The quick audit under AI Governance.

The fast way in. A two-week independent read across the gateways, scanners, SIEM and observability, MDM, IDP, and SaaS systems you already run, plus every AI tool, agent, and embedded feature.

In two weeks, the audit turns stack signals, workflows, usage, spend, and evidence gaps into a board-ready opinion: where AI earns its place, where it exposes the balance sheet, and what to govern next.

The first read makes AI value and AI risk specific.

Representative findings show the shape of the deliverable before the full working-paper pack follows.

Outside the approved estate, with no owner, policy evidence, or exception path.

Annualized AI license spend above observed usage needs, driven by seats on higher plans than workflow usage supported.

Live in workflows with minimal eval coverage, too thin to support an audit opinion without exceptions.

Recoverable capacity from role-level AI fluency gaps in recurring finance workflows.

Where AI is creating value. Where AI is exposing risk.

The Audit separates visible adoption from unmanaged Shadow AI, then ties gateway, scanner, identity, device, SaaS, code, and observability evidence to workflows, spend, risk, and outcomes.

Finance leaders see which AI is approved, which AI is already running outside IT’s view, which workflows are producing outcomes, and where risk lacks evidence or an owner.

Show which workflows deserve the next dollar.

The Audit returns a workflow evidence map: what AI workflows exist, who owns human review, what data each workflow touches, what proof exists, and which workflow should move next.

Workflow

Which AI workflows already run, which tools or agents touch them, and which business decision each workflow influences.

Owner

The named business owner, human reviewer, risk owner, and escalation path for material outputs.

Proof

The source records, traces, reviewer decisions, confidence markers, exceptions, and change logs already present or missing.

Next move

Which workflow to fund, harden, monitor, pause, or hand to AI Fluency because the human owner is the bottleneck.

The Audit works with the stack you already run.

Gateways, scanners, SIEM, observability, MDM, IDP, SaaS/admin systems, code hosting, and SDK traces remain in place.

AI gateways and proxies

Traffic through controlled routes: prompts, responses, policies, apps, egress patterns, and evidence freshness.

Agent security and MCP scanners

Risky tool calls, MCP servers, agent behavior findings, control failures, and the owner each finding needs.

SIEM, observability, and SDK traces

Runtime events, app traces, incidents, eval results, and source-anchored evidence from internal agents.

MDM, IDP, SaaS/admin, and code hosting

Device coverage, identity groups, enabled AI features, repos, service owners, and workflow context.

The Audit is two weeks because the start is ready.

Access, exports, stakeholder routing, and materiality inputs are not a hidden delivery phase. They are the entry conditions for a fixed-scope two-week Audit.

Executive sponsor and Day 1 kickoff owner named

MDM / EDR deployment path or endpoint export confirmed

IDP read access or export path confirmed

SaaS admin exports for productivity, CRM, helpdesk, collaboration, and AI tools

Current AI policy, exception process, vendor list, and known AI use cases collected

Existing evals, model-risk, security, privacy, and audit artifacts collected

Stakeholder roster and survey distribution path confirmed

Materiality inputs prepared by finance segment and customer risk appetite

Optional Shadow MCP Discovery scope confirmed for developer fleets

Missing prerequisites either move Day 1 or become a named scope limitation in the Audit Opinion. They do not stretch the public offer beyond two weeks.

Four moves. One 2 week Audit.

The two-week clock starts after prerequisites are complete. Required access that does not arrive becomes a scope limitation, not an invisible extension.

Prerequisites locked

Sponsor, materiality inputs, stakeholder routing, access paths, exports, and optional Shadow MCP scope are ready before the two-week clock starts.

First-read memo

Cross-cutting read across AI Transformation, AI Governance, and AI Fluency: approved AI, Shadow AI, workflow candidates, usage, spend, and evidence gaps.

Cross-pillar baseline

Tool inventory, license utilization, workflow candidates, role capability gaps, risk posture, eval coverage, and evidence systems.

Opinion and sequence

Audit Opinion, Workflow Evidence Map, materiality exceptions, working-paper package, and a recommendation for what to fund first, next, and later.

The same two-week motion also produces the first golden dataset for one priority surface when the Audit scopes an eval benchmark.

Every Audit lands one of four opinions.

The same discipline finance has used for a century, applied to the AI estate.

AI estate visible. Material risk contained.

With exceptions noted.

Do not extend in current state.

Access was insufficient.

AI estate is visible, evidenced, and material risk is contained.

Inventory complete, Shadow AI under threshold, governance evidence in place, adoption outcomes traceable.

With exceptions noted.

Most of the estate is in order. Specific named exposures require remediation before next quarter.

Named Shadow AI hotspots, specific evals gaps, specific roles below fluency baseline.

Material exposures span multiple anchors. New AI workstreams should pause until remediated.

Unmanaged Shadow AI on regulated workflows, no evidence systems, no policy enforcement, internal agents in production with no evals.

Access was insufficient.

Visibility access was insufficient to issue an opinion.

MDM coverage gap, IDP access denied, fleet too small for valid sample.

What’s a material AI failure?

