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AI+ Cloud Practitioner™ eLearning
Formerly known as AI+ Cloud™
Transform Cloud Computing with Cutting-Edge AI integration
Cloud-AI Fusion: Learn to integrate AI into scalable cloud environments
Advanced Infrastructure: Master CI/CD, cloud AI models, and deployment strategies
Capstone Project: Gain hands-on experience with real-world applications
Future-Ready Skills: Prepares professionals to lead AI-powered cloud innovation
Cloud Professionals: Enhance your cloud management skills by integrating AI to optimize cloud performance, improve resource utilization.
Cloud Architects & Engineers: Learn to leverage AI to design scalable cloud infrastructures, automate cloud provisioning, and enhance security.
IT Infrastructure Managers: Use AI to optimize cloud deployment, automate system management, and improve cloud security and disaster recovery planning.
Business Leaders: Drive innovation in your organization by adopting AI in cloud technologies to enhance scalability, reduce costs, and optimize cloud solutions.
Students & Fresh Graduates: Gain a competitive edge in the cloud computing field by mastering AI tools and techniques that are revolutionizing cloud infrastructure.
Leverage AI for Smarter Leadership Decisions:Learn how to harness AI tools to streamline operations, enhance strategic planning, and drive performance.
Enhance AI Integration Across the Organization:Use AI to accelerate the integration of AI-driven solutions, automating processe.
Stay Ahead in AI-Driven Innovation:As demand for AI expertise rises, Chief AI Officers with advanced AI knowledge are highly sought after to spearhead AI.
Boost Strategic Decision-Making with AI Analytics:Master AI models to analyze business data, predict outcomes, and enable more informed, real-time decisions.
Advance Your Career in AI Leadership:With AI reshaping industries, this certification equips you with the skills needed to lead AI initiatives.
Module 1: Cloud Fundamentals
1.1 Cloud Computing Models
1.2 Core Cloud Services
1.3 Identity & Access Management (IAM), Security & Compliance Basics
1.4 Billing, Cost Optimization, and Cloud Economics
1.5 Multi-cloud Concepts
1.6 Infrastructure as Code (IaC) Basics with Terraform
1.7 Use Cases
1.8 Case Studies
1.9 Hands-On Activity
Module 2: AI Fundamentals and Python Fundamentals
2.1 Introduction to Artificial Intelligence, Machine Learning Types
2.2 Neural Networks and Deep Learning Fundamentals
2.3 Python Programming
2.4 Essential Libraries
2.5 Mathematics for AI
2.6 Data Preprocessing, Exploration, and Visualization Techniques
2.7 Use Cases
2.8 Case Studies
Module 3: Data Engineering for AI
3.1 Data Collection, Storage, and Processing Pipelines (ETL/ELT)
3.2 Big Data Technologies
3.3 Data Lakes, Data Warehouses, and Feature Stores
3.4 Data Quality, Governance, Versioning, and Cataloging
3.5 Real-Time Data Streaming
3.6 Use Cases
3.7 Case Studies
Module 4: Cloud with AI
4.1 Managed AI/ML Platforms
4.2 Model Training, Deployment, and Inference on Cloud
4.3 Containerization with Docker and Orchestration with Kubernetes
4.4 Serverless AI Architectures
4.5 Scaling and Monitoring AI Workloads
4.6 Use Cases
4.7 Case Studies
Module 5: Generative AI and LLM Models
5.1 Transformer Architecture, Attention Mechanism, and Tokenization
5.2 Major LLM Families: GPT, Llama, Gemini, Claude, Mistral
5.3 Prompt Engineering Techniques
5.4 Generative Model Lifecycle
5.5 Multimodal Generative AI
5.6 Use Cases
5.7 Case Studies
Module 6: Cloud with Generative AI and LLM Models
6.1 Deploying and Hosting LLMs on Cloud Platforms
6.2 Inference Optimization Techniques
6.3 Integration with Cloud-Native Services
6.4 Cost Governance for GenAI Workloads
6.5 Hybrid and Edge Deployment Strategies
6.6 Use Cases
6.7 Case Studies
Module 7: AI Workloads on Cloud
7.1 MLOps Lifecycle and Best Practices
7.2 Experiment Tracking (MLflow), Model Versioning, and CI/CD Pipelines
7.3 Model Monitoring and Performance Drift Detection
7.4 Orchestration Tools: SageMaker Pipelines, Vertex AI Pipelines, Kubeflow
7.5 Use Cases
7.6 Case Studies
Module 8: Retrieval-Augmented Generation (RAG)
