Shubham Pathak

Gaining in-depth exposure to offensive security tools, threat modeling, and attack simulation techniques.
Participated in live red team exercises across various network and application environments.
Conducting vulnerability assessments, privilege escalation, social engineering, and post-exploitation analysis.

This achievement marks a significant milestone in my journey toward mastering cybersecurity and forensic analysis.
Key Learnings & Achievements:
1.Hands-on experience with Kali Linux tools for forensic investigations.
2.Conducted data recovery and evidence analysis from compromised systems.
3.Explored network traffic analysis to detect security breaches.

Major Projects:
1. Digital Evidence Extraction: Used forensic tools to recover deleted files and analyze metadata from compromised systems.
2. Network Forensics: Investigated packet captures to identify malicious activities and potential security threats.
3. Incident Response Simulation: Simulated cyberattacks and developed strategies for forensic analysis and threat mitigation.

This internship was an incredible opportunity to gain hands-on experience in cybersecurity, ethical hacking, and threat analysis.
Key Learnings & Achievements:
# Conducted penetration testing to identify and exploit security vulnerabilities.
# Performed vulnerability assessments using industry-standard tools.
# Enhanced skills in cryptography, OSINT, and security automation.
Major Projects:
# Red Team vs. Blue Team Simulation – Participated in cybersecurity attack-defense scenarios.
# Social Engineering & OSINT – Collected intelligence for ethical hacking using open-source tools.

This internship allowed me to explore cutting-edge AI technologies, machine learning models, and NLP applications while working on real-world AI-based projects.

Key Learnings & Achievements:
# Explored deep learning concepts with TensorFlow and PyTorch.

Major Projects:
# Landmark Detection System – Developed an AI model to detect and classify landmarks using computer vision techniques. Utilized Deep Learning (CNNs), OpenCV, and TensorFlow to enhance accuracy and efficiency.