Notes on industrial AI, edge intelligence, and trustworthy systems.
Research writing from the Director of the AI Data & Security Research Center at KETI — foundation models for industry, agentic-AI security, and the data infrastructure underneath.
Running AI Inside a Trusted Execution Environment
Less a tutorial, more a field report — why you'd run an AI model inside a Trusted Execution Environment (to keep weights and data out of the host's reach), why today's TEEs strain under it (tiny enclave memory, CPU-only trust, costly CPU↔GPU transfers), and recent research directions, including why confidential GPU inference needs a Hopper-class data-center GPU and why Jetson Thor's Blackwell doesn't qualify.
All writing
9 posts- Jul 01, 2026 ~11 minLeaking Through an Authorized Door — the Security Problem of Generative and Agentic AI
Industrial foundation models and agentic AI can transform process management, but they punch a different kind of hole in information protection. This is about information that leaks through a legitimate access route, the attacks worth thinking about, the standards and guidelines that speak to them, and a defensive paradigm that brings an AI point of view into cybersecurity.
- Jun 29, 2026 ~12 minAn Industrial Data Lake for Industrial AI
A sketch of an Industrial Data Lake — a way to build industrial AI while keeping data and models protected as corporate assets. It covers the conflicting requirements industry faces, agentic AI as an answer, a stakeholder-and-business-model structure, what Germany and Europe's IPCEI-AI suggests, and the security functions the system has to carry.
- Jun 06, 2026 ~9 minIndustrial AI and its visibility
Commercial AI is already astonishingly good — so do we even need a separate industrial AI model? A look at three constraints (protecting information, cost, and sustainability) and why the thing that ultimately holds it all together is AI visibility.
- Jun 03, 2026 ~6 minWhat an Industrial Foundation Model Should Be
The phrase 'industrial foundation model' turns up in every R&D program now. But to earn the word 'foundation,' such a model shouldn't be an omniscient know-it-all — it should be the bedrock that domain knowledge gets built on.
- May 29, 2026 ~19 minWhen the Knowledge Base Wants to Be a Graph
Two days after building a markdown knowledge base, the cracks started showing. Notes on turning it into an RDF graph using Apache Jena Fuseki - the architectural calls, the model comparisons across Claude, qwen2.5, and exaone3.5, and why the documents themselves are the only sustainable source of truth.
- May 27, 2026 ~7 minBootstrapping a Personal Knowledge Base in an Afternoon
Notes from a day spent designing folders, slugs, and a small LLM skill so that future updates to a personal knowledge base only require pointing at the source material.
- May 23, 2026 ~5 minA Study Roadmap for Uncertainty Quantification + Inverse Dynamics
A leveled reading list and a 10-week curriculum for getting from Bayesian basics to physics-informed, uncertainty-aware inverse dynamic models.
- May 23, 2026 ~8 minUncertainty Quantification Meets Inverse Dynamics
A concept review of uncertainty quantification (UQ), inverse dynamic models (IDM), and why combining them matters for safe, data-efficient robot control.