AI Analytics
ML-powered intelligence for certificate management.
Predict certificate failures before they happen, detect anomalies in real time, and get smart recommendations to optimize your infrastructure. Move from reactive monitoring to proactive prevention.
Anomaly detected
Certificate issuance spike
+340% above baseline
Issuance analytics
Expected
Actual
Modelanomalydetector
+340%
MonTueWedThuFriSatSun
Model accuracy 98.4%
AI active
Predictive analytics
Predict failures before they impact production
Models trained on millions of certificate events forecast renewals at risk, likely outages, and capacity trends — 30 days before impact.
How it works
- Failure probability scoring
- Risk scoring per certificate
- Trend analysis over months
- Early warning system
Anomaly detection
Spot unusual activity in real time
Behavioral baselines detect unusual certificate usage patterns that indicate misconfiguration or active threats — with automated investigation to speed response.
How it works
- Behavioral baselining per workload
- Real-time outlier detection
- Threat correlation across signals
- Automated investigation runbooks
Recommendations
Smart recommendations for continuous optimization
AI-generated suggestions surface automation opportunities, cost savings, and security improvements — with concrete, one-click remediation.
How it works
- Automation opportunity discovery
- Cost optimization advice
- Security posture improvements
- Best-practice recommendations
ML-powered capabilities
Certificate data, turned into intelligence.
Models trained on millions of certificates across every industry.
Usage pattern analysis
Understand how certificates are actually used across your infrastructure.
- Traffic pattern analysis
- Unused certificate detection
- Optimization insights
Renewal failure prediction
Predict which renewals will fail based on historical patterns.
- Failure probability scoring
- Root cause analysis
- Proactive intervention
Capacity planning
Forecast future certificate needs and optimize resource allocation.
- Growth trend forecasting
- Budget planning insights
- Resource optimization
Outage prevention
Prevent 95% of certificate-related outages before they occur.
- 30-day advance warning
- Automatic remediation
- On-call escalation
Intelligent automation
Models continuously learn and improve automation strategies.
- Adaptive renewal windows
- Auto-tuning policies
- Feedback-loop learning
Compliance prediction
Forecast compliance violations before audits and remediate proactively.
- Violation forecasting
- Framework drift alerts
- Remediation suggestions
From production ML models
10M+
Certificates analyzed
98%
Prediction accuracy
30 days
Advance warning window
Case study
Global · Telecommunications
Predicted and prevented 94% of renewal failures before they impacted production.
“The models flagged a batch of ACME renewals days before they broke. Turned out our DNS provider had drifted. We fixed it on Tuesday instead of paging at 3am on Friday.”
94%
Renewal failures predicted
30d
Median advance warning
3x
MTTR reduction on cert-related incidents
Integrations
Works with every tool in your stack
Streams predictions and anomalies to the observability tools your on-call already uses.
Splunk
SIEM
Datadog
Observability
Grafana
Observability
Elastic
Observability
Prometheus
Monitoring
New Relic
Observability
Sumo Logic
SIEM
Microsoft Sentinel
SIEM
PagerDuty
Alerting
Opsgenie
Alerting
Slack
Alerting
Snowflake
Data
FAQ
Frequently asked questions
Models train on certificate lifecycle events (issuance, renewal, revocation, discovery), certificate metadata (algorithm, validity, chain composition, SAN patterns), and operational signals (renewal latency, deployment success, CA response time). Training data is your tenant's data plus anonymised patterns across the TigerTrust fleet — no raw customer data crosses tenant boundaries. Models can also run tenant-only if data sharing is not permitted (predictions are slightly weaker without cross-tenant baselines).
For renewal failure prediction the current model achieves ~98% precision at 30-day horizons and ~94% at 90-day horizons across the production fleet. Anomaly detection precision depends on baseline quality — after 30 days of history in your tenant, false-positive rates typically settle under 5%. Every prediction ships with a confidence score and a plain-language explanation of which features contributed, so operators can decide whether to act.
Expiry alerts fire on time-to-expiry. AI Analytics predicts things time can't see: renewals that will fail because of DNS drift, deployments that will 500 because of a chain mismatch, workloads that are drifting outside their normal certificate usage pattern (potential compromise indicator). Think of expiry alerts as calendar reminders; the ML layer is your on-call engineer noticing patterns before they become incidents.
Yes. Predictions above a configurable confidence threshold generate structured events routed to PagerDuty, Opsgenie, Slack, ServiceNow, or Jira — with severity based on predicted impact (single certificate vs fleet-wide). Runbook links, root-cause hypothesis, and suggested remediation ship in every event. For lower-confidence predictions, a weekly digest surfaces trends without paging.
Yes, and they self-tune over time. Explicit tuning knobs: per-workload sensitivity, alert threshold, feature weighting for domain-specific signals (e.g. weight financial-services-specific patterns higher for fintech tenants). Implicit tuning: every operator feedback ("useful", "false positive", "resolved") is fed back into the model so accuracy improves per-tenant. Enterprise plans include quarterly model reviews with our ML team for high-stakes environments.
Only anonymised, aggregated patterns — never raw certificate data, subject names, or infrastructure detail. Cross-tenant learning uses statistical features (e.g. "renewal failure rates by CA type", "typical validity distributions by workload category") not individual records. Tenant-only mode disables all cross-tenant learning if your compliance regime requires it. Full detail is in our data processing agreement.
Related resources
Continue exploring
Product
Certificate Lifecycle Automation
The automation layer that acts on the predictions this product surfaces.
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Product
Compliance Monitoring
Pair predictive analytics with continuous compliance evidence generation.
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Solution
Outage prevention
Reference programme for cutting certificate-caused Sev-1s to zero.
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Solution
Enterprise
Applied ML patterns for large infrastructure teams and platform organisations.
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