8.1 RAG Architecture and Components
8.2 Vector Databases and Embeddings
8.3 Advanced RAG Patterns
8.4 Evaluation Metrics for RAG Systems
8.5 Cloud-Native Vector Search Services
8.6 Use Cases
8.7 Case Studies
Module 9: Fine-Tuning and Optimization on Cloud
9.1 Full Fine-Tuning vs. Parameter-Efficient Fine-Tuning (PEFT)
9.2 Distributed Training and Hyperparameter Optimization
9.3 Model Compression, Distillation, and Quantization
9.4 Domain Adaptation and Continual Learning
9.5 Cloud Tools for Efficient Fine-Tuning
9.6 Use Cases
9.7 Case Studies
Module 10: Agentic AI on Cloud
10.1 AI Agents Fundamentals
10.2 Distributed Frameworks: LangGraph, CrewAI, AutoGen, Semantic Kernel
10.3 Multi-Agent Systems and Orchestration
10.4 Autonomous Workflows and Decision Engines
10.5 Cloud Deployment of Agentic Systems
10.6 Use Cases
10.7 Case Studies
Module 11: Evaluation, Monitoring, Security & Responsible AI
11.1 Comprehensive LLM and GenAI Evaluation Frameworks
11.2 Bias Detection, Fairness, and Explainability
11.3 Security Threats
11.4 Guardrails, Content Moderation, and Compliance (GDPR, SOC2)
11.5 Responsible AI Governance and Audit Practices
11.6 Use Cases
11.7 Case Studies
Module 12: Capstone Project
12.1 Problem Identification and Solution Planning
12.2 AI Model Development and Cloud Deployment
12.3 Deliverables
Optional Module: AI Agents for Cloud
1. What Are AI Agents?
2. Examples of AI Agents for Cloud Services
3. Significance of AI Agents in Cloud Services
4. Trends in AI Agents for Cloud Services
5. Importance of AI Agents
6. Types of AI Agents
7. Case Studies
8. Hands-On Activity
Tools you will explore
TensorFlow
SHAP (SHapley Additive exPlanations)
Amazon S3
AWS SageMaker
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.
Instructor-led OR Self-paced course + Official exam + Digital badge
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.
€530
E-Learning
max 999
5 dagen
AI+ Data Practitioner™
's-Hertogenbosch
ma 11 jan. 2027
Formerly known as AI+ Data™
Mastering AI, Maximizing Data: Your Path to Innovation
Core Concepts Covered: Data Science foundations, Python, Statistics, and Data Wrangling
Advanced Topics: Dive into Generative AI, Machine Learning, and Predictive Analytics
Capstone Application: Solve real-world problems like employee attrition with AI
Career Readiness: Develop skills for AI-driven data science roles with hands-on mentorship
Data Analysts & Scientists: Enhance data analysis capabilities using AI for predictive modeling and decision-making.
Business Intelligence Professionals: Leverage AI to uncover insights, trends, and opportunities in complex data sets.
IT Specialists & System Integrators: Implement AI-powered solutions to optimize data management and infrastructure.
Data Engineers: Design and develop AI-driven data pipelines and architectures for scalable solutions.
Students & New Graduates: Build valuable AI and data science skills to thrive in an increasingly data-driven world.
Demand for Certified Experts:Organizations seek certified experts who can transform complex data into actionable insights while ensuring data integrity and privacy.
Mitigating Data and AI Risks:Poor handling of data and AI technologies can lead to inaccurate analysis and business risks. This certification helps professionals mitigate such challenges.
Designing AI-Driven Data Strategies:Certified professionals play a crucial role in designing AI-driven data strategies that optimize performance and align with regulatory standards.
Career Advancement:As AI-powered data solutions become essential for businesses, this certification provides professionals with a competitive edge in advancing their careers.
Course Overview
Course Introduction Preview
Module 1: Foundations of Data Science
1.1 Introduction to Data Science
1.2 Data Science Life Cycle
1.3 Applications of Data Science
Module 2: Foundations of Statistics
2.1 Basic Concepts of Statistics
2.2 Probability Theory
2.3 Statistical Inference
Module 3: Data Sources and Types
3.1 Types of Data
3.2 Data Sources
3.3 Data Storage Technologies
Module 4: Programming Skills for Data Science
4.1 Introduction to Python for Data Science
4.2 Introduction to R for Data Science
Module 5: Data Wrangling and Preprocessing
5.1 Data Imputation Techniques
5.2 Handling Outliers and Data Transformation
Module 6: Exploratory Data Analysis (EDA)
6.1 Introduction to EDA
6.2 Data Visualization
Module 7: Generative AI Tools for Deriving Insights
7.1 Introduction to Generative AI Tools
7.2 Applications of Generative AI
Module 8: Machine Learning
8.1 Introduction to Supervised Learning Algorithms
8.2 Introduction to Unsupervised Learning
8.3 Different Algorithms for Clustering
8.4 Association Rule Learning with Implementation
Module 9: Advance Machine Learning
9.1 Ensemble Learning Techniques
9.2 Dimensionality Reduction
9.3 Advanced Optimization Techniques
Module 10: Data-Driven Decision-Making
10.1 Introduction to Data-Driven Decision Making
10.2 Open Source Tools for Data-Driven Decision Making
10.3 Deriving Data-Driven Insights from Sales Dataset
Module 11: Data Storytelling
11.1 Understanding the Power of Data Storytelling
11.2 Identifying Use Cases and Business Relevance
11.3 Crafting Compelling Narratives
11.4 Visualizing Data for Impact
Module 12: Capstone Project - Employee Attrition Prediction
12.1 Project Introduction and Problem Statement
12.2 Data Collection and Preparation
12.3 Data Analysis and Modeling
12.4 Data Storytelling and Presentation
Optional Module: AI Agents for Data Analysis
1. Understanding AI Agents
2. Case Studies
3. Hands-On Practice with AI Agents
Tools you will explore
Google Colab
MLflow
Alteryx
KNIME
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.
