Enterprise Architecture
AI Under Governance
A governed signal layer for scoring, anomaly detection, and next-best-action recommendations under policy and human oversight.
Signals matter only when governance remains in control
Institutions need the efficiency of machine assistance without creating opaque decision paths or unmanaged conduct risk.
LL positions AI as a signal layer inside a governed operating model, with explainability, approvals, and evidence capture built into execution.
Table of Contents
Architectural principles
- Separation of policy, execution, accounting, and AI signal layers
- Canonical data normalization across lifecycle stages
- Event-driven state transitions with audit traceability
- Versioned APIs and controlled change governance
- Hybrid-ready deployment topology
System boundaries
- LL governs: workflows, policies, accounting events, audit evidence
- Enterprise retains: core ledger and ERP authority
- Integration layer: REST, OAuth2/OIDC, mTLS, versioning