CompTIA SecAI+ Certification

Price
Net €
VAT €

Price
Price on Request

Duration
5 days

For companies and job seekers:
this course is 100% fundable!
 

Location

Course Language
English

Training Solutions
Online Live

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Security concepts are evolving rapidly, as automated systems give rise to new attack patterns while simultaneously enabling innovative protective mechanisms. Today, those who understand IT security no longer think solely in terms of static structures, but rather in terms of learning, adaptive systems that continuously adjust themselves.

Key Topics

  • Machine learning models in cyber defense
  • Analysis of complex attack scenarios
  • Integration of intelligent security solutions
  • Data-driven decision-making processes
  • Compliance and regulatory requirements

Prerequisites
Solid foundation in IT, networks, and security principles

Target Audience
Professionals in IT security, data analysis, system architecture, and technology management

An advanced understanding of intelligent security technologies provides the foundation for identifying risks early on and building robust systems. Relevant expertise combines technical knowledge with strategic thinking and promotes sustainable security.

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Course Content
  • AI Concepts and Major Types of AI
  • Generative AI and Transformers
  • Machine learning and deep learning
  • Natural Language Processing
  • Approaches to Training AI Models
  • Fundamentals of prompt engineering
  • Security aspects of models
  • AI data types and data security techniques
  • RAG Concepts (Retrieval-Augmented Generation)
  • Data integrity and control mechanisms in data processing
  • Fundamentals of AI Threat Modeling
  • Processes and Prerequisites for Threat Modeling
  • Frameworks for AI Threat Modeling
  • Types of AI Security Controls
  • Model Security Boundaries and Prompt Templates
  • Gateway and Interface Controls
  • Usage Quotas and Restriction Controls
  • Testing Security Controls
  • Principles and Models of AI Access Control
  • Access Controls for Models and Agents
  • Security for API and Network Access
  • Security Measures for AI Data
  • Encryption and Data Security Measures
  • Monitoring and Logging of AI Systems
  • Performance and cost monitoring
  • AI audits and compliance monitoring
  • Security in the AI Lifecycle
  • Ethical Considerations in AI Design
  • Types and Techniques of AI Attacks
  • Attacks on Models via Backdoors and Trojans
  • Model poisoning and inversion
  • Risks from model theft
  • Countermeasure Strategies
  • AI Analysis Following an Incident
  • AI-powered security tools
  • AI use cases for detection and analysis
  • AI for vulnerability analysis
  • AI-powered attack vectors
  • AI for social engineering and deception
  • AI-powered reconnaissance techniques
  • AI-driven automation
  • AI in DevSecOps Workflows
  • AI-powered scripting and summarization
  • AI Governance Structures
  • Organizational Roles in the Field of AI
  • Principles for Responsible AI
  • Identification and Assessment of AI Risks
  • Regulatory Issues in AI
  • Compliance frameworks for AI
  • Designing AI policies in organizations
  • Compliance Reporting

Frequently Asked Questions

  • Greater visibility within security teams, access to AI security projects, and better opportunities for specialized roles.
  • Attacks are increasingly leveraging AI. Those who understand the risks become immediately valuable to companies.
  • Detecting AI-based threats, securing models, and addressing new attack methods.
  • For IT security professionals, analysts, and anyone looking to transition into AI security.
  • Undetected attacks, manipulated models, and serious security vulnerabilities in data-driven systems.
  • Combines traditional security with a focus on AI—a field experiencing strong growth in demand.
  • Ready for immediate use in day-to-day project work, particularly for the analysis, validation, and evaluation of AI systems.
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