Instructor-led OR Self-paced course + Official exam + Digital badge
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.
€3.930
Klassikaal
max 12
5 dagen
AI+ Data Practitioner™ eLearning
Formerly known as AI+ Data™
Mastering AI, Maximizing Data: Your Path to Innovation
Core Concepts Covered: Data Science foundations, Python, Statistics, and Data Wrangling
Advanced Topics: Dive into Generative AI, Machine Learning, and Predictive Analytics
Capstone Application: Solve real-world problems like employee attrition with AI
Career Readiness: Develop skills for AI-driven data science roles with hands-on mentorship
Data Analysts & Scientists: Enhance data analysis capabilities using AI for predictive modeling and decision-making.
Business Intelligence Professionals: Leverage AI to uncover insights, trends, and opportunities in complex data sets.
IT Specialists & System Integrators: Implement AI-powered solutions to optimize data management and infrastructure.
Data Engineers: Design and develop AI-driven data pipelines and architectures for scalable solutions.
Students & New Graduates: Build valuable AI and data science skills to thrive in an increasingly data-driven world.
Demand for Certified Experts:Organizations seek certified experts who can transform complex data into actionable insights while ensuring data integrity and privacy.
Mitigating Data and AI Risks:Poor handling of data and AI technologies can lead to inaccurate analysis and business risks. This certification helps professionals mitigate such challenges.
Designing AI-Driven Data Strategies:Certified professionals play a crucial role in designing AI-driven data strategies that optimize performance and align with regulatory standards.
Career Advancement:As AI-powered data solutions become essential for businesses, this certification provides professionals with a competitive edge in advancing their careers.
Course Overview
Course Introduction Preview
Module 1: Foundations of Data Science
1.1 Introduction to Data Science
1.2 Data Science Life Cycle
1.3 Applications of Data Science
Module 2: Foundations of Statistics
2.1 Basic Concepts of Statistics
2.2 Probability Theory
2.3 Statistical Inference
Module 3: Data Sources and Types
3.1 Types of Data
3.2 Data Sources
3.3 Data Storage Technologies
Module 4: Programming Skills for Data Science
4.1 Introduction to Python for Data Science
4.2 Introduction to R for Data Science
Module 5: Data Wrangling and Preprocessing
5.1 Data Imputation Techniques
5.2 Handling Outliers and Data Transformation
Module 6: Exploratory Data Analysis (EDA)
6.1 Introduction to EDA
6.2 Data Visualization
Module 7: Generative AI Tools for Deriving Insights
7.1 Introduction to Generative AI Tools
7.2 Applications of Generative AI
Module 8: Machine Learning
8.1 Introduction to Supervised Learning Algorithms
8.2 Introduction to Unsupervised Learning
8.3 Different Algorithms for Clustering
8.4 Association Rule Learning with Implementation
Module 9: Advance Machine Learning
9.1 Ensemble Learning Techniques
9.2 Dimensionality Reduction
9.3 Advanced Optimization Techniques
Module 10: Data-Driven Decision-Making
10.1 Introduction to Data-Driven Decision Making
10.2 Open Source Tools for Data-Driven Decision Making
10.3 Deriving Data-Driven Insights from Sales Dataset
Module 11: Data Storytelling
11.1 Understanding the Power of Data Storytelling
11.2 Identifying Use Cases and Business Relevance
11.3 Crafting Compelling Narratives
11.4 Visualizing Data for Impact
Module 12: Capstone Project - Employee Attrition Prediction
12.1 Project Introduction and Problem Statement
12.2 Data Collection and Preparation
12.3 Data Analysis and Modeling
12.4 Data Storytelling and Presentation
Optional Module: AI Agents for Data Analysis
1. Understanding AI Agents
2. Case Studies
3. Hands-On Practice with AI Agents
Tools you will explore
Google Colab
MLflow
Alteryx
KNIME
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.
Instructor-led OR Self-paced course + Official exam + Digital badge
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.
€530
E-Learning
max 999
5 dagen
AI+ Security Practitioner™
Nieuwegein
ma 4 jan. 2027
Empowering Cybersecurity with AI
This certification validates foundational knowledge of AI-driven cybersecurity concepts and assesses understanding of security principles, threats, and controls. The exam evaluates competency in applying core cybersecurity knowledge within AI-enabled environments.
Cybersecurity Professionals: Those who want to enhance their security skills by applying AI techniques for threat detection, analysis, and response.
Security Analysts: Those who work with security monitoring, incident investigation, vulnerability assessment, and AI-assisted security operations.
IT Professionals: Those who want to understand how AI can strengthen infrastructure protection, risk management, and security workflows.
Network and System Administrators: Those who manage digital environments and want to use AI tools to improve security monitoring and protection.
