Sii Poland
SII UKRAINE
SII SWEDEN
Sii Poland
SII UKRAINE
SII SWEDEN
Industry-Leading Content: Our program reflects the latest threats, frameworks, and best practices from 2024–2025. You will learn using real-world case studies and modern attack scenarios.
Ready-to-Implement Operational Frameworks: You will learn structured approaches—such as MITRE ATLAS, OWASP, STRIDE, and NIST—that you can deploy in your organization immediately upon returning from the training.
Competitive Advantage: As AI security becomes a critical topic at the board level, specialists with this expertise can expect higher salaries and greater career advancement opportunities.
Upon completion of this intensive training program, you will be able to:
Identify and mitigate vulnerabilities specific to Artificial Intelligence (AI) and Machine Learning (ML) systems.
Implement secure AI development practices throughout the organization.
Conduct professional penetration tests targeting AI applications.
Build effective incident response plans designed specifically for AI security.
Apply compliance frameworks and governance structures to AI projects.
Assess and reduce supply chain risks during the development of AI solutions.
Detect and respond to adversarial attacks in real time.
“Cybersecurity & AI” is an intensive, practical training course featuring hands-on labs. It is designed for Intermediate and Advanced levels, specifically for:
Cybersecurity Specialists
IT Analysts
System Administrators
Security Architects
Master the fundamentals of securing artificial intelligence systems in the reality of 2024–2025. This module builds the foundation for understanding why traditional cybersecurity approaches are insufficient for protecting AI.
Definition and importance of AI security in today’s threat landscape.
AI system architecture: Large Language Models (LLMs), ML pipelines, API endpoints.
Key differences between conventional cybersecurity and AI security.
Overview of the threat landscape: the MITRE ATLAS framework.
Practical Lab
Discover the ten most critical security risks affecting large language models and AI applications. These vulnerabilities represent real, exploitable weaknesses that adversaries actively target.
LLM01: Prompt Injection – theory and practical exploitation.
LLM02: Insecure Output Handling.
LLM03: Training Data Poisoning.
LLM04: Model Denial of Service.
LLM05: Supply Chain Vulnerabilities.
Practical Lab
Understand how machine learning models can be deceived, corrupted, and compromised. This module explores sophisticated attack vectors aimed at the very foundations of AI systems.
Types of adversarial attacks: evasion, poisoning, and model extraction.
Model Inversion and Membership Inference attacks.
Backdoor attacks in machine learning models.
Defense mechanisms and detection strategies.
Practical Lab
Learn how artificial intelligence accelerates and enhances penetration testing capabilities. Explore the tools and methodologies modern red teamers use to identify vulnerabilities faster and more effectively.
AI tools in penetration testing processes.
Open-Source Intelligence (OSINT) automation using machine learning.
Testing processes based on RAG (Retrieval-Augmented Generation) architecture.
Automation of vulnerability detection and exploitation.
Practical Lab
Proactively identify and mitigate threats before they lead to breaches. This module teaches a structured approach to understanding risks unique to AI architectures.
STRIDE methodology applied to AI systems.
AI-specific threat libraries and frameworks.
Risk assessment and prioritization using industry tools.
Building effective threat models for chatbots and LLM applications.
Practical Lab
Supply chain attacks in AI ecosystems are among the fastest-growing threats. Learn how to secure every element of the AI development and deployment pipeline.
Supply chain attacks in the AI ecosystem.
SLSA and SCVS security frameworks.
SBOM (Software Bill of Materials) generation and model signatures.
Dependency verification in machine learning projects.
Securing containerized AI applications.
Practical Lab
Detect threats in real time using AI-driven security monitoring. Learn how to integrate intelligence across your entire infrastructure.
Threat detection and response utilizing AI.
Behavioral analytics in anomaly detection.
SIEM integration with AI tools and platforms.
Real-time monitoring of model behavior and performance.
Practical Lab
Navigate the complex regulatory landscape surrounding artificial intelligence. Ensure your AI systems comply with the latest legal and industry standards.
EU AI Act: requirements and implications.
NIST AI Risk Management Framework.
Governance structures for Responsible AI.
Documenting and auditing AI security controls.
Practical Lab: Conducting compliance audits of AI systems.
When an incident occurs, speed and precision are vital. This module prepares you to respond effectively to security incidents unique to AI.
AI-specific incident response playbooks.
Recovery procedures following a model compromise.
Forensics analysis of compromised AI systems.
Communication and escalation protocols.
Practical Scenario: Real-time incident response simulation.
ITIL® and PRINCE2® are registered trademarks of AXELOS Limited, used under permission of AXELOS Limited. All rights reserved. AgilePM® is a registered trademark of Agile Business Consortium Limited. All AgilePM® Courses are offered by Sii, an Affiliate of Eraneos Iberia S.L.U., an Accredited Training Organization of The APM Group Ltd. Lean IT® Association is a registered trademark of the Lean IT Association LLC. All rights reserved. Sii is an Affiliate of Accredited Training Organization Eraneos Iberia S.L.U. SIAM™ is a registered trademark of EXIN Holding B.V. All prices presented on the website are net prices. 23% VAT should be added.
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