Elastic-Native Accelerator

Your P1 Incident Just Fired. Your AI Triage Assistant Already Has a Runbook.

AI Triage Assistant analyzes the alert, pulls context from Elasticsearch, and generates a remediation plan — in seconds. Reduce MTTR 50-70%. Every engineer responds like a senior.

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trending_down 73% MTTR Reduction dns Self-Hosted LLM Support emoji_events Elastic Innovation Award 2023 schedule 24-Hour Response SLA
The Triage Bottleneck

Your Incident Response Is Only as Fast as Your Escalation Chain

Junior Engineers Escalate. MTTR Climbs.

When a P1 fires at 2am, your junior on-call engineer doesn't have the context to act. They escalate to a senior. That escalation adds 30-60 minutes to every incident. Your MTTR is a function of who's on call — not your tooling.

300 Alerts Per Shift. No Context for Any of Them.

Your SOC analysts face hundreds of alerts every shift. Most are noise. The cost of missing one real threat is a breach or a customer-facing outage. More alerts than analysts. The math doesn't work.

Post-Incident Documentation: Manual, Inconsistent, Skipped.

After every incident, someone is supposed to write up what happened. It takes 30 minutes. It's inconsistent. It's usually skipped. Your compliance team has no evidence. Your next on-call has no playbook.

Five Steps from Alert to Resolution

1

Alert Fires

Incoming alert from Elasticsearch alerting, PagerDuty, Opsgenie, or your SIEM platform triggers AI Triage Assistant.

2

AI Analyzes

LLM model analyzes alert type, severity, and triggering conditions. Not keyword matching — contextual analysis.

3

Elasticsearch Context Pull

Queries Elasticsearch indices for correlated data: historical incidents, affected service topology, runbook library.

4

Triage Report Generated

LLM generates a structured triage report: root cause hypothesis, blast radius estimate, ranked remediation steps.

5

Ticket Auto-Created

Jira or ServiceNow ticket created automatically with the full triage report. Engineer acts on AI guidance immediately.

Five-step alert-to-resolution flow diagram with connected nodes and timeline bar showing MTTR reduction
45 min before
12 min after

What AI Triage Assistant Does

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LLM-Powered Root Cause Analysis

AI reads the alert and correlated Elasticsearch context to generate a root cause hypothesis. Not a keyword search. A contextual analysis that considers alert type, historical patterns, and service dependencies.

assignment

AI-Generated Remediation Runbooks

Step-by-step remediation instructions generated per incident. Pulled from your historical runbooks and past resolution patterns. Every P1 gets a playbook — written by AI, grounded in your institutional knowledge.

radar

Blast Radius Estimation

Estimates downstream service impact at time of alert. Gives on-call engineers the "how bad is this" answer immediately — before the blast radius expands.

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Automated Ticket Creation

Jira, ServiceNow, or PagerDuty ticket created automatically with AI triage report pre-populated. No manual documentation during the incident. Evidence generated as a byproduct.

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Multi-LLM Support

OpenAI, Anthropic, Ollama, and self-hosted models. Deploy with your existing AI infrastructure or SquareShift's recommended configuration. No vendor lock-in. Your data stays where you decide.

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Closed-Loop Learning

Engineers confirm or correct AI suggestions after resolution. The model improves continuously from production feedback. Your institutional knowledge compounds with every incident.

The Numbers That Matter

73%
MTTR reduction (45min → 12min)
P1
Junior engineers handle P1s independently
0 min
Incident documentation — zero manual effort

Before

P1 fires. On-call engineer checks the alert. Doesn't have context. Escalates to senior engineer. Senior engineer wakes up, opens laptop, gets context. Diagnosis begins. 45 minutes gone before the first remediation step.

After

P1 fires. AI Triage Assistant generates triage report: root cause hypothesis, blast radius, remediation steps. On-call engineer reads the report and acts. Ticket auto-created. 12 minutes from alert to first remediation action.

AI Triage Assistant doesn't just respond — it learns. Every confirmed suggestion makes the next triage faster. Every corrected suggestion makes the next triage smarter. Your institutional knowledge compounds with each incident resolved.
Ecosystem Fit

The AI Intelligence Layer in Your SIEM Stack

AI Triage Assistant works with the SquareShift SIEM accelerator suite — each accelerator strengthens the others.

Alarm Noise Suppression → AI Triage

Only real alerts reach AI Triage. Alarm Noise Suppression eliminates 80-90% of false positives upstream. AI Triage Assistant analyzes signal, not noise.

Threat Correlation Engine → AI Triage

Threat patterns from the Correlation Engine add context to every triage analysis. AI Triage Assistant sees the threat landscape, not just the individual alert.

AI Triage → Compliance Reporter

Every triage action generates compliance evidence automatically. SOC2, PCI-DSS, HIPAA audit trails are a byproduct of every incident response.

SIEM Readiness Assessment

AI Triage Assistant is a primary component of the SIEM Readiness Assessment engagement. Deploy as part of a full SIEM implementation or standalone.

Also applies to AI observability contexts. Triage LLM performance alerts, cost anomalies, and quality degradation incidents from SquareShift Atlas. Same AI triage capability, applied to AI workloads.

Learn more about SquareShift Atlas →

Customer Testimonial

Reduced MTTR from 45 minutes to 12 minutes. First-responders get AI-generated runbooks instantly.

— SOC Lead, FinTech Company
AI Triage Assistant Deployment · SquareShift Client

AI-Assisted Triage 90% False Positive Reduction — in conjunction with Alarm Noise Suppression

Payments Platform SOC2 case study — audit-ready with AI-generated compliance evidence

Common Questions

Direct answers to what engineering and security teams ask before deploying AI Triage Assistant.

24-hour response to all demo requests.

OpenAI, Anthropic, and self-hosted open-source LLMs including Ollama, Llama, and Mistral. No vendor lock-in. Deploy with your existing AI infrastructure or SquareShift's recommended configuration.
No. It reads your existing runbooks and applies them to each incident. The AI generates suggestions from your institutional knowledge — not generic templates. Your runbooks become the training data for every triage.
For novel incidents, the AI analyzes the alert type and available Elasticsearch context, generates the best available hypothesis, and flags it as low-confidence — prompting human review rather than false confidence.
Yes, within guardrails. AI Triage Assistant provides ranked suggestions with rationale. Engineers execute the recommendations — the AI does not take action. Senior review of novel or low-confidence incidents is still recommended.
Available both ways. Included in SIEM and AI observability engagements. Available standalone with custom implementation. Pricing is gated — schedule a demo to scope your deployment.

Your Next P1 Doesn't Have to Start with an Escalation

Schedule a 15-minute demo. See how AI Triage Assistant generates triage reports from your Elasticsearch data — and why teams cut MTTR by 73%.

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