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.
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Digital evidence, forensic artefacts, incident timelines, and malware behaviour. |
Secure systems, observable infrastructure, least privilege, and incident response. |
Agentic AI, LLM guardrails, adversarial evaluation, and trustworthy telemetry. |
Security research translated into practical, reviewable engineering.
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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
Data and Infrastructure
View 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.
