Manual trigger
Pull fresh telemetry when a review needs current context.
Use on-demand ingest when a committee review, investigation, or issue triage needs a fresh signal pull without waiting for the next schedule.
Feature · LLM telemetry and monitoring
Move AI governance from historical snapshots to live telemetry signals.
SentinelAI turns live telemetry into governance context. Connect performance, fairness, drift, and operational signals to model records, compliance workflows, and trigger rules so teams can respond to what AI systems are doing now — not just what they looked like during the last review.
What this area covers
Bring live assurance signals into governance work instead of leaving them in isolated monitoring tools.
Most AI oversight programs still rely on snapshots: a review at intake, a checklist during approval, and a scramble for evidence when something changes in production. The operational reality often lives elsewhere in dashboards, logs, and monthly reports.
SentinelAI extends governance into operations by giving teams a structured place to manage telemetry connectors, ingest live signals, preserve signal history, and route important findings into governed workflows and evidence trails.
Multi-source signal ingestion
Connect live signals from the telemetry platforms your teams already use.
SentinelAI keeps telemetry and discovery separate by design: discovery enumerates assets, while telemetry ingests live assurance signals that can shape governance follow-up.
Supported connector patterns
PrometheusDatadogAzure MonitorAWS CloudWatchGrafanaMLflowDatabricksCustom
The telemetry domain in SentinelAI supports governed connectors for Prometheus, Datadog, Azure Monitor, AWS CloudWatch, Grafana, MLflow, Databricks, and custom sources. Teams can choose the sources that already hold operational truth instead of standing up a parallel reporting layer.
The goal is not to replace your monitoring stack. It is to make the right live signals available where governance teams make decisions, review evidence, and coordinate follow-up.
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Telemetry connector list showing provider, display name, health status, and ingestion cadence
Connector list with provider, cadence, and health context
Ingestion modes
Control how live signals enter the governance workflow.
SentinelAI supports the operational patterns teams use most often without forcing one ingestion model across every monitoring stack.
Scheduled cadence
Define regular signal collection with a governed cron schedule.
Configure ingest cadence per connector so governance teams see recent signal history without manually requesting exports from operations or data science teams.
Push ingest
Accept direct signal payloads when source systems publish updates.
Single-signal and batch ingest patterns let teams push telemetry into SentinelAI from existing pipelines without rebuilding their whole monitoring estate.
Signal recording and status tracking
Every signal is preserved with the context needed for audit and follow-up.
Telemetry records stay useful because SentinelAI keeps source detail, timing, linkage, and processing state attached to the same signal history.
Signal kinds and processing state
DriftFairnessEvaluationLatencyThroughputError rateCustom
- Signals can carry connector source, signal name, observed value, structured payload, observed timestamp, and ingest timestamp.
- Signals preserve processing state so teams know whether a record is raw, processed, ignored, or failed during downstream handling.
- Signals can be linked to governed model records when that relationship exists, while unmatched signals still remain queryable for later investigation.
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Signal records table showing signal kind, metric name, value, model linkage, observed time, and processing status
Signal history with timing, linkage, and processing state
Telemetry-triggered governance actions
Let live signals create governed follow-up without removing human authority.
Telemetry becomes operationally useful when important changes can trigger work, route context, and refresh evidence without auto-approving decisions.
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Model oversight view showing telemetry evidence, recent anomalies, and trigger-driven follow-up activity
Telemetry context routed into governance follow-up
Evidence integration
Make live telemetry part of the evidence teams already review.
The value of telemetry rises when live signals are available alongside model context, compliance workflows, and stakeholder-ready outputs.
Why signals-as-evidence changes the operating model
- Compliance and risk teams can review recent telemetry without waiting for ad hoc exports from monitoring owners.
- Model workspaces can carry operational context next to ownership, lifecycle, control, and review records.
- Audit and reporting workflows can reference exactly which signal instances informed a decision and when they were ingested.
- Framework-oriented evidence packs can include live operating context without overstating regulatory outcomes.
