Monitor
Continuous monitoring for enterprise AI systems
Most oversight stops the moment a use case gets signed off. Trustible keeps watching after launch, tracking internal signals like cost and drift alongside external ones like vendor changes and new regulation.
Continuous AI governance lifecycle
Intake
Manage Risk
Monitor
Comply
The problem
Approval isn't the finish line, but most teams treat it like one
A use case gets approved, and then nobody checks it again until something breaks or a regulator asks. By then the vendor's terms have changed, the model has drifted, or the incident already happened.
Oversight that stops at launchSet it and forget it
- No visibility once a use case is live
- Vendor changes and incidents surface only when someone notices
- Drift and quality issues go unflagged until performance breaks down
- Review dates exist on paper, not on a schedule anyone tracks
- No record of what was known and when
Monitoring with TrustibleContinuous, routed oversight
- Internal and external signals tracked automatically
- Every metric mapped to a taxonomy built for AI systems
- Threshold breaches surface as alerts, not surprises
- Review dates trigger on schedule, not on memory
Platform capabilities
What's included
Everything you need to track, flag, and review AI after launch
Internal & External Monitoring
Connect internal and external signals. Internal monitoring covers cost, quality, drift, and safety. External monitoring covers vendor changes, incidents, regulation, and litigation.
See how
Metric Taxonomy
No single metric set works for every system, so Trustible ships nine categories pre-built and tied to multiple frameworks.
See how
Alerts
When a metric crosses its threshold, Trustible surfaces it immediately, and your team decides whether to investigate, dismiss, adjust, update, pause, or escalate.
See how
Review Workflows
Set automated review dates so reassessment happens on schedule, not by memory.
See how
The Monitoring Hub
See monitoring coverage across your AI portfolio
The Monitoring Hub shows every tracked use case, its current signals, and any open alerts, with the built-in assistant available to answer questions about what changed and when.
Monitoring overview
128 live use cases · updated 5 minutes ago
OverviewAlertsReviews
128
Use cases tracked
7
Open alerts
12
Reviews due this month
9
Metric categories
Signals this weekBy category
Open alerts7 open
InternalHigh
Output drift above baseline
ExternalMed
Provider retention terms changed
InternalLow
Token spend up 18%
Upcoming reviewsNext 30 days
Sep 307d
Quarterly performance review
Oct 1219d
Vendor terms review
Oct 2027d
Annual reassessment
Assistant Ask about any use case
What changed on Claims Triage Assistant this week?
Assistant · Output drift crossed its threshold on Tuesday and was escalated to the Technical Lead. The model provider also updated its data retention terms, which opened a vendor terms review due Oct 12.
The human layer
Expert-defined thresholds, not guesswork
The metric taxonomy and alert thresholds are built by practitioners who've run AI monitoring programs inside regulated enterprises, and updated as new risks and incidents emerge.
Calibrated
to your systems
Adapted
to your org structure
Defensible
from day one
Frequently asked
Questions about AI monitoring
What does AI monitoring cover after a use case is approved?
Trustible monitors each approved use case against internal signals (cost, quality, drift, and safety) and external signals (vendor changes, incidents, regulation, and litigation). Both land in one feed, tied to the use case they affect.
What is the difference between internal and external AI monitoring?
Internal monitoring tracks how the system itself is performing, such as cost, output quality, drift, and safety. External monitoring tracks changes outside the system that can change its risk, such as a vendor updating its terms, a public incident, new regulation, or litigation.
Which metrics should we track for an AI system?
It depends on the system, since no single metric set fits every use case. Trustible ships nine pre-built metric categories mapped to multiple frameworks, so teams choose what applies instead of defining metrics from scratch.
What happens when a monitoring metric crosses its threshold?
Trustible raises an alert as soon as a threshold is crossed. Your team then decides whether to investigate, dismiss, adjust, update, pause, or escalate, and that decision is recorded.
How often should AI use cases be reviewed after approval?
Review frequency usually follows the use case's risk level, with higher-risk systems reviewed more often. Trustible lets you set recurring review dates for each use case so reassessments happen on schedule, regardless of staffing changes.
Does Trustible replace our model monitoring tools?
No. Trustible works at the governance layer. It connects signals from the tools your teams already use and ties them to use cases, thresholds, alerts, and reviews.
Related Solutions
Explore our platform
Comply
AI Compliance and Audit
Map governance controls to 20+ frameworks, including the EU AI Act, NIST AI RMF, ISO 42001, Australia AI standard, and more.
Explore
Intake
AI Use Case Inventory
Every model, agent, and vendor enters one system the moment it's submitted, ready to be scored.
Explore
Manage Risk
Manage AI Risk and Impact
Score and tier every use case, trigger impact assessments, and route reviews to the right owner, automatically.
Explore
Explore the AI Monitoring Hub
Trustible gives governance teams continuous, documented, and audit-ready oversight across their entire AI inventory.