Monitor

AI monitoring

Continuous monitoring for enterprise AI systems

When monitoring is tied to a model, critical governance context gets lost as systems evolve. Trustible monitors the use case across models, vendors, and agents, preserving risk visibility, ownership, and audit-ready evidence.

Request a demo Read the white paper

Continuous AI governance lifecycle

Intake

Manage Risk

Comply

Monitor

What's included

Track the use case, not the model

A model is one component, a use case (the claims assistant, the underwriting app, the fraud model) is the whole system your team approved. Trustible tracks it as one record with one owner, so a vendor swap or an added agent updates the record instead of breaking it.

Monitor the modelThe component view

one endpoint at a time, blind to the rest
breaks the moment a model is swapped
no owner, no risk profile
blind to vendor-hosted tools

Monitor the use caseOne governed system

runs on many models as one thing
survives vendor swaps and added agents
one owner, one risk profile
same surface, self-hosted or vendor-hosted

Connect internal and external signals

Internal monitoring covers cost, quality, drift, and safety. External monitoring covers vendor changes, incidents, regulation, and litigation, the signals most governance teams haven't built yet, and where the highest-impact risks tend to show up first.

Search tracked metrics…

Filter 4 of 4 metrics

Metric	Latest	Owner	Frequency	Source	Updated	Trend
Cost per request Spend per request across the use case.	$1,255	AGAI Governance Committee	Monthly	API	18d ago	⋮
Groundedness / faithfulness Whether answers are supported by the sources retrieved.	0.91	ACAna Contributor	Monthly	RAGAS	4d ago	⋮
Input / data drift How far inputs have shifted from the training baseline.	+2.3%	TLTech Lead	Weekly	API	2d ago	⋮
Jailbreak attempts Prompt-injection and jailbreak attempts detected.	44 / day	TLTech Lead	Daily	Manual	1d ago	⋮
Vendor model update Model v2.4 shipped by the provider.	v2.4	RMR. Mehta	Continuous	Vendor feed	3d ago	—⋮
Public incident match Comparable-system failures reported publicly.	2 matched	GLGov Lead	Continuous	News feed	6d ago	—⋮
Regulation change New obligations for high-risk providers.	EU guidance	GLGov Lead	Continuous	Reg feed	9d ago	—⋮
Litigation filed Enforcement action on a comparable use.	1 new	GLGov Lead	Continuous	Case feed	21d ago	—⋮

Start with a built-in metric taxonomy

No single metric set works for every system, so Trustible ships nine categories pre-built and tied to NIST, OWASP, and sector rules like Colorado's AI Act. Ownership defaults by role, and anything an API can't measure becomes a documented judgment call instead.

Add a metric to track

Search the taxonomy of evaluations and metrics…

Alerts, and your team's responses, saved automatically

When a metric crosses its threshold, Trustible surfaces it immediately, and your team decides whether to investigate, dismiss, adjust, update, pause, or escalate. Whichever is chosen is what enters the record, not the alert.

Security · Jailbreak attemptsLast 10 samples

Review Dismiss Adjust Update Pause Escalate

The Monitoring Hub

See monitoring coverage across your AI portfolio

Monitoring Hub · Portfolio

Coverage by designation

96%

of high-risk use cases have required monitoring configured and collecting

312

Active systems

91%

Monitoring Controls satisfied

Open alerts

Required set satisfied

EU High-Risk Provider96%

Deployer88%

Colorado AI Act79%

Sample alerts

Open alerts · portfolio14 open

Claims processing assistantCost per requestOverdue 9dRM

Customer support chatbotJailbreak attemptsHighTL

Metrics catalog

Cost & Usage

Active users weeklyCost per request

Agent Behavior

Task completion rate

Compliance

Audit-log completeness

Security

Authentication anomalies dailyJailbreak attempts

AI Quality

GroundednessBenchmark score

Ethics & Responsible AI

Bias / fairness disparity quarterly

Frequently asked

Questions about AI monitoring

What's the difference between AI monitoring and model monitoring?

Model monitoring tracks an individual endpoint, reporting accuracy, latency, token use, and drift. AI monitoring tracks the use case: the system you deploy, own, and answer for, evaluated against the risk tolerance it was approved under. That stays answerable through a vendor swap or a migration, because the use case carries one owner, one risk profile, and one governance history.

Does this replace our existing observability tooling?

No. Model observability and MLOps tools sit a layer below and remain useful, and Trustible consumes their signals where they exist, so a threshold breach can push a sample and open a workflow. Trustible operates at the governance layer above, establishing whether the obligation is met, whether an owner reviewed the signal, and whether the record shows it.

How does monitoring data get into Trustible?

Three ways: a person records it manually, a pipeline pushes it through the API, or an agent submits it through MCP. Manual entry isn't a fallback, since a qualified reviewer recording a determination on a defined cadence is the monitoring itself. Cost and usage pull cleanly from most model platforms, and native integrations are on the roadmap.

Does monitoring work if a system is fully hosted by a vendor?

Yes, because metrics don't require raw model access. Cost, usage, and many quality and safety checks can be entered manually or pushed through the API from wherever you already track them, whether a vendor invoice, an evaluation harness, or a support queue. A vendor-hosted use case receives the same Monitoring tab and the same controls as a self-hosted one.

Can we define our own metrics instead of using the taxonomy?

Yes. The taxonomy covers most of what a typical use case requires, and any use case can add a custom metric, numeric or boolean, with its own cadence, owner, and alert condition.

Does an alert firing automatically pause the system?

No. Pausing is one of several responses a person can choose, it isn't automatic.