Walid Daboubi
Data & AI Engineering Lead
Lausanne Metropolitan Area Switzerland
Summary
👤
Senior🎓
Top SchoolWalid Daboubi is a Data & AI Engineering Lead with 12 years of experience building and productionizing AI-driven security and data products across finance, luxury, and cloud software domains. He combines hands-on engineering with strategic leadership, having founded Richemont’s Group Security Data Science team and delivered real-time cyber detection, SOAR-integrated ML, and anomaly-detection systems. An active open-source and conference contributor, he has improved high-profile security tooling such as the Neo23x0 log4shell-detector and presented AI-powered security tools at Black Hat Arsenal and DEF CON. Fluent in turning complex security problems into automated, scalable solutions, he also holds machine learning training from Stanford and a computer science engineering degree from UTC. Known for ideation and arrangement strengths, he pairs technical depth with a knack for translating business needs into impactful data products.
12 years of coding experience
12 years of employment as a software developer
Bachelor's degree Computer Engineering, Bachelor's degree Computer Engineering at Ecole Supérieure des Sciences et de TechnologieDiplôme d'ingénieur Computer Science, Diplôme d'ingénieur Computer Science at Université de Technologie de Compiègne (UTC)Machine Learning, Machine Learning at Stanford University
English, French, Arabic, Spanish
Stackoverflow
948reputation
106kreached
63answers
5questions
Github Skills (22)
python10
vulnerability-scanners10
audit10
log4j10
code-auditing10
audit-logging10
scanning10
auditing10
audit-trail10
subprocess9
command-line-interface8
testing7
git7
xml6
aws-cli6
Programming languages (3)
Github contributions (5)
Neo23x0/log4shell-detector
Dec 2021 - Dec 2021
Detector for Log4Shell exploitation attempts
Role in this project:
Security Engineer
Security EngineerContributions:1 review, 8 commits, 2 PRs in 4 days
Contributions summary:Walid primarily contributed to enhancing the `log4shell-detector` tool, which detects Log4Shell exploitation attempts. They added a feature to check for Log4j usage before scanning and incorporated a silent mode to refine output. Furthermore, the user fixed a bug in the `check_log4j_used()` function, improving the tool's core functionality. These changes improved the tool's usability and precision in detecting Log4Shell vulnerabilities.
log4shellpowershellexploitationdetector
slrbl/Intrusion-and-anomaly-detection-with-machine-learning
Mar 2017 - Dec 2022
Machine learning algorithms applied on log analysis to detect intrusions and suspicious activities.
Contributions:115 commits, 2 PRs, 152 pushes in 5 years 10 months
log-analysismachine-learning-algorithmssuspiciouscybersecurityhttp-status-code
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