Scene 02 of 06The routeCase file: E. Ologunde
Scene 02
The route
22 dated nodes, 2011 to the expected D.Eng. in 2027. With motion on, it draws itself as you scroll.
In plain English: each mark is a job, a degree or a project, in date order. The lines show how one step led to the next, and the red line is the shortest route from my first job to the work I want to do now: keeping AI-era systems secure.
Read it like an attack graph: one entry point, pivots that each opened the next position, assets picked up on the way, one objective.
Every node is a dated fact from my record; the layout and the edge labels are my reading of it. Select a node, or tab to it, for the full story. Skip the drawing and read the route as text.
Shortest path, 6 hops Internet cafe Tech support OT / SCADA M.S. Cyber Forensics GWU D.Eng. Breakwater Securing AI-era systems
The same graph, as text22 nodes in date order, each with the edges that enter and leave it
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Internet cafe
Global Link Internet Cafe, Ilorin, Nigeria
My first job: running an internet cafe. The machines, the network, and every customer whose email would not open. Keeping strangers' computers alive is where the whole route starts.
- out to Tech support by troubleshooting
- out to Dreamlabs by web + networks
- out to A.S. Computer Sci. by formal CS
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Dreamlabs
Dreamlabs Softwares, Ilorin
Manager and website administrator internship: I built web features in HTML, CSS and JavaScript and kept the network behind them up. I came back from 2020 to 2022 as the IT support specialist.
- in from Internet cafe by web + networks
- out to Tech support by user support
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A.S. Computer Sci.
Kwara State Polytechnic
Associate of Science in Computer Science. The formal foundations under what the cafe had taught by hand.
- in from Internet cafe by formal CS
- out to B.S. CS + Education by CS + education
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Tech support
Pulse Technologies, Ilorin
Four years as a technical support agent: Windows, business applications, user accounts, and who is allowed to open what. Access control, learned one ticket at a time.
- in from Internet cafe by troubleshooting
- in from Dreamlabs by user support
- out to OT / SCADA by network integration
- out to Health insurance IT by access control
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B.S. CS + Education
University of Ilorin
B.S. Computer Science and Education, dual major. Building systems and explaining them, on one transcript.
- in from A.S. Computer Sci. by CS + education
- out to Boston Univ. TA by teaching
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Health insurance IT
Ebonyi State Health Insurance Agency, Abakaliki
IT officer at a public health insurance agency that handled sensitive health data: my first regulated environment. I built the interface for a claims registration system.
- in from Tech support by access control
- out to OT / SCADA by regulated systems
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OT / SCADA
Total Secure (remote)
Integration technician: connecting oil pump control and monitoring systems to OT networks and SCADA data pipelines. My first operational technology work.
- in from Tech support by network integration
- in from Health insurance IT by regulated systems
- out to M.S. Cyber Forensics by forensics
- out to Intro to GenAI by new attack surface
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Boston Univ. TA
Boston University
Graduate teaching assistant for Experience Design.
- in from B.S. CS + Education by teaching
- out to Syracuse CSD by classroom labs
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Intro to GenAI
Google Cloud, via Udacity
Introduction to Generative AI. The first formal look at the thing everyone was about to plug into everything. The AI thread starts here.
- in from OT / SCADA by new attack surface
- out to AI engineering cert by building with models
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M.S. Cyber Forensics
University of Baltimore
M.S. Cyber Forensics, GPA 3.9. The turn: from keeping systems up to explaining what happened to them. It taught me to ask what happened and how we know, and that question followed me into AI-era security.
- in from OT / SCADA by forensics
- out to GWU D.Eng. by evidence rules
- out to SSRN preprints by writing in public
- out to Cyntraix by risk assessment
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SSRN preprints
SSRN, listed on ORCID; essays on Medium
24 preprints, including BootKitty, an analysis of modern bootkit and rootkit threats (October 2025), and Adversarial Machine Learning on Automotive Attack Surfaces (2026). SSRN is a preprint repository, not a peer-reviewed journal: the count says I write, not that anyone refereed it. Essays include why risk management matters more than ever in the age of AI (December 2025).
