Shadow AI
Identify common pathways through which employees, students and researchers may move sensitive information into unmanaged AI services.
Sovereign AI & IP Protection
Sovereign AI, Research Data & Intellectual-Property Protection
Learn how to classify AI workloads, address Shadow AI, protect research and emerging intellectual property, and evaluate Data, Compute, Model, Knowledge, Cognitive and Operational Sovereignty.
Program content
A decision matrix for workload placement, controls and institutionally governed environments
Participants work through the practical decisions that determine whether information belongs in public, enterprise, private, hybrid or specially governed AI environments.
Research & emerging IP
Protect knowledge that may become valuable before it is formally classified as intellectual property.
Six dimensions of sovereignty
Understand Data, Compute, Model, Knowledge, Cognitive and Operational Sovereignty.
Approved alternatives
Design institutional options that are useful enough to reduce incentives for unsanctioned AI use.
Model & knowledge alignment
Evaluate whether models and authoritative sources are appropriate to the institution and jurisdiction.
Governance
Connect classification, policy, identity, permissions, retention, logging and human accountability.
Learning flow
Learning becomes useful when it changes what the organization can do next.
Each engagement is configured around the participants’ actual responsibilities.
Context and preparation
Identify workloads, information types and institutional constraints.
Explanation and guided work
Apply the framework to representative use cases.
Decision matrix
Document environment choices and required controls.
Next action
Leave with a concrete governance, architecture or training action.
Continue the journey