AI+ Security Expert™
Protect and Secure: Leverage Intelligent AI Solutions
$495one-time payment
Overview
About This Certification
This certification validates intermediate-level knowledge of AI-driven cybersecurity concepts and assesses competency in applying security controls, risk management practices, and AI-enabled threat detection techniques. The exam evaluates understanding of advanced security principles within AI-augmented environments.
Prerequisites & Duration
Interest in AI technologies, basic computer science knowledge, curiosity to learn, and awareness of AI ethics and data privacy.
Instructor-Led: 5 days (live or virtual) Self-Paced: 40 hours of content
Assessment
50 questions, 70% passing, 90 minutes, online proctored exam
Curriculum
Certification Modules
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
Tools
AI Tools Covered
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