Risk and Compliance Professionals: Those who want to explore AI-driven approaches for security governance, compliance management, and risk assessment.
Ethical Hackers and Security Researchers: Those who want to apply AI capabilities to identify vulnerabilities, analyse threats, and improve defensive strategies.
AI and Technology Professionals: Those who want to understand the security challenges of AI systems and implement safer AI solutions.
Security Managers and Decision Makers: Those who want to leverage AI insights to improve cybersecurity strategies, operational efficiency, and organizational resilience.
Comprehensive Competency Assessment:Validates foundational knowledge of AI-powered cybersecurity concepts.
Practical Knowledge Assessment:Demonstrates foundational competency in security concepts and practices.
Current Industry Standards:Exam includes scenario-based questions reflecting real-world security situations.
Boost Strategic Decision:Making with AI Analytics — Validates readiness for entry-level to intermediate cybersecurity roles.
AI-Driven Threat Detection:Validates essential knowledge and competency required for modern security teams.
Module 1: Computing, Linux, and Operating System Foundations
1.1 Computing Fundamentals
1.2 Linux Essentials
1.3 Access Control Concepts
Module 2: Networking Fundamentals and Traffic Analysis
2.1 Internet and Networking Basics
2.2 TCP/IP and Common Protocols
2.3 Network Security Concepts
Module 3: Python for Security and Automation
3.1 Python Fundamentals
3.2 Python for Security
3.3 Security Data Analysis
Module 4: Cybersecurity Foundations and Threat Landscape
4.1 Core Cybersecurity Concepts
4.2 Common Cyber Threats
4.3 Security Frameworks
Module 5: Cryptography, Authentication & Identity Security
5.1 Cryptography Basics
5.2 Authentication and Identity
5.3 Identity Security Risks
Module 6: Introduction to Artificial Intelligence and Machine Learning
6.1 AI and ML Fundamentals
6.2 Core Machine Learning Concepts
6.3 AI in Cybersecurity
Module 7: AI Applied to Security Detection and Threat Hunting
7.1 AI-Based Detection Concepts
7.2 Threat Hunting Concepts
7.3 AI in SOC Operations
Module 8: AI Security, LLM Security and Responsible AI
8.1 LLM and Generative AI Basics
8.2 OWASP LLM Top 10
8.3 Responsible AI and Governance
Module 9: Offensive Security for AI Systems
9.1 AI Threat Modeling
9.2 AI System Attacks
9.3 AI Red Teaming Concepts
Module 10: Security Operations, Incident Response and Malware Analysis
10.1 SOC and Incident Response Basics
10.2 Malware and Threat Analysis
10.3 AI-Assisted SOC Operations
Module 11: Governance, Compliance and Ethical AI Security
11.1 Governance and Risk Management
11.2 Compliance and Privacy
11.3 Ethical and Responsible Security
Module 12: Capstone Project — AI-Driven Security Operations and Defense
12.1 Phase 1: Reconnaissance and Environmental Review
12.2 Phase 2: Detection and Threat Analysis
12.3 Phase 3: AI Security Assessment
12.4 Phase 4: Incident Response
12.5 Phase 5: Governance and Reporting
Optional Module: AI Agents for AI+ Security Practitioner
1.1 What Are AI Agents?
1.2 Key Capabilities of AI Agents in Cyber Security
1.3 Applications and Trends for AI Agents in Cyber Security
1.4 How Does an AI Agent Work?
1.5 Core Characteristics of AI Agents
1.6 Types of AI Agents
Tools you will explore
Scikit-learn
TensorFlow
PyTorch
Kali Linux
Wireshark
Nmap
Wazuh
Splunk
OWASP ZAP
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.
Instructor-led OR Self-paced course + Official exam + Digital badge
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.
€995
Klassikaal
max 12
1 dag
AI+ Security Practitioner™ eLearning
Empowering Cybersecurity with AI
This certification validates foundational knowledge of AI-driven cybersecurity concepts and assesses understanding of security principles, threats, and controls. The exam evaluates competency in applying core cybersecurity knowledge within AI-enabled environments.
Cybersecurity Professionals: Those who want to enhance their security skills by applying AI techniques for threat detection, analysis, and response.
Security Analysts: Those who work with security monitoring, incident investigation, vulnerability assessment, and AI-assisted security operations.
IT Professionals: Those who want to understand how AI can strengthen infrastructure protection, risk management, and security workflows.
Network and System Administrators: Those who manage digital environments and want to use AI tools to improve security monitoring and protection.
Risk and Compliance Professionals: Those who want to explore AI-driven approaches for security governance, compliance management, and risk assessment.
Ethical Hackers and Security Researchers: Those who want to apply AI capabilities to identify vulnerabilities, analyse threats, and improve defensive strategies.
AI and Technology Professionals: Those who want to understand the security challenges of AI systems and implement safer AI solutions.
Security Managers and Decision Makers: Those who want to leverage AI insights to improve cybersecurity strategies, operational efficiency, and organizational resilience.
Comprehensive Competency Assessment:Validates foundational knowledge of AI-powered cybersecurity concepts.
