Ayomide D.

Key Skills

Software

Data Annotation Tech

OneForma

Other

Clickworker

Top Subject Matter

Cybersecurity log analysis and incident triage for AI training relevance

SIEM security event labeling

threat intelligence structuring

Top Data Types

Text

Computer Code Programming

Video

Top Task Types

Classification

Object Detection

Red Teaming

Computer Programming/Coding

Function Calling

Freelancer Overview

Cybersecurity Analyst — AI Training Data Relevance (Deloitte Australia · Cyber Job Simulation). Brings 1+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal and Proprietary Tooling. Education includes Bachelor of Science, Bingham University (2027). AI-training focus includes data types such as Text and labeling workflows including Classification.

Labeling Experience

Cybersecurity & Infrastructure Intern (Galaxy Backbone Limited · GBB)

TextClassification

Analyzed high-volume security event data from enterprise SIEM platforms and identified true positives, false positives, and anomalies for dataset annotation and quality assurance. Contributed to structuring raw threat intelligence into actionable formats that align with AI training pipeline data requirements. Led intern cohort activities that supported consistent labeling through quality review and coordination. • Categorized SIEM security events and labeled outcomes (true positive/false positive/anomaly). • Supported data structuring for threat intelligence ingestion into training datasets. • Conducted/participated in quality review processes and inter-rater coordination. • Coordinated task delegation and deadline adherence for labeling project cycles.

2025 - 2025

Cybersecurity Analyst — AI Training Data Relevance (Deloitte Australia · Cyber Job Simulation)

TextClassification

Reviewed and categorized web activity logs to identify anomalous patterns and support consistent cybersecurity labeling workflows. Investigated and labeled security events by type, severity, and response category to improve structured decision-making for annotation outputs. Participated in simulated breach response exercises requiring contextual judgment under ambiguity to mirror training data labeling conditions. • Labeled web activity and security event records with anomaly relevance and classification attributes. • Performed incident triage-style categorization (type, severity, response category). • Applied rule-based judgment to determine appropriate labels during simulations. • Produced QA-ready interpretations aligned with AI training relevance requirements.

2025

Education

Work History