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Every dataset and model arrives through a named owner. The first act of governance is knowing who brought it in and when.
eventParity is the sovereign governance layer that sits beneath your AI products. Every asset, every sign-off, every evidence pack. One auditable record your regulator can read on demand.
AI is making decisions faster than anyone can prove they were lawful. When a regulator, an auditor or a minister asks how a model was trained, who approved it, and what it was allowed to do, most organisations cannot answer. EventParity makes that answer one document away.
EventParity's Fabric tracks the lineage of every data asset and AI model across your organisation. Every transformation, every approval, every attestation is recorded in a layer you control. The graph is always current. The audit trail cannot be revised.
Every dataset and model follows the same governed path. Nothing is deployed without a complete, signed record behind it.
Every dataset and model arrives through a named owner. The first act of governance is knowing who brought it in and when.
Schema, sensitivity classification, lawful basis, and responsible owner are captured at first contact. Nothing passes through undocumented.
Quality checks run automatically. Gaps are surfaced to the team who can fix them, not buried in a log no one reads.
Applicable standards are applied, named human sign-off is captured, and every change is preserved in an immutable history.
A regulator-ready dossier produced on demand. One document. Every question the auditor might ask, already answered.
You do not need a separate compliance tool for each regulator. EventParity maps your record to all seven frameworks simultaneously, so a single piece of evidence answers multiple authorities.
European Union
EU AI Act
Articles 10 to 17 covering data governance, technical documentation, transparency, human oversight, and accuracy requirements for high-risk AI systems.
European Union
GDPR
Core Articles 5 to 49 covering lawful basis, data subject rights, controller accountability, impact assessments, and cross-border transfer conditions.
Nigeria
NITDA AI Code of Practice
Model governance requirements M1 to M5: training-data provenance, NDPA Section 25 lawful basis, named human sign-off, model card, and change control.
Nigeria
Nigeria Data Protection Act (NDPA) 2023
Section 25 lawful basis for AI model training, data subject rights, and accountability obligations for data controllers and processors.
Nigeria
NDPR 2019
Nigeria Data Protection Regulation principles: purpose limitation, data minimisation, accuracy, storage limits, and security of personal data.
South Africa
POPIA
Eight conditions for lawful processing, special and children's data protections, and cross-border transfer provisions under the Protection of Personal Information Act.
Kenya
Kenya Data Protection Act 2019
Registration requirements, data protection principles, data subject rights, security obligations, breach notification, and cross-border transfer rules.
Every capability here is live today, working on your real data and your real models.
Every deployed AI model gets a registry entry: training-data provenance, a recorded lawful basis, a named human approver, a model card, and a change-control audit trail. Nothing is deployed without this record.
Turns the live record into a single regulator-ready document, mapped requirement by requirement to whichever frameworks apply. NITDA, EU AI Act, NDPA, GDPR, POPIA, and Kenya DPA are all supported.
Every sign-off is permanent. Nothing can be quietly edited after the fact. Revoking or renewing an attestation creates a new row, so the full history of who said what, and when, is always visible.
Six dimensions scored on a five-level ladder, with a one-page brief your leadership team can read. Benchmarks where you stand, surfaces where effort will have the most impact.
Every upstream and downstream dependency between data assets and models is tracked. When a source changes, the impact on every downstream model is visible immediately.
Use the major hosted providers, a sovereign in-country model, or your own model running on infrastructure you control. Your keys stay yours, and the AI assistance never requires you to route data through a third party.
Every model and dataset you run sits in one register, each with its named owner and approver. Nothing is deployed without a responsible person attached to the record.
Once a model is signed off, the dossier is one click away, so the trail reads like what actually happened. No reconstruction, no retrospective paperwork.
When the question comes, the Evidence Pack turns the record into a single document a regulator can read. Mapped to NITDA AI Code, EU AI Act, NDPA, GDPR or POPIA as required.
EventParity serves institutions wherever AI governance is not optional. If you are building, procuring, or regulating AI, this is your platform.
Agencies procuring or deploying AI need to demonstrate lawful authority, data protection compliance, and accountable human oversight. EventParity gives you that record without requiring a specialist team to assemble it.
Research institutions developing AI models need provenance on training data, ethics-committee sign-off in the record, and a clear lineage between source datasets and published outputs.
Product teams need governance that keeps pace with development, not a compliance exercise bolted on at the end. EventParity integrates into the model lifecycle from the first dataset to the first deployment.
The AI assistance in EventParity never requires your data to leave your perimeter. You supply the key; we supply the tooling.
AES-256-GCM encryption of all credentials and the self-hosted sovereign-LLM path are real and available today. A fully packaged on-premise deployment is offered as a scoped pilot engagement for organisations that need it. It is not a self-serve download yet, and we will tell you that plainly.
What is real today