Practical Knowledge Assessment:Demonstrates foundational competency in security concepts and practices.
Current Industry Standards:Exam includes scenario-based questions reflecting real-world security situations.
Boost Strategic Decision:Making with AI Analytics — Validates readiness for entry-level to intermediate cybersecurity roles.
AI-Driven Threat Detection:Validates essential knowledge and competency required for modern security teams.
Module 1: Computing, Linux, and Operating System Foundations
1.1 Computing Fundamentals
1.2 Linux Essentials
1.3 Access Control Concepts
Module 2: Networking Fundamentals and Traffic Analysis
2.1 Internet and Networking Basics
2.2 TCP/IP and Common Protocols
2.3 Network Security Concepts
Module 3: Python for Security and Automation
3.1 Python Fundamentals
3.2 Python for Security
3.3 Security Data Analysis
Module 4: Cybersecurity Foundations and Threat Landscape
4.1 Core Cybersecurity Concepts
4.2 Common Cyber Threats
4.3 Security Frameworks
Module 5: Cryptography, Authentication & Identity Security
5.1 Cryptography Basics
5.2 Authentication and Identity
5.3 Identity Security Risks
Module 6: Introduction to Artificial Intelligence and Machine Learning
6.1 AI and ML Fundamentals
6.2 Core Machine Learning Concepts
6.3 AI in Cybersecurity
Module 7: AI Applied to Security Detection and Threat Hunting
7.1 AI-Based Detection Concepts
7.2 Threat Hunting Concepts
7.3 AI in SOC Operations
Module 8: AI Security, LLM Security and Responsible AI
8.1 LLM and Generative AI Basics
8.2 OWASP LLM Top 10
8.3 Responsible AI and Governance
Module 9: Offensive Security for AI Systems
9.1 AI Threat Modeling
9.2 AI System Attacks
9.3 AI Red Teaming Concepts
Module 10: Security Operations, Incident Response and Malware Analysis
10.1 SOC and Incident Response Basics
10.2 Malware and Threat Analysis
10.3 AI-Assisted SOC Operations
Module 11: Governance, Compliance and Ethical AI Security
11.1 Governance and Risk Management
11.2 Compliance and Privacy
11.3 Ethical and Responsible Security
Module 12: Capstone Project — AI-Driven Security Operations and Defense
12.1 Phase 1: Reconnaissance and Environmental Review
12.2 Phase 2: Detection and Threat Analysis
12.3 Phase 3: AI Security Assessment
12.4 Phase 4: Incident Response
12.5 Phase 5: Governance and Reporting
Optional Module: AI Agents for AI+ Security Practitioner
1.1 What Are AI Agents?
1.2 Key Capabilities of AI Agents in Cyber Security
1.3 Applications and Trends for AI Agents in Cyber Security
1.4 How Does an AI Agent Work?
1.5 Core Characteristics of AI Agents
1.6 Types of AI Agents
Tools you will explore
Scikit-learn
TensorFlow
PyTorch
Kali Linux
Wireshark
Nmap
Wazuh
Splunk
OWASP ZAP
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.
Instructor-led OR Self-paced course + Official exam + Digital badge
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.
€225
E-Learning
max 999
1 dag
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.
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
Tools you will explore
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.
Instructor-led OR Self-paced course + Official exam + Digital badge
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.
€3.930
Klassikaal
max 12
5 dagen
AI+ Security Expert™ eLearning
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.
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
Tools you will explore
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.
Instructor-led OR Self-paced course + Official exam + Digital badge
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.
€530
E-Learning
max 999
5 dagen
AI+ Security Strategist™
Validate Your Expertise in Cybersecurity
This certification validates advanced-level expertise in AI-driven cybersecurity strategy, governance, and risk management. The exam assesses deep knowledge of advanced security architectures, AI-enabled threat intelligence, and strategic security decision-making within complex enterprise environments.
Cybersecurity Professionals: Individuals looking to enhance their skills in compliance and security management.
Risk Management Specialists: Those interested in improving risk assessment and mitigation strategies using AI.
Compliance Officers: Professionals responsible for ensuring adherence to regulatory standards who want to leverage AI for compliance processes.
IT Security Analysts: Analysts seeking to integrate AI technologies into their security practices and frameworks.
Ethical Hackers and Penetration Testers: Individuals wanting to explore AI techniques for identifying vulnerabilities, defending against adversarial attacks, and stress-testing systems.
Tech-Savvy Leaders: IT managers or security architects aiming to future-proof their organizations with AI-enhanced compliance, governance, and security practices.
Aspiring AI Security Experts: Learners with foundational knowledge in AI and cybersecurity eager to master AI-powered solutions for emerging threats and advanced security challenges.
IoT Security Using AI:Demonstrates advanced competency in AI-powered security architectures and controls.
Deep learning algorithms:Exam includes advanced scenario-based assessments focused on strategic cyber defense decision-making.
AI-Driven Network Security:Advanced exam scenarios focused on enterprise-level security challenges.
Endpoint Protection with AI:Validates readiness for executive and CISO-level cybersecurity leadership roles.
