xeeshanajmal - Overview

Zeeshan Ajmal

Doctoral Researcher · Quantum Cybersecurity & AI · Cyber Exposure & Vulnerability Lead

About

I am a doctoral researcher in Computer Science at the University of Oulu, focusing on quantum cybersecurity and the use of AI/ML and agentic AI to detect and mitigate security threats.

Alongside my research, I work as a Cyber Exposure and Vulnerability Lead in Oulu, where I help improve visibility into internet-facing assets, identify critical exposures, and work with engineering teams to reduce risk in production environments.

My background combines:

Security engineering and penetration testing
Quantum computing and secure AI systems
Cloud security across AWS, Azure, and GCP
Threat intelligence, vulnerability management, and compliance

Current Roles

Doctoral Researcher – Quantum Cybersecurity

University of Oulu

PhD research on quantum cybersecurity using AI/ML and agentic AI
Completed master’s thesis:
“Using artificial intelligence and machine learning to detect malicious quantum circuits”
Focus areas: malicious quantum circuit detection, secure quantum software development, and LLM-based reasoning for secure quantum workflows

Cyber Exposure & Vulnerability Lead – Oulu

Improve the NCSC Feed and exposure monitoring for internet-facing assets
Map, track, and document vulnerabilities across products and services
Use tools such as Nmap, Masscan, ProjectDiscovery, Shodan, Wireshark, Metasploit, and CVE/CWE analysis
Work closely with product and engineering teams to prioritize remediation and improve secure-by-design practices
Align work with security frameworks such as MITRE ATT&CK and common regulatory / compliance needs

Research & Professional Interests

Quantum cybersecurity and secure quantum software
AI-powered threat detection and security automation
LLM and agentic AI security (prompt injection, data exfiltration, jailbreaks)
Secure AI systems (adversarial ML, privacy-aware ML)
Internet-wide exposure mapping and attack surface management
Cloud security (Azure, AWS, GCP) and DevSecOps

Skills & Tools

Programming & Data

Python, Bash, SQL
Data analysis and visualization

AI, ML & LLMs

Classical ML for security analytics
LLM-based applications and agentic workflows
Applied use of frameworks such as LangChain / similar LLM stacks

Quantum Computing

Qiskit and quantum circuit experimentation
Research on malicious quantum circuits and secure quantum protocols

Security Engineering & Offensive Security

Network and web application penetration testing
Threat modeling, risk analysis, and secure architecture review
Tools: Nmap, Masscan, Shodan, Burp Suite, Metasploit, Nessus, Wireshark, ProjectDiscovery stack, OpenVAS, SIEM platforms

Cloud & DevSecOps

AWS, Azure, GCP
Docker, container security, CI/CD integration (e.g., GitHub Actions)

Publications & Thesis

Master’s Thesis (University of Oulu)
Using artificial intelligence and machine learning to detect malicious quantum circuits
Focus on combining quantum computing with ML-based detection of malicious behavior in quantum circuits.
Ongoing PhD Research
Quantum cybersecurity with AI/ML and agentic AI, including secure development practices and automated detection of misuse in quantum and AI systems.

Collaboration

I am open to:

Research collaboration in quantum cybersecurity, secure AI, and threat detection
Joint security projects and tooling (exposure management, LLM security, quantum-safe designs)
Guest talks, seminars, and workshops on cybersecurity, AI, and quantum topics

You can reach me via email or LinkedIn.