oConsent
Enterprise ยท AI data governance

Govern AI data use across teams, systems, and pipelines.

OConsent gives product, data, legal, security, and ML teams a shared permission layer for AI data use.

Discuss a pilot → View draft spec
The problem

AI is moving faster than consent and governance systems.

Enterprises are adopting AI faster than their consent and governance systems can adapt. Data moves into model pipelines, agents, vector databases, analytics systems, and partners. Legal approval, data permissions, and runtime enforcement often live in different systems, so no one can answer a simple question at the moment it matters: is this actor allowed to use this asset for this purpose?

Capabilities

A shared permission layer for AI data use.

Private consent registry

A registry your teams control for issuing and looking up consent records.

Policy templates

Reusable purpose and scope definitions for common AI data uses.

Point-of-use verification

Checks for apps, models, agents, and pipelines before access.

Enforcement hooks

Block disallowed use by default, not after the fact.

Revocation workflows

Withdraw permissions and propagate the change to downstream consumers.

Audit exports

Export tamper-evident records of consent checks and decisions.

Data catalog integration

Associate consent records with assets in your data catalog.

AI gateway integration

Gate model and agent calls behind a consent check at the gateway.

Evidence for governance reviews

Produce proof of who used what, for which purpose, under whose consent.

Deployment models

Start small, then operate it yourself.

Reference model and code

Use the open reference model and code to prototype consent-aware flows.

Available now

Self-hosted registry

Operate a private consent registry and verification service in your environment.

Planned

Managed verification service

A hosted registry and verification API operated for you.

Planned

Enterprise integrations

Connectors for data catalogs, AI gateways, and governance tools.

Planned
Use cases

Where a permission layer pays off.

  • AI training governance. Prove which data a model was allowed to train on.
  • Agent permissions. Scope what an autonomous agent may access and do.
  • Dataset licensing. Bind datasets to allowed purposes and actors.
  • Research consent. Track participant permissions across studies.
  • Internal AI policy enforcement. Apply data policies at the point of use.
  • Vendor and partner data sharing. Carry consent across organizational boundaries.
Maturity

OConsent is currently suitable for pilots, prototypes, reference implementations, and design partnerships. Production deployment details should be validated with your security, legal, and governance teams.

Bring a permission layer to your AI stack.

Run a pilot against real datasets, agents, and pipelines, and produce evidence your governance reviews can use.

Discuss a pilot → View draft spec