Module 1: Foundations of AI and ML for Security Engineering
This module equips you to implement cutting-edge AI-driven security solutions. You’ll explore core algorithms like neural networks, advanced NLP techniques, and deep learning models to analyze security logs. The module also guides you on designing AI pipelines, managing imbalanced datasets, and mitigating adversarial threats, ensuring that your security systems remain adaptive and robust against evolving cyber risks.
Module 2: ML for Threat Detection and Response
This module provides practical expertise in applying supervised and unsupervised learning methods for tasks such as malware classification, anomaly detection, and real-time threat response. You’ll also learn to build advanced pipelines, optimize AI models, and use tools like Apache Kafka and Spark for scalable real-time solutions.
Module 3: Deep Learning for Security Applications
In this module, you’ll gain proficiency in implementing CNNs, RNNs, and hybrid models for network traffic classification, phishing detection, and intrusion analysis. Additionally, you’ll explore autoencoders for anomaly detection and adversarial training methods to strengthen defenses against manipulated inputs.
Module 4: Adversarial AI in Security
This module explores the strategies for crafting secure AI systems, including adversarial training, ensemble methods, and red teaming. You’ll also explore tools for simulating attacks and designing architectures that resist adversarial inputs while maintaining transparency and trust.
Module 5: AI in Network Security
This module teaches you to implement AI-powered IDS, anomaly detection models, and zero-trust architectures. With case studies and hands-on projects, you’ll develop skills in integrating AI into next-generation firewalls and optimizing network security for high-throughput environments.
Module 6: AI in Endpoint Security
In this module, you’ll learn to build AI-based malware detection systems, optimize models for polymorphic threats, and leverage ML for anomaly detection on endpoints. The content also covers securing IoT devices and implementing lightweight AI solutions for resource-constrained environments.
Module 7: Secure AI System Engineering
This module provides expertise in designing robust AI pipelines, incorporating cryptographic techniques, and optimizing models for real-time security. You’ll also explore frameworks for ensuring explainability, scalability, and compliance with data protection regulations.
Module 8: AI for Cloud and Container Security
This module equips you to build AI systems for cloud security, integrate tools into container orchestration platforms like Kubernetes, and deploy AI-driven solutions for serverless architectures. You’ll also explore DevSecOps practices and advanced security testing methods.
Module 9: AI and Blockchain for Security
This module offers insights into integrating AI with blockchain for transaction security, optimizing consensus mechanisms, and safeguarding smart contracts. Practical case studies showcase applications in cryptocurrency exchanges and supply chain management.
Module 10: AI in Identity and Access Management (IAM)
This module focuses on automating role-based access controls, detecting unauthorized access, and implementing AI-driven MFA systems. You’ll also explore real-world applications of reinforcement learning and AI-based fraud detection in IAM scenarios.
Module 11: AI for Physical and IoT Security
This module covers AI solutions for securing smart cities, industrial IoT, and autonomous vehicles. You’ll also learn about federated learning for decentralized security and techniques for safeguarding smart home devices against unauthorized access.
Module 12: Capstone Project – Engineering AI Security Systems
This module guides you through every step, from defining project goals and selecting datasets to integrating AI models into existing infrastructures. You’ll gain hands-on expertise in creating scalable, adaptive, and effective security solutions.
Tools you will explore
Splunk UBA
Microsoft Defender for Endpoint
Microsoft Azure AD Conditional Access
Adversarial Robustness Toolkit (ART)
CrowdStrike Falcon XDR
Palo Alto Cortex XDR
Darktrace Enterprise
Vectra for Cloud
Fortinet AI Cloud Security
Semgrep
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.
Instructor-led OR Self-paced course + Official exam + Digital badge
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.
€3.930
Klassikaal
max 12
5 dagen
AI+ Security Strategist™ eLearning
Validate Your Expertise in Cybersecurity
This certification validates advanced-level expertise in AI-driven cybersecurity strategy, governance, and risk management. The exam assesses deep knowledge of advanced security architectures, AI-enabled threat intelligence, and strategic security decision-making within complex enterprise environments.
Cybersecurity Professionals: Individuals looking to enhance their skills in compliance and security management.
Risk Management Specialists: Those interested in improving risk assessment and mitigation strategies using AI.
Compliance Officers: Professionals responsible for ensuring adherence to regulatory standards who want to leverage AI for compliance processes.
IT Security Analysts: Analysts seeking to integrate AI technologies into their security practices and frameworks.
Ethical Hackers and Penetration Testers: Individuals wanting to explore AI techniques for identifying vulnerabilities, defending against adversarial attacks, and stress-testing systems.
Tech-Savvy Leaders: IT managers or security architects aiming to future-proof their organizations with AI-enhanced compliance, governance, and security practices.
Aspiring AI Security Experts: Learners with foundational knowledge in AI and cybersecurity eager to master AI-powered solutions for emerging threats and advanced security challenges.
IoT Security Using AI:Demonstrates advanced competency in AI-powered security architectures and controls.
Deep learning algorithms:Exam includes advanced scenario-based assessments focused on strategic cyber defense decision-making.
