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100% fundable

AI+ Ethical Hacker™

Those who truly want to protect IT systems need to think like attackers – and use AI to uncover vulnerabilities before they can be exploited. This training gives you exactly that perspective.

Key topics

  • Conduct AI-assisted security analyses and systematically prioritize vulnerabilities.
  • Understand defensive security measures – from classic techniques to automated countermeasures.
  • Train threat detection with intelligent tools and evaluate attack data in a structured way.
  • Develop risk-based assessments of digital threats and derive effective countermeasures.
  • Apply generative AI in simulation scenarios and design security tests that reflect real-world conditions.

Prerequisite
A basic understanding of IT environments and networks makes it easier to get started. Openness to AI-assisted working methods matters more than extensive prior experience.

Target group
Developers, security professionals, and IT consultants who want to take their attack and defense capabilities to the next level with AI support.

Ethical hacking meets AI: after this training, you will detect threats faster, analyze attack vectors with precision, and build protection concepts that hold up.

Course content
  • Concepts, goals, and legal foundations of ethical hacking
  • Phases and methodologies of the ethical hacking process
  • Hacker categories and their different attack motivations
  • Techniques for information gathering and analysis
  • Footprinting strategies and passive reconnaissance methods
  • Network scanning and active discovery techniques
  • Enumeration approaches and system profiling
  • Compliance requirements and regulatory frameworks
  • Application areas of AI in ethical hacking
  • Core concepts and algorithms of artificial intelligence
  • Machine learning as an analytical tool in cybersecurity
  • Natural Language Processing (NLP) for security-related tasks
  • Deep learning for automated attack detection
  • Adversarial machine learning: principles and risks
  • AI-driven threat intelligence systems in action
  • Automating security processes with AI
  • Overview of AI technologies and their application areas
  • AI-based solutions for automated threat detection
  • Overview of machine learning frameworks and their use cases
  • Penetration testing tools with integrated AI support
  • Behavior-based analysis tools for anomaly detection
  • Strengthen network security with AI-powered monitoring systems
  • Automated scanners for vulnerability and security assessments
  • AI in the context of web application security
  • Malware analysis and detection using AI methods
  • Understanding and applying cognitive security systems
  • Reconnaissance as a phase in the ethical hacking process
  • Comparing classic and AI-assisted reconnaissance methods
  • OS fingerprinting using AI techniques
  • AI-optimized port scanning and reachability analysis
  • Automating network mapping with machine learning
  • AI-assisted techniques in social engineering
  • OSINT analysis using machine learning approaches
  • DNS enumeration and target profiling with AI
  • Deploying and evaluating AI-based vulnerability scanners
  • Overview of penetration testing tools with AI integration
  • Refining exploit techniques with machine learning
  • Dynamic application security testing (DAST) with AI support
  • Fuzz testing with AI-driven approaches
  • Applying adversarial machine learning in penetration testing scenarios
  • Automated reporting through AI-powered tools
  • Threat modeling using AI-based methods
  • Ethical and legal considerations in AI-assisted pentesting
  • Supervised learning for classifying security threats
  • Unsupervised learning for anomaly detection in network data
  • Reinforcement learning for adaptive security responses
  • Using NLP to evaluate structured threat intelligence
  • Applying behavior-based models for threat assessment
  • Ensemble learning for more precise threat prediction
  • Feature engineering as a foundation for security models
  • Machine learning in endpoint security
  • Explainable AI for transparent security decisions
  • Using behavioral biometrics for secure user authentication
  • Analyzing user behavior data with ML models
  • Monitoring and interpreting network traffic through behavioral analysis
  • Strengthening endpoint security with behavior-oriented monitoring
  • Time-series analysis for detecting anomaly patterns
  • Applying heuristic methods in anomaly detection
  • AI-based threat hunting during live operations
  • Understanding and applying User and Entity Behavior Analytics (UEBA)
  • Limitations and challenges of behavior-based security models
  • Conducting automated threat assessment with AI methods
  • Classifying attack patterns using machine learning
  • Integrating real-time threat intelligence into security systems
  • Preparing predictive analyses for early incident response
  • Applying AI-based forensic analysis to security incidents
  • Planning automated containment and recovery measures
  • Behavioral analysis as part of the incident response strategy
  • Continuous learning from feedback loops in security operations
  • Organizing human-AI collaboration in incident handling
  • Understanding and applying AI-driven authentication methods
  • Using behavioral biometrics for secure access control
  • Integrating anomaly detection into IAM systems with ML approaches
  • Adapting dynamic access policies through machine learning
  • Securing Privileged Access Management (PAM) with AI support
  • Implementing continuous authentication using ML models
  • Managing automated user provisioning and access deactivation
  • Implementing risk-based authentication with AI-driven models
  • Identity Governance and Administration (IGA) with AI support
  • Understanding and defending against adversarial attacks on AI models
  • Security standards for training and deploying AI models
  • Ensuring data protection and data security in AI-based systems
  • Securely planning and implementing AI application deployments
  • Establishing explainability and interpretability as security features
  • Strengthening robustness and resilience in AI architectures
  • Secure sharing and collaborative use of AI models
  • Setting up monitoring and threat detection for AI systems
  • Ethical decision-making in security-critical AI systems
  • Identifying and reducing bias and fairness issues in AI models
  • Anchoring transparency and accountability as ethical requirements
  • Ensuring data privacy in AI-driven security solutions
  • Responsibility and accountability in AI security deployments
  • Ethical questions around threat intelligence sharing
  • Protecting human and digital rights in AI contexts
  • Understanding legal and ethical frameworks for AI use
  • Responsible disclosure and responsible handling of security vulnerabilities
  • Case study 1: AI-assisted threat detection and response planning
  • Case study 2: Implementing ethical hacking with AI methods in practice
  • Case study 3: Building and securing IAM systems with AI support
  • Case study 4: Securely deploying and operating AI applications
  • Architecture and functionality of autonomous AI agents
  • Application examples and case studies from security practice
  • Hands-on exercises: exploring AI agents in security contexts

Up to 100% funding for your continuing education

Attractive funding opportunities are available for numerous continuing education programs – depending on the funding program, up to 100% funding is possible.
The requirement is that you live or work in Germany. Our funding opportunities are not available outside of Germany.

Funding opportunities are available for career starters, job seekers, career changers, startups, experienced professionals, those seeking career advancement, and specialists.