AI Audit
What AI is actually running?
Approved tools, Shadow AI, embedded SaaS AI, internal agents, spend waste, and evidence gaps become one current-state read.
The operating layer for AI value, AI risk, and workforce fluency.
For finance teams that need one board-ready view across gateways, scanners, identity, devices, SaaS, code, observability, agents, and workflows.
Operating view
AI value, AI risk, and workforce fluency need the same view.
The platform turns source-system signals into the four finance questions that decide what gets funded, contained, evidenced, or taught next.
AI Transformation
Where is AI creating value?
Workflow usage, adoption depth, and outcome signals show which AI investments deserve more funding and which should consolidate.
AI Governance
Where is AI creating exposure?
Policy gaps, agent behavior, tool-call risk, and audit evidence gaps are mapped to owners, materiality, and control work.
AI Fluency
Is the workforce keeping up?
Role-level capability, manager telemetry, and usage patterns show which teams need enablement before the next workflow lands.
Stack coverage
Your existing AI stack becomes one operating read.
Gateways, scanners, SIEM, observability, MDM, IDP, SaaS/admin systems, code hosting, endpoint agents, and SDK traces all stay useful.
Source
AI gateways and controlled routesPrompt, response, policy, app, and egress events that already pass through approved AI routes.
Which AI use is controlled, where evidence is fresh, and what sits outside the route.
Source
Endpoint agents, MDM, and IDPDevice coverage, identity, group, fleet, and unapproved desktop usage signals.
Which employees, teams, and devices create material Shadow AI or fluency gaps.
Source
SDK traces, code hosting, and observabilityInternal-agent spans, tool calls, eval results, repos, service owners, incidents, and runtime logs.
Which agents create value, drift, policy exceptions, or audit evidence gaps.
Source
SaaS/admin systems and security telemetryEnabled AI features, admin exports, SIEM findings, security alerts, and workflow context.
Where embedded AI changes spend, workflow outcomes, exposure, and ownership.
Tools and operating questions
Different tools answer different questions.
TrustEvals respects the control stack. The platform assembles its signals into the finance questions that decide value, exposure, evidence, and fluency.
AI gateways
What traffic flows through controlled routes?
TrustEvals uses those signals to separate approved adoption, policy coverage, and evidence freshness from the unmanaged surface.
Agent-security and MCP tools
What agent or MCP behavior looks risky?
TrustEvals connects the finding to materiality, workflow owner, remediation sequence, and framework evidence.
SIEM, observability, and security tools
What happened in the systems?
TrustEvals pulls the AI-relevant events into the operating view instead of leaving them as isolated telemetry.
SaaS/admin, MDM, IDP, and code hosting
Who has access, what is enabled, and what shipped?
TrustEvals turns identity, device, vendor, and repo context into AI Audit, Governance, Transformation, and Fluency decisions.
TrustEvals operating view
What AI is creating value, exposure, evidence gaps, or fluency gaps?
That board-ready read is the layer above point tools. The source systems remain in place.
One pipeline. Two outputs.
The live operational view. The audit-grade evidence. Same trace data.
The architectural choice that matters
Architecture
The architecture, in one picture.
Layer 5
Executive Intelligence
Single pane of glass · maturity scoring · benchmarks
Layer 4
Compliance Mapping
Frameworks (ISO 42001, NIST AI RMF, AIUC-1) · regulations (EU AI Act, GDPR, CCPA, Colorado AI) · guidelines (Singapore AGA, OECD, custom)
Layer 3
Policy Evaluation
Baselines · thresholds · policy-as-code per use case
Layer 2
Data Classification
Structured metadata extraction from traces
Layer 1
Raw Production Traces
Every interaction, every agent, every tool
Same stack for a 50-person pilot and a 50,000-employee rollout. The layers above compose (framework, policy, executive); the layers below stay stable (data, traces). Layer 4 handles frameworks, regulations, and guidelines.
Framework
The 12 levers every finance AI team has to pull.
Every framework, consultant, and point tool has its own model. We published ours because the ones in market are either too narrow (governance only) or too broad (transformation theater). The 12 Levers is the CIO’s reference guide: one page, every lever, mapped to who owns it.