- in from M.S. Cyber Forensics by writing in public
- out to Breakwater by ACM paper draft
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Syracuse CSD
Syracuse City School District
My first classroom of my own: high school cybersecurity with Wireshark and Nessus labs and capture-the-flag exercises.
- in from Boston Univ. TA by classroom labs
- out to Lawson State by curriculum
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Cyntraix
Cyntraix
I founded Cyntraix: security assessments, Zero Trust architecture and risk governance, and AI security risk and governance advisory. Client names stay out of this page.
- in from M.S. Cyber Forensics by risk assessment
- out to Security lab by test first
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Lawson State
Lawson State Community College, Birmingham
Computer science instructor. I helped redesign the CIS curriculum into one stackable pathway.
- in from Syracuse CSD by curriculum
- out to UAGC faculty by pen testing
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AI engineering cert
App Academy
AI-Powered Software Development and Generative AI Engineering certificate. Building with models, so I know where they break.
- in from Intro to GenAI by building with models
- out to HoWz + local model by LLM guardrails
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UAGC faculty
University of Arizona Global Campus
Online faculty for CYB102, Network Penetration Testing.
- in from Lawson State by pen testing
- out to Adrian College by hacking
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GWU D.Eng.
The George Washington University
D.Eng. candidate in Cybersecurity Analytics. Coursework is done; praxis research began in fall 2026, on explainable AI, AI and ML threat modeling, adversarial machine learning and AI-enabled security governance. The degree is pending, not awarded.
- in from M.S. Cyber Forensics by evidence rules
- out to Breakwater by attack-graph research
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Breakwater
GWU, SEAS 8414, Analytical Tools for Cyber
An end-to-end security-analytics pipeline for a simulated OT/IoT network: discovery, device identity, vulnerability triage, attack paths, a digital twin and post-quantum readiness. Phase 1 admitted 20 hosts, 77% of the declared 26, with 0 phantom hosts and 248 NVD CVEs matched. Later phases: a federated intrusion-detection model with poisoning and privacy defenses, and reinforcement-learning agents kept behind safety controllers. An ACM-format paper is in preparation. Simulated network only.
- in from GWU D.Eng. by attack-graph research
- in from SSRN preprints by ACM paper draft
- out to Securing AI-era systems by AI threat modeling
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Security lab
My own hardware, at home
Two small Proxmox VE machines, deliberately unclustered, and one log pipeline: 304,708 events in 24 hours from 5 log sources, and about 3,700 host-based detection alerts over 7 days. Before I recommend SIEM or detection tooling to a Cyntraix client, I run it here. It caught a VPN container that had never worked, crash-looping more than 12,000 times and writing about 361 GB to disk.
- in from Cyntraix by test first
- out to Securing AI-era systems by run what I recommend
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HoWz + local model
HoWz, my private operations platform, and the lab
Two of my own systems use a language model. HoWz has a question pane where a model may suggest changes; the lab runs a small language model on a CPU that listens only on the lab network. The model can only propose: code validates every proposal and nothing is written until I approve it. The runtime was installed only after its release checksum was verified.
- in from AI engineering cert by LLM guardrails
- out to Securing AI-era systems by human approval
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Adrian College
Adrian College, via Rize Education
Ethical hacking instructor. With Lawson State and UAGC, that makes three institutions where I teach security now.
- in from UAGC faculty by hacking
- out to Securing AI-era systems by training defenders
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Securing AI-era systems
Open to work: Birmingham, Alabama, remote available
Where the route points: security engineering, AI security and governance, GRC, detection and cybersecurity education roles. The rule I carry from forensics into AI: AI proposes, evidence decides. A model can suggest and rank; a finding needs measured evidence, and a change needs validation and a human approval.
- in from Breakwater by AI threat modeling
- in from HoWz + local model by human approval
- in from Security lab by run what I recommend
- in from Adrian College by training defenders