John J Joseph

Entry-Level SOC Analyst / Detection Engineer with hands-on blue-team and attacker-side experience.

Built

NeuralWatch an ML based log anomaly detection system achieving F1 0.924 and AUC-ROC 0.998 across 15M+ logs with LLM-powered alert explanations—alongside a MITRE ATT&CK-mapped Wazuh SIEM lab. Proficient in SIEM log ingestion, detection engineering, rule tuning, Python scripting, and threat modeling to streamline Tier 1 alert triage across EDR and SIEM workflows.

Vulnerability Assessment at Freelance (2025-12 – Present)

• Led an authorized black-box vulnerability assessment of a live commercial website, identifying and documenting 6 security findings across OWASP Top 10 vectors, focusing on input-validation flaws and server security hygiene.
• Diagnosed critical security misconfigurations—specifically missing Content Security Policy (CSP) enforcement and unconfigured XSS protection headers—and authored a CVSS-scored remediation report aligned with NIST CSF and ISO 27001 controls to guide the business owner through prioritized patching.

Data Analyst Intern at Oracuz Infotech Ltd (2023-12 – 2024-05)

• Cleaned, transformed, and analyzed large unstructured datasets stored on AWS to support ML workflows — the same data-wrangling foundation later applied to log analysis in NeuralWatch.
• Built exploratory data analysis (EDA) reports and Tableau/Power BI dashboards to surface outliers and trends, a skill set that carries directly into anomaly detection and threat-hunting work.
• Collaborated with the data science team on predictive-model prototyping, applying validation and performance-tuning practices later used to train NeuralWatch's detection model.

MSc in Computer Science – Cybersecurity, Ethical Hacking & Cyber Forensics – S-VYASA University (2024-06 – 2026-07)

BVoc in Business Process & Data Analytics – Cochin University of Science and Technology (CUSAT) (2021-10 – 2024-05)