AI-Driven Network Security:Advanced exam scenarios focused on enterprise-level security challenges.
Endpoint Protection with AI:Validates readiness for executive and CISO-level cybersecurity leadership roles.
Module 1: Foundations of AI and ML for Security Engineering
This module equips you to implement cutting-edge AI-driven security solutions. You’ll explore core algorithms like neural networks, advanced NLP techniques, and deep learning models to analyze security logs. The module also guides you on designing AI pipelines, managing imbalanced datasets, and mitigating adversarial threats, ensuring that your security systems remain adaptive and robust against evolving cyber risks.
Module 2: ML for Threat Detection and Response
This module provides practical expertise in applying supervised and unsupervised learning methods for tasks such as malware classification, anomaly detection, and real-time threat response. You’ll also learn to build advanced pipelines, optimize AI models, and use tools like Apache Kafka and Spark for scalable real-time solutions.
Module 3: Deep Learning for Security Applications
In this module, you’ll gain proficiency in implementing CNNs, RNNs, and hybrid models for network traffic classification, phishing detection, and intrusion analysis. Additionally, you’ll explore autoencoders for anomaly detection and adversarial training methods to strengthen defenses against manipulated inputs.
Module 4: Adversarial AI in Security
This module explores the strategies for crafting secure AI systems, including adversarial training, ensemble methods, and red teaming. You’ll also explore tools for simulating attacks and designing architectures that resist adversarial inputs while maintaining transparency and trust.
Module 5: AI in Network Security
This module teaches you to implement AI-powered IDS, anomaly detection models, and zero-trust architectures. With case studies and hands-on projects, you’ll develop skills in integrating AI into next-generation firewalls and optimizing network security for high-throughput environments.
Module 6: AI in Endpoint Security
In this module, you’ll learn to build AI-based malware detection systems, optimize models for polymorphic threats, and leverage ML for anomaly detection on endpoints. The content also covers securing IoT devices and implementing lightweight AI solutions for resource-constrained environments.
Module 7: Secure AI System Engineering
This module provides expertise in designing robust AI pipelines, incorporating cryptographic techniques, and optimizing models for real-time security. You’ll also explore frameworks for ensuring explainability, scalability, and compliance with data protection regulations.
Module 8: AI for Cloud and Container Security
This module equips you to build AI systems for cloud security, integrate tools into container orchestration platforms like Kubernetes, and deploy AI-driven solutions for serverless architectures. You’ll also explore DevSecOps practices and advanced security testing methods.
Module 9: AI and Blockchain for Security
This module offers insights into integrating AI with blockchain for transaction security, optimizing consensus mechanisms, and safeguarding smart contracts. Practical case studies showcase applications in cryptocurrency exchanges and supply chain management.
Module 10: AI in Identity and Access Management (IAM)
This module focuses on automating role-based access controls, detecting unauthorized access, and implementing AI-driven MFA systems. You’ll also explore real-world applications of reinforcement learning and AI-based fraud detection in IAM scenarios.
Module 11: AI for Physical and IoT Security
This module covers AI solutions for securing smart cities, industrial IoT, and autonomous vehicles. You’ll also learn about federated learning for decentralized security and techniques for safeguarding smart home devices against unauthorized access.
Module 12: Capstone Project – Engineering AI Security Systems
This module guides you through every step, from defining project goals and selecting datasets to integrating AI models into existing infrastructures. You’ll gain hands-on expertise in creating scalable, adaptive, and effective security solutions.
Tools you will explore
Splunk UBA
Microsoft Defender for Endpoint
Microsoft Azure AD Conditional Access
Adversarial Robustness Toolkit (ART)
CrowdStrike Falcon XDR
Palo Alto Cortex XDR
Darktrace Enterprise
Vectra for Cloud
Fortinet AI Cloud Security
Semgrep
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.
Instructor-led OR Self-paced course + Official exam + Digital badge
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.
€530
E-Learning
max 999
5 dagen
AI+ Ethical Hacker Practitioner™
Nieuwegein
ma 15 feb. 2027
Protect Digital Landscapes: Harness AI-Enhanced Technologies
The AI+ Ethical Hacker Practitioner™ certification validates knowledge of the intersection of cybersecurity and artificial intelligence, a pivotal juncture in an era of rapid technological progress. Designed for cybersecurity professionals and ethical hacking practitioners, it assesses comprehensive knowledge of AI’s impact on digital offense and defense strategies. Unlike conventional ethical hacking certifications, this certification validates competency in applying AI techniques to enhance cybersecurity approaches. It is intended for professionals seeking to validate expertise in the integration of advanced AI methods with ethical hacking practices in a rapidly evolving digital landscape.
Cybersecurity Professionals: Those looking to enhance their skills in proactive defense and AI-driven threat detection.
Ethical Hackers: Individuals focused on mastering advanced hacking techniques and staying ahead of emerging cybersecurity threats.
Technology Leaders and Decision Makers: Executives and managers aiming to understand how AI and ethical hacking can secure their organizations.
