Earlier service perspective
An earlier approach to this work, retained for readers following our original articles. For the current product, start with a concrete failure and follow the reliability lifecycle.
How teams work with Tensile ↗
About this material
This historical material retains its original perspective. Delivery promises, example metrics, regulatory references and timelines have not been revalidated here. It is not a current offer or a compliance conclusion.
Govern the AI you can see, and the AI you can’t.
The control side of reliable AI. Discover shadow AI and shadow MCP across the fleet, prove policy in the workflow, and produce framework-mapped evidence. SR 11-7, ISO 42001, NIST AI RMF, and the EU AI Act on one infrastructure.
Deployed is not the same as working. Most production AI never moves the number because its output becomes the record before anything proves it earned it. AI Governance governs that moment with evidence, not a reviewer’s attention.
CTA: Book a quick audit
Secondary CTA: Book a discovery call
Start with what’s running without anyone’s sign-off.
Most finance teams discover far more AI than IT recognizes. Governance begins by finding it, then deciding what to allow, contain, or kill.
Shadow AI discovery
Unapproved tools, embedded SaaS AI, and personal accounts running on finance workflows. Each finding gets a risk read and a named owner.
Shadow MCP discovery
Unauthorized MCP servers and tool calls wiring agents to your systems of record. The new attack surface as agents become composable.
Policy evidence in the workflow
Policy that lives where the work happens, not in an unread PDF. Every material output ties to a reviewer decision, a control, and a trace.
Real-time view. Audit-grade evidence.
Real-time view
Drift, hallucination rate, policy violations, multi-turn consistency, vendor exposure. Read by the operator, every day.
Continuous behavior evaluation
Live dashboards and alerts
Vendor and internal-agent coverage
Audit-grade evidence
The same traces, mapped to the framework your auditor is holding. Pulled on demand. No quarterly scramble.
Framework-mapped artefact pack
Source-of-truth trace lineage
SR 11-7, ISO 42001, NIST AI RMF, EU AI Act
One pipeline. Two outputs.
Production traces flow into a measurement engine. The operating view and the audit pack are the same evidence in two formats. There is no second pipeline.
The split matters: operators need live behavior data, while risk teams need framework-mapped evidence. Tensile AI keeps both on the same trace data.
The governance read becomes continuous evidence built on a golden dataset, replacing point-in-time artifact churn.
The four finance auditors actually ask about.
One trace pipeline, mapped to all four. SR 11-7 leads for our segment. The others sit alongside it on the same pipeline.
The bank-grade discipline US examiners already apply to model governance. Our evidence pipeline maps every production trace to the SR 11-7 development, validation, and ongoing-monitoring spine.
The certification track procurement teams ask for. Continuous evidence underneath, audit pack on demand. Auditors run the audit.
Govern, Map, Measure, Manage. We produce the artefacts each function expects, sourced from the same trace pipeline that feeds the operating view.
Risk classification, data governance, post-market monitoring, incident reporting. Mapped to the same trace data. No second pipeline.
The quick audit is the fast read. Governance is the continuous one.
The quick audit, the entry artifact under AI Governance, gives finance leaders an independent operating read in two weeks. From there, AI Governance turns production behavior, owners, controls, and framework mapping into continuous assurance evidence.
We build governable AI solutions, and we are the independent read on whether they hold. The same discipline runs arm’s-length: the audit is available on its own.
Without governance, there is no operating read.
Teams cannot show which AI tools, agents, and outputs are running, who owns them, or which controls have evidence behind them. Policy work stays detached from operating reality.
Governance moves on live evidence.
Production traces, owner mapping, control evidence, and framework coverage give AI Governance the working papers the audit committee expects.
Two ways teams engage us on the risk side.
Continuous evidence is the default. Remediation is the incident-driven shape when something has already moved.
Evidence pipeline
Always-on. Production traces in, framework-mapped evidence out. Operating view and audit pack from the same source. The default shape after a Maturity Model places governance on your roadmap.
Procedure checks
Three to six week engagements. Triggered by drift, a regulator question, vendor exposure, or an AIUC-1 certification ask. We stand up the evidence stream around the incident and hand back an operating loop.
Govern the AI you already run.
Start with the quick audit for the fast read, or a discovery call to scope the continuous evidence stream. Leave with shadow AI and shadow MCP findings, owners, controls, and framework-mapped proof.
CTA: Book a quick audit
Secondary CTA: Book a discovery call
Common questions. Direct answers.
Yes if you have already captured AI value and have something in production worth protecting. If you have not, governance is the wrong problem to solve first. We will tell you that on the discovery call rather than sell you a governance engagement that will not stick.
We do not run SOC 2 audits. For your SOC 2 or ISO 42001 readiness we produce the evidence pipeline that feeds the audit. Auditors run the audit.
Point-in-time tools and single-vendor dashboards generate a snapshot. We are framework-agnostic and continuous. The same infrastructure produces the real-time operating view and the audit-grade evidence trail.
Related links and sources
From context to an observable check
Define the job
What exact customer result is expected, for whom and by when?
Capture the input, intended owner, authoritative result and completion condition.
See this in Tensile ↗
Find the affected work
Which jobs failed, and what portion of the source is visible?
Record job IDs, agent versions, time window, exclusions and source freshness.
See this in Tensile ↗
Explain the gap
Where did expected behavior diverge from observed behavior?
Link source evidence to the hypothesis; keep missing evidence and owner decisions explicit.
See this in Tensile ↗
Retain the expectation
What reusable check would catch this failure without rejecting correct work?
Include positive cases, counterexamples, policy scope and an independently reviewed expectation.
See this in Tensile ↗
Bring the workflow and the evidence gap.
We can start with an affected customer job and the result your team needs to establish.
Discuss a concrete workflow ↗
AI reliability, from production to customer outcome.