Cybersecurity

Cybersecurity work focuses on large-scale security platforms and data-driven security systems operating in production environments.

The work combines applied data science, machine learning, and systems engineering to analyze and improve platforms responsible for web application protection, exposure management, asset discovery, and vulnerability analysis.

Rather than treating security as a collection of tools, the emphasis is on system-level understanding — how security data flows through complex platforms, how signals are generated and prioritized, and how large security systems can be observed, optimized, and continuously evolved to support effective protection and decision-making.

Technical Capabilities

Cybersecurity work spans two complementary domains: defensive security platforms and offensive security focused on attack surface discovery and vulnerability scanning.

On the defensive side, work on large-scale Web Application Firewall (WAF) platforms involved leading data science initiatives focused on improving how security systems interpret and respond to high-volume web traffic signals. This included risk scoring models, machine learning for bot detection, and security signal prioritization.

On the offensive side, experience spans the full lifecycle of External Attack Surface Management (EASM), including OSINT-driven reconnaissance, internet-scale asset discovery, and vulnerability scanning of internet-facing infrastructure.

Across both domains, the common thread is the application of data science, automation, and systems engineering to transform large security datasets into actionable intelligence.

EASM - External Attack Surface Management

Principal-Level Engineering Responsibilities @ SecurityTrails / Recorded Future (later acquired by Mastercard) Contractor/Consultant | 3+ years

Platform Context

The work was conducted within a large-scale External Attack Surface Management (EASM) platform designed to continuously discover, scan, and analyze internet-facing infrastructure.

The platform combines asset discovery, automated vulnerability scanning, and post-scan analytics to monitor domains, IP infrastructure, DNS data, and exposed services across large portions of the public internet.

Engineering work focused on improving how the platform discovers assets, orchestrates large-scale scanning workflows, processes scan results, and derives intelligence from cybersecurity datasets generated through reconnaissance and vulnerability scanning.

Key Responsibilities

Principal-level engineering across platform transitions, system handovers, and stabilization
Development of internet-scale asset discovery and vulnerability scanning systems
Analysis of internet-facing infrastructure (domains, IP assets, DNS, exposed services)
Development of security data pipelines and analytical workflows
Application of machine learning techniques (classification, clustering, anomaly detection)
Development of internal analytical and operational tooling
Design of post-scan analytics and risk evaluation mechanisms
Work with distributed pipelines, containerized services, CI/CD and cloud infrastructure

Internet-Scale Discovery & Scanning

Large-scale attack surface discovery requires continuous reconnaissance, asset discovery, and automated vulnerability scanning of internet-facing infrastructure.

Work in this area focused on building and improving systems capable of discovering, scanning, and analyzing exposed services across internet-scale datasets.

Key areas included:

OSINT-driven reconnaissance and asset discovery across domains, IP infrastructure, DNS records, and exposed services
Development and optimization of automated vulnerability scanning pipelines for large-scale infrastructure analysis
Extensive work with security scanning ecosystems, including Nmap, Nuclei, wafw00f, testssl.sh, and Wappalyzer
Customization of scanning templates and detection logic, including development of internal mechanisms and scanning workflows
Design of scan orchestration systems enabling efficient distributed scanning across large attack surfaces
Development of post-scan analytics pipelines used to enrich, classify, and prioritize security findings
Analysis of internet-scale reconnaissance and scanning datasets to identify exposed infrastructure, vulnerabilities, and misconfigurations
Application of data science and statistical techniques to improve interpretation and prioritization of scanning results

WAF - Web Application Firewall

Lead Data Scientist in Cybersecurity @ StackPath

Platform Context

The work on Web Application Firewall (WAF) systems focused on analyzing large volumes of web traffic telemetry and improving automated detection of malicious activity in production environments.

As part of the WAF data team, the work involved applying machine learning, statistical analysis, and large-scale data analytics to identify patterns of malicious behavior such as automated bot activity, coordinated attacks, and anomalous traffic patterns.

A central part of the work focused on improving how security platforms generate and prioritize risk signals, enabling more accurate classification of suspicious traffic and more effective automated mitigation.

Key Responsibilities

Member of the Web Application Firewall (WAF) data team, responsible for analysis of large-scale web traffic telemetry and threat intelligence derived from production security systems
Work on R&D initiatives related to WAF technologies, focused on data-driven detection of malicious internet activity and automated threat classification
Development of machine learning models and statistical detection methods applied to large cybersecurity datasets
Led the R&D and development of the IP Spotlight risk scoring component, from concept and research to production implementation
Design and implementation of IP risk scoring mechanisms used to evaluate malicious activity associated with internet infrastructure
Application of machine learning techniques such as clustering, classification, and NLP to detect malicious patterns in web traffic and security telemetry
Development of internal analytical and reporting tools used for exploration, debugging, and validation of machine learning workflows
Analysis of large-scale WAF telemetry datasets and behavioral patterns associated with automated attacks and malicious infrastructure
Development and optimization of complex analytical queries and investigation workflows used for security data analysis
Large-scale security telemetry analysis using column-oriented analytical databases such as ClickHouse, supporting high-volume security analytics and threat investigation

IP Spotlight Project

IP Spotlight is a feature that allows our customers to investigate an IP address and gather more meaningful data about it. Some of the data we provide per IP address is as follows:

IP threat summary, including a risk assessment score
Use of botnets
“Known for” activitiesa
IP ownership information
Request information, including requests blocked and a unique session count
Geographic information on attack origins and targets
Attack distribution over time

This feature shows you all the information we have gathered on any entered IP address so that you can make a more data-driven decision regarding securing your web presence.

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