About KROG
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I work at the intersection of data governance, privacy engineering, and AI systems. I build technology and advise organizations on how to operationalize compliance - turning policy from paperwork into executable infrastructure.
What I Do
Advisory
I advise companies on data governance strategy, privacy architecture, and AI compliance. This includes GDPR implementation, AI Act readiness, and building the organizational capabilities needed to govern data and AI systems at scale. I work with legal teams, technical teams, and leadership to bridge the gap between regulatory requirements and operational reality.
Technology
I build the infrastructure that makes governance executable rather than aspirational.
Context Graphs for Data Governance
Traditional compliance treats documentation as static records. I treat it as operational infrastructure. A Context Graph captures not just what data you process, but why, under what authority, with what constraints, and for whom - in a form that both humans and AI agents can query and reason over.
This is the missing layer between policy and practice. Between what organizations say they do and what their systems actually permit. When an AI agent asks “Can I use this data for this purpose?” - the Context Graph provides the answer.
KROG: Universal Framework for Rules & Authorization
I developed KROG - a mathematical framework based on deontic modal logic that brings clarity to any rule-based system. KROG answers four fundamental questions:
What can participants Know? (Transparency)
What are they Permitted to do? (Rights)
What are they Required to do? (Obligations)
How are decisions Governed? (Meta-rules)
KROG expresses rules using formal operators: P() for permissions, O() for obligations, F() for prohibitions. It serves as a meta-language - enabling cross-domain reasoning from contract analysis to AI authorization to game mechanics.
Why This Matters Now
AI agents need context to make good decisions. Not just data - situated knowledge about what’s permitted, obligated, and forbidden in specific circumstances. Without this, we get AI systems that are powerful but ungrounded, capable but unaccountable.
The organizations that invest in knowledge infrastructure - capturing procedural knowledge, encoding decision traces, building context graphs - will be the ones that successfully deploy trustworthy AI. The rest will find their AI initiatives foundering on missing context.
Process knowledge isn’t the “boring stuff.” It’s foundational infrastructure for intelligent systems.
What I Write About
Context graphs and knowledge infrastructure for governance
Process knowledge management and procedural ontologies
AI authorization, oversight, and the limits of autonomy
Rule specification and contract engineering
Building systems where “compliance by design” actually means something
The semantic web technologies that make this possible
Connect
KROG Framework: krogrules.com
Know your Rights. Know your Obligations. Know your Governance.
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