Field Notes
Practical notes on systems, governance, and implementation reality.
Short essays, implementation patterns, and working observations from the space between application architecture, cybersecurity governance, and organizational execution.
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AI Governance, Data Governance, Enterprise Architecture, Enterprise Governance, Information GovernanceFrom Data Catalog to Evidence System
Data catalogs tell enterprises what data exists. Evidence systems go further by connecting ownership, quality, controls, AI use, decisions, and outcomes to proof of governance.
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AI Governance, Compliance, Data Governance, Enterprise Governance, Information Governance, Risk Management
Data Governance Needs Evidence, Not Policies
Policies define what data governance should do. Evidence demonstrates what actually happened, who made the decision, which controls operated, and whether governance can be defended.
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AI Governance, Artificial Intelligence, Data Governance, Enterprise Governance, Information Governance, Third-Party Risk Management
Third-Party Data Is Still Your Governance Problem
Third-party data may originate outside the enterprise, but accountability does not. Organizations must govern its provenance, quality, permitted use, lineage, and decision impact.
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Inside NC State’s Nuclear Reactor: From the Power of the Atom to Plasma Lightsabers
A firsthand NCER tour of NC State’s PULSTAR reactor and plasma labs reveals research spanning nuclear energy, fusion, semiconductors, spectroscopy, and medicine.
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Artificial Intelligence, Data Governance, Enterprise Governance, Information Architecture, Knowledge Management
Data Retention Is Becoming an AI Risk
AI is changing data retention from a storage and records issue into an operational risk. Information that remains retrievable can influence future AI decisions and actions.
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AI Strategy, Artificial Intelligence, Cybersecurity, Data Governance, Enterprise Governance, Information Architecture
When Sensitive Data Becomes AI Context
AI changes what authorized access means. When sensitive data becomes AI context, retrieval, aggregation, inference, persistence, and action create governance risks traditional access controls were not designed to address.
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AI Strategy, Artificial Intelligence, Data Governance, Enterprise Architecture, Enterprise Governance, Information Architecture
The Governance Problem Hidden Inside RAG
RAG is more than an AI architecture. Every step from source selection and chunking to retrieval, generation, citation, and deletion introduces consequential data governance decisions.
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Artificial Intelligence, Data Governance, Enterprise Architecture, Enterprise Governance, Information Architecture
Your AI Is Only as Governed as Its Data
AI governance cannot compensate for ungoverned data. Trusted AI depends on information with clear ownership, quality, classification, lineage, provenance, permissions, lifecycle, and purpose.
