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AI+ Security Expert™
USD$495.00 (GST excl.)
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.
Duration
Instructor-Led: 5 days (live or virtual)
Self-Paced: 40 hours of content
Self-Paced: 40 hours of content
Format
Online, self-paced
Exam
50 questions, 70% passing, 90 minutes, online proctored exam
Overview
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.
What you’ll learn
- 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.
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.
Tools you’ll use
CrowdStrike Falcon
Darktrace Enterprise
Vectra Cognito
SentinelOne Singularity
Cylance PROTECT
IBM QRadar Advisor with Watson
Exabeam Advanced Analytics
Rapid7 InsightIDR
Cynet 360
Fortinet FortiAI
Learning Modules
13 modules
1Module 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
1.2 AI Security Roles and Responsibilities
1.3 AI Security Use Cases and Opportunities
1.4 Use Cases
1.5 Case Studies
2Module 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
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
3Module 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
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
4Module 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
4.2 Secure Design Patterns
4.3 Secure AI SDLC
4.4 Use Cases
4.5 Case Studies
5Module 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
5.2 Sensitive Information Disclosure Risks
5.3 Unsafe Output Handling
5.4 Use Cases
5.5 Case Studies
6Module 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
6.2 RAG-Specific Risks
6.3 RAG Controls and Monitoring
6.4 Use Cases
6.5 Case Studies
7Module 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
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
8Module 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
8.2 Identity and Secret Controls
8.3 Abuse Prevention and Cloud Controls
8.4 Use Cases
8.5 Case Studies
9Module 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
9.2 Detection Engineering for AI Threats
9.3 AI Incident Response
9.4 Use Cases
9.5 Case Studies
10Module 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
10.2 Privacy and Data Protection
10.3 Assurance Artifacts and Evidence
10.4 Use Cases
10.5 Case Studies
11Module 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
11.2 Advanced Threat Vectors
11.3 Red Team Reporting
11.4 Use Cases
11.5 Case Studies
12Module 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
12.2 AI-Driven Cybersecurity Solution Development
12.3 AI-Powered SOC Automation
12.4 LLM Security Monitoring and Defense System
13Optional 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
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
| Format | Online, self-paced |
|---|---|
| Duration | Instructor-Led: 5 days (live or virtual) |
| Exam | 50 questions, 70% passing, 90 minutes, online proctored exam |
| Included | Instructor-led OR Self-paced course + Official exam + Digital badge |
| Prerequisites | Interest in AI technologies, basic computer science knowledge, curiosity to learn, and awareness of AI ethics and data privacy. |
| Delivery | Projects & case studies |
| Outcome | Industry-recognized credential + hands-on experience |
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