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Model detail page with telemetry evidence section, linked findings, and recent monitoring context
Telemetry linked into model and evidence workflows
Connector management
Configure, verify, and maintain telemetry connections from one governed surface.
Connector setup is part of the governance operating model too. SentinelAI tracks provider, cadence, health, and error context so teams know whether live-signal pipelines are trustworthy.
Connector health and lifecycle
PENDING_VERIFICATIONACTIVEPAUSEDERROR
- Store provider details, base URLs, credentials, schedules, and connector-specific configuration in one place.
- Verify whether connectors are pending setup, active, paused, or in error before stakeholders depend on the signals they produce.
- Track last connection time and recent errors so broken telemetry pipelines do not quietly create blind spots in oversight.
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Connector detail form showing provider selection, cadence, health status, and credential configuration
Connector configuration with cadence and health state
Core capabilities
Built to support continuous AI oversight in production.
These capabilities help governance teams move from passive monitoring awareness to operationally useful, evidence-backed oversight.
Telemetry capability
Telemetry connector management
Configure governed connectors for monitoring providers, track connector health, and control ingestion cadence from one admin surface.
Telemetry capability
Live signal ingestion
Capture drift, fairness, evaluation, latency, throughput, error-rate, and custom signals from external monitoring systems as governed records.
Telemetry capability
Signal history and processing state
Preserve timestamps, payload context, linkage state, and processing outcomes so teams can audit how each signal entered the governance workflow.
Telemetry capability
Trigger-driven governance actions
Use trigger rules to create tasks, flag records for review, refresh evidence, and notify stakeholders when live telemetry crosses governance thresholds.
Telemetry capability
Evidence integration
Bring recent telemetry directly into model records, compliance reviews, and audit-ready evidence views instead of chasing updates from monitoring teams.
Who it's for
Designed for the teams that need operational reality inside governance decisions.
Live telemetry matters across governance, risk, assurance, and technical operations. SentinelAI keeps the workflow shared without flattening each team's role.
Target users
- Compliance officers and governance teams responsible for ongoing oversight after deployment
- Risk managers who need live signal context before issues turn into governance gaps
- ML platform, data science, and operations teams wiring telemetry into governed processes
- Audit and assurance stakeholders who need evidence tied to production behavior, not just intake documentation
Governance value
- Moves governance from periodic snapshots to continuous, evidence-backed operational awareness
- Reduces manual evidence collection from data science and monitoring teams during reviews
- Creates traceable links between live signals, governance actions, and human decisions
- Keeps monitoring context connected to the same operating model used for model, dataset, and compliance work
How teams use it
A practical flow for telemetry-driven governance.
Telemetry becomes useful when teams can connect sources, preserve signal context, and route findings into repeatable follow-up.
Step 1
Connect telemetry sources
Register telemetry connectors, verify access, and define when SentinelAI should pull or accept incoming signals.
Step 2
Record and route live signals
Store incoming telemetry with timestamps, provider context, and processing state so recent signals are always available for review.
Step 3
Trigger action and preserve evidence
Use governed trigger rules and evidence workflows to turn telemetry findings into follow-up action without automating final approval decisions.
Continue exploring
Telemetry connects to the broader SentinelAI governance operating model.
Model registry
Maintain a governed inventory for AI models and use-case context with lifecycle state, ownership, risk posture, and supporting evidence.
- • Structured model records and intake depth
- • Lifecycle visibility
Compliance workflows
Operationalize evidence collection, control tracking, remediation, and framework mapping across AI systems.
- • Framework-aligned tracking
- • Evidence capture
Reports and certificates
Prepare executive reporting, audit-ready evidence views, and governance certificate workflows without overstating outcomes.
- • Executive reporting layer
- • Evidence-backed outputs
Start telemetry-driven oversight
Bring live AI signals into governance workflows your teams can actually use.
SentinelAI connects telemetry connectors, signal history, governance triggers, and evidence workflows in one operating model. Evaluate it in a demo or explore the broader platform.