Founder · Machine Learning Engineer · Cybersecurity & Enterprise Risk Analytics
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I am a Machine Learning Engineer and Cybersecurity & Enterprise Risk Analyst with a focus on designing and deploying applied AI systems for risk monitoring, anomaly detection, and decision support.
I am the Founder of NxtAbroad Limited and the technical lead behind multiple live, production-style AI systems used for operational risk assessment, security monitoring, and workflow automation. My work sits at the intersection of digital technology, governance, and real-world system reliability, with applications across the UK and African markets.
- Applied Machine Learning & Data Analysis
- Cybersecurity Monitoring & Incident Analysis
- Enterprise Risk Intelligence & KRIs
- AI-assisted Decision Support Systems
- Python System Architecture & API Design
- Governance, Audit Support & Risk Reporting
Role: AI/ML Lead & System Architect
Type: Independent applied digital technology project
Status: Live demo with public access
🔗 Live Demo:
https://enterprise-risk-intelligence-engine.onrender.com
Description:
Designed and implemented an AI-assisted risk intelligence engine that ingests structured log/event data and produces:
- Key Risk Indicators (KRIs)
- Estimated anomaly rates
- Normalised risk scores
- Automated narrative risk summaries for decision-makers
The system is designed to reduce alert noise, support security monitoring teams, and translate low-level events into clear, human-readable risk insights. Emphasis is placed on decision support rather than full automation, with human judgment remaining central.
Technical Scope:
- Feature engineering and anomaly estimation
- Risk scoring logic and prioritisation
- Narrative generation for governance and audit contexts
- End-to-end deployment using Python and Streamlit
This work demonstrates innovation in applied AI for enterprise risk monitoring, beyond academic research or coursework.
Role: Founder & Technical Lead
Type: AI-enabled workflow automation platform
Status: Live API documentation available
🔗 API Documentation:
https://nxtabroad-ai-demo.onrender.com/docs
Description:
Designed an AI-assisted eligibility and readiness assessment engine to support structured decision-making in complex visa and immigration workflows.
The system focuses on:
- Reducing process ambiguity
- Improving consistency of assessments
- Supporting advisors with structured, explainable outputs
This platform demonstrates applied digital technology delivering measurable operational value beyond employment, with a focus on reliability, transparency, and compliance.
Type: Applied security analytics project
Built machine learning-based detection logic to analyse network traffic and security events, focusing on:
- Feature extraction from structured data
- Classification and anomaly indicators
- Monitoring-style analysis aligned with SOC workflows
This work reflects practical application of ML within cyber defense and security monitoring environments.
Context: Regulated healthcare environment
Contributed to cybersecurity assurance activities including:
- Patch compliance verification
- Secure configuration evidence
- Audit and assurance documentation
This experience demonstrates the ability to apply digital technology within regulated and high-assurance environments, working alongside governance and compliance requirements.
My approach to security and risk engineering prioritises:
- Data validation before escalation
- Impact and urgency-based prioritisation
- AI as a tool to reduce noise, not replace analysts
- Clear documentation and disciplined escalation paths
This reflects practical experience in monitoring, investigation, and operational decision-making, aligned with enterprise and financial system risk environments.
Across my work, I focus on building production-style digital systems that:
- Translate complex data into actionable insight
- Support human decision-makers
- Operate within real governance and risk constraints
My contributions demonstrate technical leadership, innovation, and sustained impact in digital technology, consistent with industry-facing expectations rather than academic outputs.
For professional or technical enquiries: