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AI+ Security Expert™

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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Certificate Code

AT-2102

Exam Format

AI-Driven Remote Exam Proctoring

Course Overview

Important details and certification information

Instructor-led OR Self-paced course + Official exam + Digital badge
Instructor-Led: 3 Days (live or virtual)
Interest in AI technologies, basic computer science knowledge, curiosity to learn, and awareness of AI ethics and data privacy.
50 questions, 70% passing, 90 minutes, online proctored exam

Certification Modules

  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

  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

  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

  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

  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

  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

  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

  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

  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

  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

  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

  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

  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

AI Tools Covered

CrowdStrike Falcon
Darktrace Enterprise
Vectra Cognito
SentinelOne Singularity
Cylance PROTECT
IBM QRadar Advisor with Watson
Exabeam Advanced Analytics
Rapid7 InsightIDR
Cynet 360
Fortinet FortiAI
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