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1- 🔐 Cybersecurity, Social Engineering and AI Security / Main Repository

A Data-Driven Approach Integrating Web Services, Machine Learning, and Financial Data Infrastructure

Abstract

This work presents an integrated academic and applied framework for cybersecurity, social engineering analysis, and artificial intelligence system protection. The proposed approach combines data-driven methodologies, distributed systems, and machine learning techniques to address contemporary challenges in cyber risk and intelligent system security.

The environment is supported by a recently established Bloomberg Laboratory at PUC-SP, enabling access to professional financial data, APIs, and analytical tools. This infrastructure allows the integration of real-world financial datasets into cybersecurity and OSINT-oriented workflows.

The project is structured around a complete data pipeline, including data ingestion from external APIs, transformation, storage in relational databases, and exposure through RESTful services, combined with predictive modeling and anomaly detection.

Cybersecurity and Social Engineering Integrated Project - PUC-SP 5th Semester (2026)

Institution: Pontifical Catholic University of São Paulo (PUC-SP Humanistic AI & Data Science • 5º Semestre • 2026
School: FACEI - Faculty of Interdisciplinary Studies
Course Repo: INTEGRATED PROJECT: Cybersecurity and Social Engineering - 72 Hours
Professor: ✨ Eduardo Savino Gomes
Extensionist Activities: Social projects with open-source software for community support.

Note

⚠️ Heads Up

• Projects and deliverables may be made publicly available whenever possible.

• The course emphasizes practical, hands-on experience with real datasets to simulate professional consulting scenarios in the fields of Machine Learning and Neural Networks for partner organizations and institutions affiliated with the university.

• All activities comply with the academic and ethical guidelines of PUC-SP.

• Any content not authorized for public disclosure will remain confidential and securely stored in private repositories.

Table of Contents

• Overview
• System Architecture
• Security Architecture
• Methodology
• Integrated Extension Project
• Weekly Roadmap
• Applied Projects
• Technologies
• Results and Discussion
• Conclusion

Overview

This repository defines a data-driven cybersecurity framework integrating:

- Cybersecurity and social engineering
- Artificial intelligence and anomaly detection
- Web Services and distributed systems
- Financial and OSINT-based intelligence

It connects theory and practice through real-world data pipelines and secure architectures.

[🇧🇷 Português] [🇬🇧 English]

Cybersecurity, Social Engineering and AI Security

Applied Data Science, Web Intelligence and Threat Analysis @ PUC-SP

PUC-SP has recently established a dedicated Bloomberg Laboratory on campus, providing access to Bloomberg’s professional financial infrastructure, including terminals, datasets, and APIs.

This enables the integration of real-world financial data into cybersecurity, OSINT, and data intelligence applications.

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Table of Contents

• Overview
• System Architecture
• Security Architecture
• Methodology
• Integrated Extension Project
• Weekly Roadmap
• Applied Projects
• Technologies
• Results and Discussion
• Conclusion
• Keywords

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Overview

This repository defines a data-driven cybersecurity framework integrating:

• Cybersecurity and social engineering
• Artificial intelligence and anomaly detection
• Web Services and distributed systems
• Financial and OSINT-based intelligence

It connects theory and practice through real-world data pipelines and secure architectures.

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System Architecture

The system follows a multi-layered architecture:

1. Data Sources (Bloomberg APIs, external APIs, OSINT)
2. Secure Access Layer (JWT, OAuth2, RBAC)
3. Data Ingestion
4. Data Processing
5. Secure Storage (SQL / NoSQL)
6. Intelligence Layer (ML, anomaly detection)
7. Application Layer (REST APIs, dashboards)
8. Monitoring Layer

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Security Architecture (Zero Trust)

• Continuous verification
• Least privilege
• Encryption (TLS + at rest)
• OWASP protection
• Secure API gateways
• Monitoring and anomaly detection

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Methodology

CRISP-DM:

1. Business understanding
2. Data understanding
3. Data preparation
4. Modeling
5. Evaluation
6. Deployment

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Integrated Extension Project (Core Implementation)

Stage 1 — Data Analysis and Prediction

• API consumption
• Data processing and visualization
• SQL storage
• Machine learning models

Stage 2 — RESTful API

• SQL database design (≥ 3 tables)
• REST API development
• Authentication (JWT)
• Data exposure

Pipeline

Data Sources → Processing → SQL → ML → REST API → Applications

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Weekly Roadmap (Programmatic Content)

Week	Topics	Description
1	Introduction	Security concepts, course structure
2	Distributed Systems	Client-server, HTTP, REST
3	CRISP-DM	Data methodology
4	APIs	Data collection (RapidAPI)
5	Project Work	API + processing
6	Data Analysis	Pandas, NumPy, visualization
7	Dashboards	Data presentation
8	Presentation	Stage 1 results
9	Final Project	Definition
10	Big Data	Concepts
11	NoSQL	Databases
12	Hadoop	Distributed processing
13	Spark	Data processing
14	Spark	Continuation
15	Project Dev	Final system
16	Project Dev	Implementation
17	Project Dev	Finalization
18	Presentation	Final evaluation

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Applied Projects (Extended Use Cases)

Financial Anomaly Detection

• Bloomberg data
• Time-series models
• Risk detection

OSINT Threat Intelligence

• Public data
• Pattern recognition
• Threat classification

Social Engineering Analysis

• Behavioral data
• Risk modeling

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Technologies

• Python (Pandas, NumPy, Plotly)
• REST APIs (FastAPI / Flask)
• SQL / NoSQL
• Machine Learning
• Hadoop / Spark

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Results and Discussion

• Scalable pipelines
• Real-world data integration
• Improved anomaly detection
• Strong alignment with industry

Conclusion

A complete framework integrating cybersecurity, AI, and real-world data infrastructures, validated through an applied extension project.

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Keywords

Cybersecurity, Social Engineering, Machine Learning, APIs, OSINT, Zero Trust, Data Engineering

💌 Let the data flow... Ping Me !

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Copyright 2026 Quantum Software Development. Code released under the MIT license.

Objective

The course aims to develop a comprehensive understanding of vulnerabilities across both technical systems and human behavior, enabling learners to:

- Identify and analyze security weaknesses
- Understand attack methodologies
- Anticipate complex threat scenarios
- Design and implement effective defensive solutions

with a strong focus on AI-driven environments and emerging risks.

💌 Let the data flow... Ping Me !

🛸๋ My Contacts Hub

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➣➢➤ Back to Top

Copyright 2026 Quantum Software Development. Code released under the MIT license.