EVE for Financial Services

For banks, asset managers, and fintechs deploying AI agents into regulated workflows that need deterministic control decisions and independently verifiable evidence.

The problem

In financial services, an AI agent that can move money, change records, or communicate with customers sits inside a control environment that expects deterministic policy enforcement and a durable, checkable audit trail. Model reasoning alone does not meet that bar.

How EVE helps

  • Deterministic pre-execution enforcement. EVE CoreGuard returns a deterministic ALLOW / BLOCK / MODIFY decision before a governed action executes, with no LLM in the decision path. Policy packs express the rules; the same inputs yield the same decision.
  • Evidence for the control file. Each governed decision can produce a signed evidence record bound to the decision (Ed25519 in the production configuration, HMAC-SHA256 fallback), supporting evidence collection.
  • Independent verification. A reviewer verifies signed evidence independently and offline in Python, Node, or the browser without trusting the EVE server.
  • Sequence and budget controls. EVE can block individually-permitted actions that combine into a prohibited sequence, and enforce deterministic budget/resource limits — useful for containing agent behavior across a workflow.
  • Non-authoritative candidate testing. Authenticated customers can evaluate a candidate policy in shadow — live or by historical replay — and see signed divergence reports with modeled-effect estimates, without changing the authoritative decision.
  • Control mapping. EVE can map controls to frameworks descriptively and produces records useful for review. Mappings are descriptive, not certification.

Framing that stays accurate

EVE supports evidence collection, helps enforce policy, and can map controls to frameworks. It does not determine regulatory outcomes, guarantee compliance, or issue certification.

Readiness

  • EVE SDK core is a RELEASE CANDIDATE WITH EXCLUSIONS.
  • The core governance architecture is pilot-validated; signed evidence and offline verification are SUPPORTED.
  • The Shadow subsystem is authenticated pilot-ready.
  • Framework adapters other than the generic adapter are experimental.

Limitations

  • EVE supports evidence collection and helps enforce policy; it does not guarantee compliance or issue certification, and does not determine regulatory outcomes.
  • Divergence estimates from shadow are modeled effects from replay/sample, explicitly not observed production outcomes; shadow cannot alter the active verdict.
  • Independent verification requires the Ed25519 public key; the HMAC fallback is symmetric and not independently verifiable.
  • Sequence state is per-session and in-process in the pilot; durable budgets across instances require a shared store.
  • The pip client governs via hosted mode (requires a reachable endpoint); embedded evaluation is the EVE service.
Part of the EVE AI Core control plane Deterministic AI Governance Control Plane → Policy decisions that return the same result for the same input every time, before execution.