Program Overview

MSc in Applicable AI & Cybersecurity

Program Overview

The MSc in Applicable AI & Cybersecurity is designed to equip students with the skills to design, secure, and govern intelligent systems within a rapidly evolving digital landscape. Participants will develop expertise in creating modern AI solutions, analyzing intricate security risks, and constructing robust digital infrastructures. Through practical projects and the use of advanced tools, students will collaborate with experts to cultivate skills essential for spearheading innovation at the intersection of AI and cybersecurity.

Accreditation

This program is state-recognized and fully accredited in Germany, with the national accreditation process anticipated to be finalized during the standard study period for the inaugural cohort.

Program Details

• Duration: 4 Semesters
• Location: Berlin
• Format: Full Time
• Language: Taught in English
• Credits: 120 ECTS
• Academic Intake: October, January, April, July
• Total Tuition Fees: 23,800 (excluding a one-time 250 admission fee and a 250 examination fee)
• Scholarships Available: Up to 30%

Learning Objectives

Students will gain competence in the following areas:

• Design, implementation, and operation of modern AI systems in secure cloud environments using DevSecOps practices.
• Identification, analysis, and mitigation of security risks in AI systems, applying techniques such as adversarial machine learning and model red teaming.
• Collection and analysis of security-related data utilizing advanced security analytics for informed decision-making.
• Application of risk modeling, reliability engineering, and incident analysis to enhance the resilience of complex digital systems.
• Integration of regulatory frameworks like EU AI regulations and data protection into technology and product development processes.

Target Audience

This program is intended for:

• Technology and data enthusiasts interested in AI and cybersecurity.
• Professionals seeking to enhance their knowledge of secure AI systems, digital resilience, and risk management.
• Individuals aiming to influence responsible AI use and compliance with evolving technological regulations.
• Future leaders aspiring to drive innovation and digital transformation within technology-oriented organizations.

Career Prospects

Graduates may pursue positions such as:

• AI Security Engineer
• AI Systems Architect
• Cybersecurity Analyst
• AI Risk & Governance Specialist
• Machine Learning Security Engineer
• Security Data Analyst

Curriculum

Semester 1

• Applied Machine Learning for Security Analytics
• Cybersecurity Foundations & Threat Modeling
• Secure Software Engineering & Secure Coding
• Data Engineering for AI Systems
• AI Governance, Ethics & Compliance
• Project Study Work

Semester 2

• Philosophy of Science and Research Methods
• Deep Learning & Multimodal AI
• Secure Cloud Architectures & DevSecOps
• AI-driven Threat Detection & Incident Response
• Cryptography, Zero Trust & Identity
• AI Security I: Adversarial ML, LLM Security & Red Teaming

Semester 3

• LLM Engineering: RAG, Tool Use, and Enterprise Knowledge Systems
• Agentic AI & Secure Automation
• MLOps/LLMOps & Reliable AI Operations
• Privacy-Preserving Machine Learning
• Human-Robot & Cyber-Physical Systems Security
• Study Work

Semester 4

• Secure Data Products & Knowledge Graphs
• Industry / Transfer Project
• Master Thesis

Admission Requirements

Educational Background

• A state-recognized university degree in computer science, IT, computer engineering, data science, or related fields with at least 180 ECTS credits, or an equivalent degree from an accredited university.

Language Proficiency

• English proficiency must be demonstrated through standardized tests:
  • IELTS: 6.0
  • PTE: 56
  • TOEFL: 80
  • Duolingo: 100
  • LANGUAGECET: 65
  • Cambridge Advanced or Proficiency

Required Documents

• An updated CV
• An official copy of the university degree
• An academic transcript

Work Experience

• No work experience is required for admission.

Specialized or Additional Requirements

• Applicants with related but not directly comparable degrees may be admitted conditionally. Missing ECTS credits required for admission can be completed through additional modules. Proof of required credits must be submitted no later than nine months after enrollment. Qualifications must demonstrate a level 6 qualification according to the German Qualifications Framework, including at least 30 CP in subjects such as mathematics, statistics, and programming.