SOVEREIGN INTELLIGENCE / ANSWERS · R1
WHO STILL COMMANDS THE MACHINE LAYER?
Control / compute / data / model / audit / exit.
What Is Sovereign AI?
Sovereign AI concerns meaningful control over compute, data, models, operations, governance, continuity, and exit. Explore the RDIV control framework.
AI-002 · WAVE CSovereign AI vs. Data Sovereignty: What Is the Difference?
Data sovereignty concerns control of data; sovereign AI extends to compute, models, operations, governance, continuity, and vendor dependency.
AI-003 · WAVE BWhat Is Compute Sovereignty?
Compute sovereignty concerns control, continuity, jurisdiction, infrastructure, chips, cloud capacity, administration, and replaceability for critical workloads.
AI-004 · WAVE DWhat Is Model Sovereignty?
Model sovereignty measures operational control over AI deployment, access, updates, auditability, portability, replacement, and continuity.
AI-005 · WAVE AWhat Is AI Vendor Dependency?
AI vendor dependency becomes a governance risk when institutions cannot independently inspect, replace, migrate, suspend, or continue critical functions.
AI-006 · WAVE CHow Should Governments Procure AI Systems?
A source-led framework for evaluating AI procurement through data rights, auditability, continuity, portability, human authority, and credible exit options.
AI-007 · WAVE BWhat Is AI Auditability?
AI auditability concerns whether qualified reviewers can inspect evidence, controls, changes, logs, data lineage, system behavior, and governance claims.
AI-008 · WAVE DWhat Is Machine-Layer Dependency?
Machine-layer dependency is an RDIV framework for measuring whether practical institutional command weakens as consequential functions move into computational systems.