AI Assurance: governing AI with enterprise discipline

Artificial intelligence creates value when it moves from a proof-of-concept to a service that underpins the P&L. To do this with managerial confidence, a good model is not enough: you need a control plane that makes AI decisions reliable, verifiable and explainable to auditors, customers and insurers. That, in essence, is the purpose of AI Assurance.

AI Assurance: a clear proposition

Treat every use of AI not as an experiment but as a service. Each service has objectives, limits, evidence of correct operation and accountable owners. In practice, AI operates within executable policies; when uncertainty exceeds defined thresholds, it escalates to a human reviewer. The aim is to govern the use of AI more than the algorithm itself: different models can be swapped without weakening the guarantees offered to the business.

From prompt to service commitment

Every use case is classified by impact—informational, binding or automatic—and given explicit SLA/SLOs: availability, response times, minimum acceptable quality and handling of out-of-policy cases. Traceability is complete: who published which rules; which data were used and on what legal basis. Decisions are explainable at process level (policies and sources), even when the model is a black box.

A restrained control architecture

Between enterprise systems and models sits a control plane that applies guardrails before and after inference; brokers the AI’s access to authorised tools (APIs, databases, RPA) with scopes and rate limits; maintains data provenance and consent; records signed logs of inputs, outputs and tool usage; enables shadow mode and replay; and provides a kill-switch and rollback per use case. Where impact is high, a human-in-the-loop reviews decisions using adaptive sampling.

Three illustrative scenarios

Credit & risk triage. AI analyses accounts and public sources, proposes a credit limit with a confidence level and outlines key evidence. If amount and confidence sit within thresholds, approval is automatic; otherwise the case is routed to an analyst. Result: shorter lead times with measured risk.

Revenue protection on orders and invoices. By cross-checking price lists, contract clauses and delivery notes, AI flags economically material discrepancies and—below a defined threshold—applies a correction with notification. Above threshold, it requests a second signature. Result: margin recovered with limited operational overhead.

Regulated knowledge operations. A copilot answers customers and agents using versioned policies, manuals and case history; every response includes a cited source. If no adequate source exists, a “knowledge gap” ticket is opened with clear ownership. Result: higher self-service and a knowledge base that improves systematically.

Adoption checklist (our only list)

• Purpose and decision class defined, with estimated economic impact.

• Executable policies versioned (rules, thresholds, fallbacks) and data lineage documented, including legal bases.

• Metrics: quality, latency, escalation rate, and coverage of out-of-policy cases.

• Safety: robustness testing (bias, injection, leakage), shadow mode before go-live, and tool-usage limits.

• Governance: roles (owner, reviewer, approver), complete audit trail, and a tested kill-switch.

Where the ROI comes from

The return is not “soft”. Capacity is released in critical functions by reducing rework on repetitive cognitive tasks; the per-process error allowance becomes explicit and therefore manageable; insurability and compliance improve through signed logs and a process design that supports coherent explanations. In short: more first-time-right decisions, fewer assisted escalations, and higher data and service quality.

Uniquon’s role

Uniquon designs and operates AI Assurance control planes: a catalogue of use cases with decision classes; policies in executable form; integration with your existing tools; end-to-end observability; safety plans and shadow mode prior to launch. The objective is straightforward to state and demanding to deliver: scale AI while keeping net risk under control, with clear commitments to customers, auditors and the Board