Pragna R

Computer Science (AI & ML) undergraduate · Cybersecurity & AI Security

Bengaluru, Karnataka, India

@pragna_r

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🎓 Bachelor of Engineering (Computer Science & Engineering - AI & ML) at Global Academy of Technology · Graduating 2027

About

Computer Science (AI & ML) undergraduate with interests in cybersecurity, ethical hacking, artificial intelligence, and Identity & Access Management. Hands-on experience building AI-powered security applications and working with penetration testing, vulnerability assessment, network security, web application security, cloud security, and AI security concepts.

Education

Bachelor of Engineering (Computer Science & Engineering - AI & ML)

Global Academy of Technology

Computer Science & Engineering (AI & ML)

Computer Science & Engineering (AI & ML)

Skills

<3 months

C DNS VPN HTML HTTP Python TCP/IP Routing Switching JavaScript AI Security Enumeration OWASP Top 10 Deep Learning Cybersecurity Sql Injection Cloud security Reconnaissance Computer Vision Ethical Hacking Machine Learning Penetration Testing Information Security Web Application Security Vulnerability Assessment

Tools / apps / platforms

<3 months

Git Nmap GitHub Postman Firebase Wireshark Kali Linux Metasploit

Projects

Secure Authentication and Multi-Factor Authentication (MFA) Platform

JWT, TOTP

Built a secure web-based authentication platform with registration, login, password management, session handling, MFA, JWT-based authentication, and role-based access control.
TwinGuard – Explainable Continuous Risk-Based Authentication Framework

Python, Machine Learning, Explainable AI

Developed an explainable AI-driven continuous risk-based authentication framework for Zero Trust environments. Modeled user access patterns, monitored contextual signals, and implemented dynamic trust scoring and anomaly detection.
SentinelAI – Security Log Anomaly Detection

Python, Machine Learning

Developed an AI-powered security analytics platform for detecting anomalous activities from system and security logs. Built preprocessing, feature extraction, anomaly detection, and automated log ingestion workflows for real-time monitoring.
Hybrid Neuro-Symbolic Multi-Agent Framework for Auditable Autonomous Governance

Python, Multi-Agent Systems, Neuro-Symbolic AI, Explainable AI (XAI), Machine Learning

Designed a hybrid Neuro-Symbolic AI framework combining neural networks and symbolic reasoning for explainable decision-making. Developed a multi-agent architecture enabling collaborative autonomous governance and policy enforcement, with auditability and transparency mechanisms.

Courses & certifications

Texial Information Security Auditor (TISA)
Getting Started with Artificial Intelligence
Computer Networking
Cisco Networking Academy – Introduction to Cybersecurity · Cisco Networking Academy

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