Aspiring Students: Learners interested in building a career in cybersecurity, gaining foundational knowledge and practical skills in ethical hacking.
Stay Ahead of Technological AdvancementsValidates capability in AI is transforming cybersecurity, enabling you to stay at the forefront of evolving threats.
Bridge the Gap Between AI and CybersecurityValidates competency in combining AI techniques with ethical hacking to improve digital defense strategies.
Boost Career OpportunitiesExam includes scenario-based evaluation of AI-driven security methods, validating competency in addressing real-world cyber threats.
Future-Proof Your SkillsValidates expertise in AI-powered ethical hacking, positioning you as an expert in a rapidly advancing digital landscape.
Module 1: Cybersecurity, Ethical Hacking, and AI Foundations
1.1 Cybersecurity Fundamentals
1.2 Ethical Hacking Fundamentals
1.3 Core Security Frameworks
1.4 AI in Cybersecurity
1.5 Safe and Responsible AI Usage
Module 2: Networking, Linux, Web Technologies, and Reconnaissance Fundamentals
2.1 Networking Fundamentals
2.2 Linux Fundamentals for Ethical Hacking
2.3 Web Application Fundamentals
2.4 Reconnaissance Fundamentals
2.5 AI-Assisted Reconnaissance
Module 3: AI-Enhanced Reconnaissance, Enumeration, and Attack Surface Mapping
3.1 Reconnaissance Methodology
3.2 Enumeration Fundamentals
3.3 Cloud Reconnaissance
3.4 API Reconnaissance
3.5 AI for Attack Surface Mapping
Module 4: Vulnerability Assessment and Risk Prioritization
4.1 Vulnerability Assessment Basics
4.2 Vulnerability Scanning
4.3 Vulnerability Validation
4.4 AI-Assisted Vulnerability Analysis
4.5 Remediation Planning
Module 5: AI-Assisted Penetration Testing: Web, API, Password, Cloud, and AI Application Security
5.1 Penetration Testing Lifecycle
5.2 Web Application Security
5.3 API Security Testing Fundamentals
5.4 Password Security
5.5 Cloud and Container Pentesting Concepts
5.6 AI in Penetration Testing
Module 6: LLM, RAG, and AI Application Security
6.1 LLM Application Architecture
6.2 OWASP LLM Top 10
6.3 Prompt Injection Fundamentals
6.4 RAG Security (Retrieval-Augmented Generation)
6.5 AI Agent Security
6.6 Defensive Controls
Module 7: Machine Learning for Cybersecurity and Adversarial AI
7.1 Machine Learning Fundamentals
7.2 Supervised Learning for Security
7.3 Unsupervised Learning for Security
7.4 Natural Language Processing (NLP) for Cybersecurity
7.5 Adversarial Machine Learning
7.6 Explainable AI (XAI)
Module 8: Threat Hunting, Detection Engineering, and AI-Driven SOC Operations
8.1 Security Operations Center (SOC)
8.2 Threat Hunting Fundamentals
8.3 Detection Engineering
8.4 AI in the Security Operations Center (SOC)
8.5 Threat Intelligence
Module 9: Incident Response, Digital Forensics, and AI-Assisted Investigations
9.1 Incident Response Lifecycle
9.2 Incident Classification
9.3 Evidence Handling
9.4 Digital Forensics Basics
9.5 AI-Assisted Incident Response
9.6 AI-Specific Incident Response
Module 10: Identity Security, Zero Trust, Cloud Security, and Non-Human Identity
10.1 Identity and Access Management (IAM) Basics
10.2 Zero Trust Fundamentals
10.3 Privileged Access Management (PAM)
10.4 Non-Human Identity Security
10.5 Cloud Security Fundamentals
10.6 AI Agent Identity Controls
Module 11: AI Governance, Compliance, Risk Management, and Responsible Disclosure
11.1 AI Governance Fundamentals
11.2 NIST AI RMF and Generative AI Profile
11.3 Privacy and Compliance
11.4 AI Ethics
11.5 Responsible Disclosure
11.6 AI Security Reporting
Module 12: Capstone Project - End-to-End AI-Assisted Ethical Hacking and Defense Engagement
12.1 Scoping and Rules of Engagement
12.2 Reconnaissance and OSINT
12.3 Vulnerability Assessment
12.4 AI-Assisted Penetration Testing
12.5 LLM and AI Application Security Testing
12.6 Detection Engineering and Threat Hunting
12.7 Incident Response
12.8 Governance and Reporting
Optional Module: AI Agents for Ethical Hacking
1.1 What Are AI Agents?
1.2 Applications and Trends of AI Agents
1.3 The Importance of AI Agents in Ethical Hacking
1.4 How Does an AI Agent Work?
1.5 Characteristics of AI Agents in Ethical Hacking
1.6 Types of AI Agents
Tools you will explore
Acunetix
Wapiti
Nessus
OWASP ZAP
HackerGPT
Cobalt Strike
Shodan
Wazuh
Sumo Logic
YARA Rules
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.
Instructor-led OR Self-paced course + Official exam + Digital badge
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.
€3.930
Klassikaal
max 12
5 dagen