AI+ Security ExpertTM

Build intermediate-level expertise in AI-driven cybersecurity, adversarial AI, secure machine learning, governance, and compliance.

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Protect and Secure: Leverage Intelligent AI Solutions

  • Applied Security Skills:
    Learn to apply security controls, risk management, and AI-enabled threat detection in real environments
  • Project-Based Learning:
    Work through projects and case studies that reflect real-world cybersecurity incidents
  • Industry Credential:
    Earn a recognized certification and digital badge after passing the official exam
  • Flexible Learning:
    Choose live instructor-led training or about 40 hours of self-paced content
  • Leading Tools:
    Explore widely used AI security platforms from vendors such as CrowdStrike and Darktrace

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.

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: Includes scenario-based questions that reflect real-world cybersecurity incidents.
  • Cutting-Edge AI Algorithms: Validates readiness for intermediate to senior-level cybersecurity responsibilities.

What You'll Learn?

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

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

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

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

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

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

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

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

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

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

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

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

Job Roles & Industry Outlook

AI Security Analyst

Uses AI techniques for threat detection, security analysis, and risk identification.

Cybersecurity Engineer

Implements AI-powered security solutions and defense strategies.

SOC Analyst

Uses AI tools for monitoring, investigation, and incident response.

Cloud Security Engineer

Applies AI security solutions to protect cloud environments.

Penetration Tester

Uses AI-driven techniques to identify vulnerabilities.

Security Consultant

Advises organizations on AI-based cybersecurity strategies.

Skills You’ll Gain

AI-Driven Threat Detection and Cybersecurity AnalysisMachine Learning Applications in Security OperationsPython-Based Security Automation and Tool DevelopmentAI-Powered Malware and Email Threat DetectionNetwork Anomaly Detection and Risk AnalysisAI-Based Authentication and Security SolutionsPenetration Testing Using AI TechniquesResponsible AI, Security Governance, and Compliance PracticesDesigning AI-Powered Cybersecurity Solutions Through Practical Projects

Tools You’ll Master

CrowdStrike FalconDarktrace EnterpriseVectra CognitoSentinelOne SingularityCylance PROTECTIBM QRadar Advisor with WatsonExabeam Advanced AnalyticsRapid7 InsightIDRCynet 360Fortinet FortiAI

Exam Details

Duration

90 minutes

Passing Score

70% (35/50)

Format

50 multiple-choice/multiple-response questions

Delivery Method

Online via proctored exam platform (flexible scheduling)

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%

What’s Included

(One-Year Subscription + All Updates)

  • High-Quality Videos, E-book (PDF & Audio), and Podcasts 
  • AI Mentor for Personalized Guidance 
  • Quizzes, Assessments, and Course Resources 
  • Online Proctored Exam with One Free Retake 
  • Comprehensive Exam Study Guide 
  • Access for Tablet & Phone