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Lever
Question
TrustEvals
01
Discovery & inventory
“What AI exists in our org?”
Core
02
Usage depth & breadth
“How deeply is AI used?”
Core
03
Workflow integration
“Is AI embedded in processes?”
Core
04
ROI & value attribution
“Is the investment paying off?”
Core
05
Training & enablement
“Are people getting better at AI?”
Strong
06
Shadow AI management
“What AI is used without approval?”
Core
07
Spend intelligence
“Are we wasting money?”
Strong
08
Policy & governance
“What rules govern AI use?”
Core
09
Agent behavior evaluation
“Are our agents doing what they should?”
Core: deepest moat
10
Cross-org visibility
“What does the full landscape look like?”
Core
11
Change management
“Is our org ready at scale?”
Services
12
Benchmarking & maturity
“How do we compare to peers?”
Strong
The baseline problem
Frameworks tell you what to track. They don’t tell you what “good enough” looks like.
Every major framework, including NIST AI RMF, ISO 42001, Singapore’s agentic AI guidelines, and the EU AI Act, identifies categories of risk: bias, hallucination, data leakage, safety. None of them define acceptable thresholds for a specific implementation.
A bias metric of 0.12: is that compliant? What about 0.15? The answer depends on the use case, the population, and the risk appetite of the organization. That judgment call is where evaluation actually happens.
Assurance requires baselines. Baselines require continuous measurement. That’s why the platform is built the way it is.
Bias metric · live agent · 30 days
The agent crosses the customer baseline before the regulator threshold, caught early, fixed before the customer notices.
Continuous
Why continuous beats comprehensive.
Point-in-time certification was built for deterministic systems. AI isn’t deterministic. A quarterly attestation means up to 90 days of unmeasured behavior between audits. Customers notice first.
Continuous evaluation runs with the system. Evidence is always fresh. An auditor asks a question on a Tuesday; you answer on Tuesday.
“A chatbot handling 200,000+ interactions per week cannot be assured through quarterly reviews or screenshot evidence.”
Integrations
Works with the stack you already have.
TrustEvals is stack-agnostic. We integrate with the data and observability layer your environment already runs on (Snowflake, Databricks, ClickHouse, DuckDB, Postgres, Supabase, your ETL/ELT, dbt, Cube.dev) and with the operational systems your AI is actually used inside (CRMs, ERPs, customer-success platforms, helpdesk, knowledge, identity, code hosting, and a long tail of others). The integration is implied; we don’t enumerate every logo. If your stack isn’t supported, ask. We’ve added five new integrations in 2026 already.
Services layer
Platform plus services. By design.
TrustEvals is platform first. The product creates the operating view: what AI is running, where value is showing up, where risk is building, and what evidence is current enough to defend.
Where customers ask for practitioner depth, we run engagement packages around the platform. Many start with the AI Audit (two weeks), the engagement we first shipped to a cybersecurity and compliance services firm and now use as the cleanest entry read. From there: AI Transformation engagements (the PE-backed mid-market shape: full adoption + vendor eval + governance foundation), Evals (for AI product companies: eval pipelines, red teaming, optimizer), and Remediation Advisory (incident-driven).
We are not a dev shop. We don’t sell engineers by the hour. Every engagement transfers methodology. The platform is the backbone, practitioners are how it gets applied inside a customer’s environment.
See engagements →FAQ · for engineers
What a platform lead asks us first.
The SDK is designed to stay off the hot path: asynchronous capture, batched traces, and out-of-band evaluation through the Ingest Gateway and Eval Engine. We start with the narrowest trace path that proves the evidence loop, then broaden instrumentation with your engineering team.
They aren't. Customer traces are single-tenant by architecture, not by policy. Our evaluation models run on your tenant; the platform does not train on your data. This is a property of the build, not a promise in the MSA.
Layer 4 is a mapping, not a monolith. When a new framework ships (or your compliance team writes an internal one), we add a mapping layer on top of the same Layer 1–3 infrastructure. Customers using TrustEvals for ISO 42001 in 2026 will use it for the next five frameworks without replumbing.
Start with the 2-week AI Audit.
Leave with the operating read: AI value, AI risk, fluency gaps, owners, and the next funded workstream.