Financial audit set materiality thresholds a century ago, often around 5% of net income. AI teams are still guessing.

The Audit defines materiality per use case through impact severity across regulatory, financial, and reputational harm, plus frequency across one user, one query type, one workflow class, or portfolio-wide exposure. Findings above the materiality threshold land in the opinion as audit exceptions. Findings below land in the appendix.

Materiality is set jointly in the kickoff session. Tensile AI walks the customer through industry defaults for their finance sub-segment. The customer signs off. The threshold lands in the engagement letter, and the opinion is issued against it.

A finding without a materiality threshold is a complaint. A finding above threshold is an audit exception.

The numbers a board has never seen before.

Most boards see vendor counts and seat licenses. The Audit returns duplicate spend, Shadow AI, internal agents in production, adoption outcomes, risk without evidence, and the workforce-fluency stage. On one page.

Audit findings. Sequenced workstreams.

The Audit shows which AI work is creating value, which Shadow AI and policy gaps create exposure, and which teams need enablement. That decides what runs next.

AI Transformation

Turn the adoption findings into production workflows with measurable revenue, margin, or cycle-time outcomes.

AI Governance

Turn the risk findings into policy, continuous evidence, and framework-ready proof before the audit committee asks.

AI Fluency

Turn usage and capability gaps into role-specific enablement, manager telemetry, and stronger day-to-day AI judgment.

The Audit fits the risk model finance already uses.

Three lines of defense means business owns the work, risk oversees the controls, and audit tests whether the evidence holds.

Continuous evaluation evidence spans all three lines, feeding operating decisions, governance proof, and audit testing without belonging to any single anchor.

LINE
PLAIN-ENGLISH ROLE
TENSILE AI ROLE
FIRST LINE 1LOD
Business teams own the AI workflow
AI Transformation + AI Fluency Workflow owners capture the upside and build the role-level fluency to operate it.
SECOND LINE 2LOD
Risk and compliance oversee the controls
AI Governance Policy, evidence, exception handling, and framework mapping.
THIRD LINE 3LOD
Internal audit and external auditors rely on the evidence
AI Audit Opinion, materiality, working papers, and substantive testing evidence.

Common questions. Direct answers.

No. It is the quick-entry artifact under AI Governance. It maps AI visibility, Shadow AI, workflow value, spend, usage, risk, and evidence gaps, then names which workstream to fund next. The wider work is AI Transformation, AI Governance, and AI Fluency, measured by Evals.

The Audit is fixed-scope for medium-to-large finance teams. Smaller organizations are scoped differently because the discovery footprint changes.

Two weeks after prerequisites are complete. The first 72 hours produce the first-read memo; Days 4-10 build the cross-pillar baseline; Days 11-14 land the Audit Opinion, working-paper package, and sequenced recommendation.

Sometimes, if you already know your AI inventory, Shadow AI exposure, usage depth, risk posture, and exact workstream gap. Most teams that try to skip the Audit pause two weeks in to build that baseline anyway.

Before Day 1, we lock employee and device footprint, MDM or endpoint export path, IDP and SaaS admin access, gateway and scanner exports, SIEM or observability evidence, developer tooling scope, internal agents, materiality inputs, and stakeholder routing.

No. The AI Audit is a use-case-specific operating diagnostic that produces evidence your auditors can review. We sit one layer below the framework audit.

No. Gateways answer what traffic flows through controlled routes. Agent-security and MCP tools answer which agent behaviors are risky. The Audit ingests those signals alongside identity, device, SaaS, code, and observability evidence to show value, exposure, evidence gaps, and fluency gaps.

Yes. Same memo. The output is a structured audit memorandum with the opinion, materiality threshold, scope, exceptions, and remediation sequencing on the first three pages. The committee gets the same shape they already read from external auditors. No separate audit-committee cut.

Yes. We deliver the working-paper package. Big-4 and mid-tier audit firms increasingly co-engage with us when their finance clients need AI assurance evidence the framework auditor cannot produce alone.

Keep the opinion current.

The 2 week Audit is the first opinion. Refreshes are smaller: quarterly to keep the baseline current, and event-driven when the AI estate materially changes.

Full diagnostic. Opinion issued.

Refresh the inventory, re-run materiality scan, refresh the opinion.

Model swap, vendor change, new internal agent in production, regulatory letter.

The cadence is not another full engagement. It is how the opinion stays defensible after tools, models, vendors, and internal agents change.

Get the quick audit under AI Governance.

Leave with an independent opinion: AI value, AI risk, fluency gaps, owners, and the next funded workstream. We build governable AI solutions and we are the independent read on whether they hold. The audit is available arm’s-length, on its own.

Related links and sources

AI Transformation Turn the adoption findings into production workflows with measurable revenue, margin, or cycle-time outcomes. Open ai transformation →
AI Governance Turn the risk findings into policy, continuous evidence, and framework-ready proof before the audit committee asks. Open ai governance →
AI Fluency Turn usage and capability gaps into role-specific enablement, manager telemetry, and stronger day-to-day AI judgment. Open ai fluency →