GT Research Lab
gtreslab.com
Lab
The Lab is where our team studies updated information, tests existing technology deeply, finds real problems and possible solutions, then publishes research and releases useful tools when the work can help people or businesses.
Lab Method
Our team continuously researches the areas we care about, following new information, papers, vulnerabilities, privacy failures, AI security risks, system weaknesses, and real-world technical changes.
The lab studies existing technologies, products, systems, APIs, security tools, AI workflows, and infrastructure patterns, then tests them to find problems, limits, risks, and possible solutions.
After research and testing, the lab publishes updated research papers, technical notes, reports, or demonstrations. When a tool can help people or businesses, we aim to release it as free, open-source, or accessible technology.
Where We Apply It
Research into prompt inspection, explainable risk decisions, output filtering, adaptive trust, and audit trails for safer AI model access.
Experimental API gateway patterns for routing AI traffic through authentication, policy enforcement, request checks, provider control, and structured logs.
Prototype work around memory artefact analysis, Volatility3 outputs, suspicious activity correlation, and clearer investigation interfaces.
Interface concepts for alerts, attack timelines, model posture events, risk signals, and Zero Trust decision visibility.
Focus Areas
Internal research into safer AI workflows, data boundaries, reviewable outputs, and practical controls around AI-assisted systems.
Technical exploration around access control, least privilege, request validation, and security-conscious architecture.
Proof-of-concept systems for repeatable checks, documentation support, and security workflow improvement.
Experimental prototypes for organising technical knowledge, project notes, reports, and applied research material.
Applied security ideas for business processes, internal tools, forms, approvals, and operational digital systems.
Research Philosophy
AI systems should be designed with verification, policy, visibility, and control from the beginning.
Security tools become more useful when their decisions can be inspected, explained, and challenged.
Access should respond to behaviour, context, risk, and evidence instead of assuming anything is safe.
Good systems leave useful traces for investigation, learning, and future improvement.
Research Mode
The Lab works through continuous research, deep testing, problem discovery, solution design, research publishing, and practical tool development. Some tools may become open source, free to use, or publicly accessible when that helps the wider community.
Prompt inspection, risk decision, output review
Memory artefacts, process context, timeline notes
Events, alerts, model posture, investigation trail