AI+ Security Expert™

Protect and Secure: Leverage Intelligent AI Solutions

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Certificate code

AT-2102

Duration

Instructor-Led: 5 days (live or virtual) Self-Paced: 40 hours of content

Exam format

50 questions, 70% passing, 90 minutes, online proctored exam

Includes

Instructor-led OR Self-paced course + Official exam + Digital badge

Overview Pricing Opportunities Course Modules Tools

Our pricing

Technical

Technical course options

Instructor-led pricing appears only when we have an open cohort for the programme.

Self-paced

NGN 283,500

NGN 358,500

Independent learning with certification preparation support.

Programme overview

Certification preparation delivered with Stackron Academy support

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

Interest in AI technologies, basic computer science knowledge, curiosity to learn, and awareness of AI ethics and data privacy.

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

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.

Who should enroll

Cybersecurity Professionals: Professionals who want to stay updated on the latest AI-driven security tools, technologies, and techniques to strengthen cybersecurity practices.

IT Professionals and System Administrators: Those who want to use AI capabilities to detect, analyze, and respond to security threats more effectively and efficiently.

Cloud Architects and Engineers: Professionals who want to integrate AI-powered security solutions into cloud architectures and enhance the protection of cloud environments.

Risk Management Specialists: Those who want to apply AI-driven approaches to identify, assess, and mitigate cybersecurity risks.

Business Leaders and Decision Makers: Professionals who want to understand the role of AI in cybersecurity and make informed decisions about security investments and strategies.

Software Developers: Developers who want to understand AI integration in security tools, applications, and secure software development practices.

Security Consultants and Advisors: Professionals who want to gain advanced knowledge of AI technologies to provide strategic cybersecurity guidance and recommendations.