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Explore Jean's repositories OWASP contributor Focus areas: DFIR, cybersecurity, and AI security
Evidence. Integrity. Resilience. Public Purpose.
I am a Digital Forensics Engineering and Cybersecurity student focused on digital investigations, incident response, secure software, and responsible AI security.
My approach is evidence-led: Understand what a system trusts, identify how it may fail, preserve what it reveals, and engineer stronger defences.
Security research translated into practical, reviewable engineering.
OWASP FinBot CTF repository AegisLLM repository SentinelML repository ZK-ML Provable Inference Verifier repository
A practical workspace for forensic workflows, evidence analysis, and repeatable investigative practice.
An intentionally vulnerable platform for learning how agentic AI systems fail and how they can be secured.
| Area | Core Capabilities |
|---|---|
| Digital Forensics and Incident Response | Evidence analysis, timeline reconstruction, memory forensics, network analysis, and chain of custody. |
| Cybersecurity | Threat modelling, vulnerability assessment, secure APIs, authentication, and defensive testing. |
| AI Security | Prompt-injection testing, agentic threat modelling, MCP security, guardrails, and telemetry. |
| Applied Security Engineering | Python, FastAPI, HMAC, Redis, Docker, testing, and observability. |
Languages and Frameworks
Python, Java, Kotlin, TypeScript, JavaScript, C, C++, Rust, FastAPI, and ReactData and Infrastructure
PostgreSQL, MySQL, Redis, MongoDB, Linux, Docker, Kubernetes, Git, GitHub, and Google CloudView Security and Forensics Tools
Wireshark • Autopsy • Volatility • Ghidra • YARA • Nmap • Burp Suite • Metasploit • OWASP • Grafana
Building security knowledge that others can inspect, test, and improve.
I contribute to OWASP FinBot CTF through the Google Summer of Code programme, with interests in agentic-AI guardrails, adversarial evaluation, security telemetry, and practical cybersecurity education.
Observe → Preserve trustworthy evidence.
Detect → Identify unsafe behaviour.
Contain → Limit permissions and impact.
Verify → Test controls against realistic threats.
Improve → Turn findings into stronger systems.
Public evidence of consistent learning and engineering practice.
Transparency: These visuals update automatically from public GitHub data. Language statistics represent repository composition, not proficiency.
- 🎓 I am a Digital Forensics Engineering and Cybersecurity student.
- 🛡️ I am developing expertise in DFIR, cybersecurity, secure AI, and resilient digital systems.
- 🌍 I am interested in public-sector innovation, institutional cybersecurity, and technology with measurable social value.
- 🤝 I welcome internships, research, mentorship, and open-source collaboration.
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Technology earns trust through evidence, integrity, security, and service.