Articles on AI governance, cybersecurity and systems architecture.
A concrete architecture for an on-premise clinical Retrieval-Augmented Generation system anchored to FHIR and DICOM: ingest, retrieval, local biomedical LLM, answers with verifiable citations, and the MDR/AI Act/GDPR constraints read as design choices.
Read moreLeo XIV's encyclical on artificial intelligence describes a technological power that is «predominantly private» and calls for independent verification, algorithmic transparency and fair access to data. These are the same criteria as verifiable governance.
Read moreAdmina 0.9.1 release notes — SDK, proxy, plugin system, CLI and dashboard in a single install. Apache 2.0 licence, hybrid Python + Rust engine.
Read moreListening notes on Eleanor Rigby (Beatles, 1966): the double string quartet arranged by George Martin, the E minor / C major oscillation, Lennon and Harrison's vocal harmonies on the chorus.
Read moreOpen, Intelligent, Secure, Governed. An adequacy vocabulary that makes autonomous AI systems verifiable, on top of existing regulatory standards.
Read moreA map of the standards and period documents that connect innovation management, research and development and the Industry 4.0 and 5.0 paradigms, with their operational boundaries.
Read moreGDPR, NIS2, DORA and the AI Act ask for largely the same controls but diverge on notification thresholds and deadlines. The hard part is not the requirement list; it is the data model that links them without duplicating them.
Read moreA cloud platform that launches automated scans and pentests on user input must prove, before it touches a host, that whoever starts the scan really controls the target. Technical notes on domain validation and the boundaries of a scan.
Read moreAutonomous AI agents operate without adequate oversight. A governance architecture designed for control, transparency, and accountability is needed.
Read moreA terminal agent launched inside an SSH session works on the server, not the workstation: it inherits that environment. Governance lives in the permissions file, not in the prompt. What it guarantees and what it leaves uncovered.
Read moreA medical device with machine learning that changes after certification is, for the regulator, a different device. MDR and the FDA answer with very different tools and timelines.
Read moreAutonomous software introduces new attack surfaces that traditional security models are not equipped to handle.
Read moreAn NVIDIA GB10 workstation with 128 GB of unified memory holds models up to 200B parameters under a desk. For health data the constraint is not capacity but memory bandwidth: 273 GB/s.
Read moreHealthcare demands AI systems that are verifiable, explainable, and compliant with rigorous clinical and regulatory standards.
Read moreA language model in contact with records and reports has to stay inside the perimeter: PII redaction upstream, immutable logging for the EU AI Act, risk classification under MDR. The architectural constraints that follow.
Read moreCDA R2, FHIR and openEHR do not sit on the same plane of a health system: they answer three different questions — archiving, exchange, keeping data over time. The roles, the contact points, and the friction that appears when a standard ends up in the wrong place.
Read moreRegulation (EU) 2025/327 on the European Health Data Space has been in force since 26 March 2025. A technical reading of primary use, secondary use, the Health Data Access Body and the application timeline.
Read moreIn NVIDIA's DGX platforms the interconnect silicon (NVLink, NVSwitch) is proprietary, while the software and mechanical layers around it are public: NCCL under a BSD licence, the GB200 NVL72 rack contributed to the Open Compute Project. Where the line falls between what is documented and reusable and what stays tied to a single vendor.
Read moreDeepSeek-R1 ships with weights under the MIT licence, but the open weights label covers the parameters alone: data, training code and the RL procedure stay out. What that changes for anyone who has to audit the model.
Read moreGoverning an autonomous agent needs three primitives: identification, diagnosis, intervention. They sit at very different levels of maturity, and the asymmetry is the main operational constraint.
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