Opleiding: AI+ Security Expert™

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.

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. 

• 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.

• 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

• 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

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

Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam

Access to all materials and exams is provided for 365 days after delivery.

De OCICT maatwerk-methode houdt in dat op basis van de behoefte van de klant - en indien nodig na een intakegesprek - een individueel opleidingstraject wordt opgesteld. De trainingen worden veelal individueel gevolgd, zodat iedere training een maatwerktraject is. Klassikale trainingen zijn natuurlijk mogelijk, mits ze aansluiten bij de leerbehoeften van de deelnemers / opdrachtgever.

Alle onderdelen van de training kennen een voortdurende wisselwerking tussen theorie en praktijk. De inhoud van de training kan vooraf per cursist worden aangepast en tijdens de cursus worden bijgesteld. De focus ligt op maatwerk: het traject wordt afgestemd op de doelstellingen van de deelnemer(s) en/of opdrachtgever, ongeacht of het individueel of klassikaal wordt aangeboden.