Introducing Deeplinq for AML

Policy becomes runtime behavior.

Apply identity, grants, content rules, tool decisions, and approval gates at the point where AI work actually happens.

Access narrows at every scope.

Organization, team, resource, and model grants compose into one decision. Missing authority ends the request before spend.

Default deny

Organization

Tenant bound

Support team

Member verified

Policy dataset

Read granted

Frontier model

No grant

Denied

Turn control documents into enforced boundaries.

Governance is applied before spend and re-evaluated for deferred work, so a revoked grant takes effect on the next request or agent turn.

  1. 01

    Default deny

    Model and dataset access require explicit grants. Unknown or cross-organization resources do not become an existence oracle.

  2. 02

    Tighten by scope

    Platform guardrails establish the floor. Organizations and agents may add restrictions, not weaken the baseline.

  3. 03

    Keep authority current

    Team membership, tool policy, dataset access, and personal connections are resolved against live state.

One decision, all relevant controls.

The engine composes tenant identity, resource grants, safety policy, tool review state, and budget before admitting work.

Control recordVerified
Tenant
Immutable organization scope bound
Access role
org-member
Dataset
Read grant via team membership
Model
Requested model not granted
Outcome
Denied before provider activity
Evidence
Access denial appended to audit

Separate visibility, use, and administration.

Roles and fine-grained grants keep platform operations distinct from tenant authority. Team membership can grant project and dataset access without sharing credentials.

  • Platform and organization role separation
  • Read, write, and administrative dataset tiers
  • Revocable API keys and short-lived signed tokens

Screen prompts, evidence, outputs, and tool arguments.

Configured guardrails cover personal data, unsafe content, topic rules, and prompt injection. Retrieved content is screened before it can influence an answer.

  • Inbound and outbound content policy
  • Fail-closed screening for retrieved evidence
  • Guardrail verdicts logged without screened text

Put the control plane in front of your first production workflow.

We will map your models, data boundaries, approval points, and evidence requirements in one working session.

Request a demo