Security of Data-Intensive Architectures

The course aims to:
- Introduce the fundamentals of cyber security applied to intelligent systems and distributed architectures.
- Analyze techniques for protecting semi-structured and unstructured data, with a focus on confidentiality, integrity, and authentication.
- Explore standards and authorization languages ​​for secure access to web services and network resources.
- Study AI pipelines and related security risks, including adversarial attacks and vulnerabilities in ML models.
- Deepen the methodologies for assurance and certification of intelligent systems, with a focus on statistical testing and risk assessment.
- Apply threat modeling techniques, such as STRIDE-AI, to evaluate the robustness of AI architectures.

Upon completion of the course, students will acquire skills in the following areas:
- Secure AI pipeline design
- Ability to design AI pipelines resilient to threats and attacks, with attention to each phase: data collection, training, validation, deployment.
- Vulnerability analysis in ML models
- Identification and mitigation of adversarial attacks, data poisoning, model inversion and membership inference.
- Implementation of security controls
- Application of authentication, authorization and encryption techniques in distributed and cloud-native environments.
- Model testing and assurance
- Use of statistical verification techniques and robustness tests to evaluate the security and reliability of AI models.
- Threat modeling
- Use of frameworks such as STRIDE-AI to analyze and document risks in intelligent architectures.
- Transversal skills in these areas: ability to work in interdisciplinary teams, effective technical communication, drafting technical documentation and presenting results to a technical and non-technical audience, planning, development and evaluation of a real project, with milestones, reviews and final delivery.
- Critical thinking and ethical evaluation