AI+ Security Expert™

$495.00
Protect and Secure: Leverage Intelligent AI Solutions

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.

Learning Modules

Module 1: AI Security Context, Scope and Opportunities

  1. 1.1 AI Security Scope and Enterprise Context
  2. 1.2 AI Security Roles and Responsibilities
  3. 1.3 AI Security Use Cases and Opportunities
  4. 1.4 Use Cases
  5. 1.5 Case Studies

Module 2: AI Application Architecture and Threat Modelling

  1. 2.1 AI Application Components
  2. 2.2 Assets, Trust Boundaries and Data Flows
  3. 2.3 Modern Cybersecurity Architecture
  4. 2.4 Threat Modelling for AI Applications
  5. 2.5 Use Cases
  6. 2.6 Case Studies

Module 3: Applied Python Automation for AI Security Evidence

  1. 3.1 Python for AI Security Tasks
  2. 3.2 Python Libraries for Security Engineering
  3. 3.3 Working with Security Data
  4. 3.4 Cybersecurity Data Analytics
  5. 3.5 Automation Patterns and Safe Scripting
  6. 3.6 Use Cases
  7. 3.7 Case Studies

Module 4: GenAI Application Security Controls

  1. 4.1 GenAI Application Components
  2. 4.2 Secure Design Patterns
  3. 4.3 Secure AI SDLC
  4. 4.4 Use Cases
  5. 4.5 Case Studies

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

  1. 5.1 Prompt Injection Techniques
  2. 5.2 Sensitive Information Disclosure Risks
  3. 5.3 Unsafe Output Handling
  4. 5.4 Use Cases
  5. 5.5 Case Studies

Module 6: RAG and Knowledge System Security

  1. 6.1 RAG System Architecture
  2. 6.2 RAG-Specific Risks
  3. 6.3 RAG Controls and Monitoring
  4. 6.4 Use Cases
  5. 6.5 Case Studies

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

  1. 7.1 AI Data Security
  2. 7.2 Model and Artifact Security
  3. 7.3 ML Pipeline and MLSecOps Controls
  4. 7.4 AI-Based Detection and Model Monitoring
  5. 7.5 Adversarial ML Risks
  6. 7.6 Use Cases
  7. 7.7 Case Studies

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

  1. 8.1 AI Deployment Patterns
  2. 8.2 Identity and Secret Controls
  3. 8.3 Abuse Prevention and Cloud Controls
  4. 8.4 Use Cases
  5. 8.5 Case Studies

Module 9: AI Security Monitoring and Incident Response

  1. 9.1 AI Security Telemetry
  2. 9.2 Detection Engineering for AI Threats
  3. 9.3 AI Incident Response
  4. 9.4 Use Cases
  5. 9.5 Case Studies

Module 10: AI Governance, Privacy and Compliance

  1. 10.1 AI Governance Foundations
  2. 10.2 Privacy and Data Protection
  3. 10.3 Assurance Artifacts and Evidence
  4. 10.4 Use Cases
  5. 10.5 Case Studies

Module 11: Advanced Adversarial Testing, Red Teaming

  1. 11.1 Red Teaming Methodologies for AI Systems
  2. 11.2 Advanced Threat Vectors
  3. 11.3 Red Team Reporting
  4. 11.4 Use Cases
  5. 11.5 Case Studies

Module 12: Capstone Project

  1. 12.1 Proactive Threat Intelligence Dashboard
  2. 12.2 AI-Driven Cybersecurity Solution Development
  3. 12.3 AI-Powered SOC Automation
  4. 12.4 LLM Security Monitoring and Defense System

Optional Module: AI Agents Security Expert

  1. 1.1 What Are AI Agents?
  2. 1.2 Key Capabilities of AI Agents in Advanced Cybersecurity
  3. 1.3 Applications and Trends for AI Agents in Advanced Cybersecurity
  4. 1.4 How Does an AI Agent Work?
  5. 1.5 Core Characteristics of AI Agents
  6. 1.6 Types of AI Agents
Why This Certification Matters

Comprehensive AI-Cybersecurity Integration:

Validates intermediate-level competency in AI-driven security defense mechanisms.

Practical Python Programming Skills:

Demonstrates competency in detecting and responding to modern cyber threats.

Advanced Threat Detection Techniques:

Exam includes scenario-based questions reflecting real-world cybersecurity incidents.

Cutting-Edge AI Algorithms:

Validates readiness for intermediate to senior-level cybersecurity responsibilities.

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