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    • AI+ Security Expert™

    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.
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    AI+ Security Expert™
    Price: USD $495.00

    At a Glance: Course + Exam Overview

    Category AI Security
    AI Technical
    Program Name AI+ Security Expert™
    Prerequisites
      • Basic understanding of cybersecurity concepts and practices.
      • Familiarity with AI and machine learning fundamentals.
      • Knowledge of cloud computing and application security.
      • Experience with security assessment methodologies.
      • Understanding of programming and automation concepts.
    Exam Format 90 minutes

    What You'll Learn

    Certification 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

    Finish the course and get certified

    Certificate 1

    Industry Opportunities

    • AI Security Analyst
      AI Security Analyst
      Uses AI techniques for threat detection, security analysis, and risk identification.
    • Cybersecurity Engineer
      Cybersecurity Engineer
      Implements AI-powered security solutions and defense strategies.
    • SOC Analyst
      SOC Analyst
      Uses AI tools for monitoring, investigation, and incident response.
    • Cloud Security Engineer
      Cloud Security Engineer
      Applies AI security solutions to protect cloud environments.
    • Penetration Tester
      Penetration Tester
      Uses AI-driven techniques to identify vulnerabilities.
    • Security Consultant
      Security Consultant
      Advises organizations on AI-based cybersecurity strategies.

    Frequently Asked Questions

    The curriculum covers AI fundamentals, Python programming, machine learning, malware detection, network security, and AI-based threat analysis.

    Learners explore tools such as CrowdStrike Falcon, Darktrace Enterprise, SentinelOne Singularity, IBM QRadar Advisor with Watson, and Fortinet FortiAI.

    Learners receive an industry-recognized credential along with hands-on experience through projects and case studies.

    Learners should have basic computer science knowledge, interest in AI technologies, and awareness of AI ethics and data privacy.

    The certification is suitable for learners interested in AI technologies, cybersecurity, threat detection, and security automation.

    Prerequisites

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    Exam Details

    Duration

    90 minutes

    Format

    50 multiple-choice/multiple-response questions

    Exam Blueprint

    AI Security Context, Scope and Opportunities 5%
    AI Application Architecture and Threat Modelling 9%
    Applied Python Automation for AI Security Evidence 9%
    GenAI Application Security Controls 9%
    Prompt Injection, LLM Risk Testing, and Adversarial Attacks 9%
    RAG and Knowledge System Security 9%
    AI Data, Model, ML Pipeline and Detection Security 9%
    Secure AI Deployment: Cloud, API and Identity 9%
    AI Security Monitoring and Incident Response 8%
    AI Governance, Privacy and Compliance 8%
    Advanced Adversarial Testing, Red Teaming 8%
    Capstone Project 8%
    Course Price: USD $495.00
    Self-Paced Online
    Purchase Self-Paced Course
    Instructor-Led (Live Virtual/Classroom)