security Data
• 3.4 Cybersecurity Data Analytics
• 3.5 Automation Patterns and Safe Scripting
• 3.6 Use Cases
• 3.7 Case Studies

Module 4: GenAI Application Security Controls

• 4.1 GenAI Application Components
• 4.2 Secure Design Patterns
• 4.3 Secure AI SDLC
• 4.4 Use Cases
• 4.5 Case Studies

Module 5: Prompt Injection, LLM Risk Testing, and Adversarial Attacks

• 5.1 Prompt Injection Techniques
• 5.2 Sensitive Information Disclosure Risks
• 5.3 Unsafe Output Handling
• 5.4 Use Cases
• 5.5 Case Studies

Module 6: RAG and Knowledge System Security

• 6.1 RAG System Architecture
• 6.2 RAG-Specific Risks
• 6.3 RAG Controls and Monitoring
• 6.4 Use Cases
• 6.5 Case Studies

Module 7: AI Data, Model, ML Pipeline and Detection Security

• 7.1 AI Data Security
• 7.2 Model and Artifact Security
• 7.3 ML Pipeline and MLSecOps Controls
• 7.4 AI-Based Detection and Model Monitoring
• 7.5 Adversarial ML Risks
• 7.6 Use Cases
• 7.7 Case Studies

Module 8: Secure AI Deployment: Cloud, API and Identity

• 8.1 AI Deployment Patterns
• 8.2 Identity and Secret Controls
• 8.3 Abuse Prevention and Cloud Controls
• 8.4 Use Cases
• 8.5 Case Studies

Module 9: AI Security Monitoring and Incident Response

• 9.1 AI Security Telemetry
• 9.2 Detection Engineering for AI Threats
• 9.3 AI Incident Response
• 9.4 Use Cases
• 9.5 Case Studies

Module 10: AI Governance, Privacy and Compliance

• 10.1 AI Governance Foundations
• 10.2 Privacy and Data Protection
• 10.3 Assurance Artifacts and Evidence
• 10.4 Use Cases
• 10.5 Case Studies

Module 11: Advanced Adversarial Testing, Red Teaming

• 11.1 Red Teaming Methodologies for AI Systems
• 11.2 Advanced Threat Vectors
• 11.3 Red Team Reporting
• 11.4 Use Cases
• 11.5 Case Studies

Module 12: Capstone Project

• 12.1 Proactive Threat Intelligence Dashboard
• 12.2 AI-Driven Cybersecurity Solution Development
• 12.3 AI-Powered SOC Automation
• 12.4 LLM Security Monitoring and Defense System

Optional Module: AI Agents Security Expert

• 1.1 What Are AI Agents?
• 1.2 Key Capabilities of AI Agents in Advanced Cybersecurity
• 1.3 Applications and Trends for AI Agents in Advanced Cybersecurity
• 1.4 How Does an AI Agent Work?
• 1.5 Core Characteristics of AI Agents
• 1.6 Types of AI Agents