Each solution is an instance of the same compliance ontology. The method stays closed; the proof stays public.
Before any solution, the thesis: for AI to operate in critical sectors, trust cannot depend on a black box or on faith. It rests on a proof that any third party can verify, without revealing the method that produces it.
“To be verifiable, the method must stay closed. To be trustworthy, the proof must be public.” — Foundational paradox of VerΣum
Nobody knows which keys the notary keeps in the safe. But their seal is verifiable by any court. The method is secret; the signature carries legal validity.
You don't know how the authority verified the site owner. But your browser trusts the certificate. Trust lives in the proof, not in the process.
The prover proves they know something without revealing the knowledge itself. VerΣum proves the truth without exposing the protocol core.
Six modules, one ontology, one chain of custody. Each solution unfolds in three moves: what it solves, how it solves it scientifically, and which proof remains sealed.
Organizations lose traceability of which models, use cases and vendors are operating. Without a registry, there is no governance: you don't know what is in production, who approved it or under which conditions.
A single, governed inventory of use cases, models and vendors, with binding approval workflows and version control over every record.
Proof: state hash per recordMore and more scientific and product workflows use AI without declaring it. Correct attribution is not bureaucracy: it is the foundation of scientific reproducibility and legal accountability.
Integrated AI detection and usage reporting, so human-machine co-creation is transparent and auditable.
Proof: sealed attribution reportAI risk is dynamic: it changes with every version, every datum and every deployment. Static compliance snapshots are obsolete the day they are published.
Complete audit trails and dynamic risk scoring reports that evolve with the system, not against it.
Proof: verifiable score & audit trailRegulations (GDPR, EU AI Act, ISO 42001, SB21-169, NIST AI RMF) change faster than compliance programs. Translating regulation into executable controls is an ontology problem, not a reading problem.
Policy packages, risks and controls library from Insights Hub, maintained and connected to current regulation.
Proof: norm→risk→control mappingThe governance bottleneck is not regulation: it is the capacity to analyze use cases against risks and controls at scale. AI assists; the protocol certifies.
AI-assisted risk mapping and admission and registration controls, accelerating human review without replacing it.
Proof: traceable recommendationsMulti-agent systems operate without direct human supervision. When an agent delegates to another, the chain of accountability breaks. This is the problem nobody is solving.
AI agent registry and agent governor: identity, permissions and traceability of every autonomous node in the system.
Proof: agent causality chain