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To triage Google SecOps alerts with AI, use either the configurable Vertex AI integration in a playbook or Google SecOps’ built-in Triage and Investigation Agent (TIN). They are separate paths: Vertex AI lets a playbook run configured generative tasks, while TIN investigates eligible alerts and returns a verdict with supporting reasoning. Use Cloud Run functions only for selected ingestion tasks—not as the AI triage engine—and retain analyst review before consequential response actions.
What “deep-sea alert triage” means in this workflow
“Deep-sea” is not a documented Google SecOps product or feature name. The practical workflow is AI-assisted alert triage in Google Security Operations, often still called Chronicle in documentation paths and API names. It combines alert prioritization, evidence gathering, an AI-assisted assessment, and a controlled response.
The key design choice is whether to use a configurable Vertex AI task inside a playbook, the built-in TIN investigator, or both for distinct purposes. Cloud Run functions may support selected threat-intelligence ingestion workflows, but the documented role is ingestion rather than alert investigation.
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| Path | What it does | Important constraints |
|---|---|---|
| Vertex AI integration in a playbook | Runs configurable generative prompts for tasks such as contextual entity summaries, EML analysis, and JSON transformations. The playbook can use the output in its workflow. | Requires service-account and IAM setup, plus configuration for API root, project, model, and location. Google recommends GA models in production because Preview models may be unstable, change incompatibly, or have limited support. |
| Triage and Investigation Agent (TIN) | Investigates supported alerts and returns a true-positive or false-positive verdict with a summary of its reasoning. Its documented tools include SecOps searches, GTI enrichment, command-line explanation, and process-tree reconstruction. | Runs only on data ingested using Google SecOps SIEM, not alerts from a Google SecOps SOAR connector. Tenant eligibility and required permissions must be confirmed. Google states TIN is not FedRAMP or CMEK compliant. |
| Cloud Run functions | Can run example scripts for selected ingestion feeds, including threat-intelligence sources such as STIX/TAXII and MISP. | This is not the documented AI triage engine. Google warns that the example scripts lack checkpoint functionality and may fail to send all logs in a stateless environment such as Cloud Run functions. |
These options are not interchangeable. A playbook can orchestrate deterministic steps and use Vertex AI output as one input; TIN is a separate built-in investigation assistant. A deployment can use either or both, but should not assume that TIN’s supported alert pathway, permissions, or compliance posture automatically applies to a custom Vertex AI playbook.
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Build a triage workflow around evidence and priority
1. Route high-risk alerts to an owned queue
Start with assigned cases and the broader case queue. Google’s response guidance recommends filtering for Critical and High priority alerts and checking that alerting is enabled for the relevant detections. This keeps urgent work visible to an analyst instead of treating arrival time as the only measure of importance.
2. Enrich the alert before asking for a verdict
Investigate the entities involved—such as assets and identities—and their risk context. Google SecOps investigation capabilities provide event enrichment and contextual information about related entities. TIN can add related SecOps searches, GTI indicator enrichment, command-line explanations, and process-tree reconstruction where supported.
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Use those results as evidence to assess, not as an automatic guarantee that an alert is benign. Missing context, an incomplete event trail, or a mistaken interpretation can change the conclusion.
3. Select the AI task and make its output actionable
Use Vertex AI integration when the playbook needs a configurable generative task, such as summarizing an entity’s context or analyzing EML content. Configure the required service account and IAM permissions, then set the integration’s API root, project, model, and location. For production workflows, follow Google’s recommendation to use generally available models rather than Preview models.
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Use TIN when the alert is eligible for its built-in investigation. Its verdict and summary can inform case routing, but preserve the evidence and workflow context around that verdict. Do not assume it can investigate alerts arriving through a Google SecOps SOAR connector.
4. Branch on results, with safeguards for risky actions
Playbooks can combine deterministic checks with AI output and route cases down different paths—for example, escalation versus additional review. Treat AI output as a workflow input, not the sole authorization for a disruptive response. Google describes manual approval steps for sensitive operations such as host isolation, file detonation, and IP blocking. Keep analyst confirmation where a false positive could disrupt a user, endpoint, or service.
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Understand investigation time and throughput limits
Google’s TIN operational guide, updated September 3, 2026, describes an average investigation time of 60 seconds and a maximum of 20 minutes. Those are Google’s product-documentation figures, not an independently audited performance guarantee.
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Google documents different throughput figures for different contexts, so do not combine them into a universal quota:
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- For Agentic Automation in playbooks, Google documents up to five automatic investigations per hour, subject to Gemini resource availability and Vertex AI capacity.
- Google’s broader response guide describes typically around ten TIN investigations per hour per tenant, with manual and automatic triggers as examples.
Confirm the current limit and feature eligibility in the target tenant before designing alert volumes or promising a response rate. Capacity and quota may vary by pathway and available resources.
Google’s response guide also says AI-powered triage can condense 15–20 minutes of manual work into a shorter timeframe. This is a Google product-documentation claim, not an independent benchmark or a guaranteed time saving for a particular environment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use Cloud Run cautiously for ingestion
Google SecOps documents Cloud Run functions as one option for running example scripts that ingest selected threat-intelligence feeds. The examples are not officially supported, and Google warns that stateless environments such as Cloud Run functions may omit logs because the scripts lack checkpoint functionality. A partial ingestion can leave the triage workflow without expected evidence, so do not treat a successful function invocation as proof that every record arrived.
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For ingestion, Google’s documentation recommends Chronicle API rather than the deprecated Ingestion API. The documented discontinuation date for the Ingestion API is July 20, 2027. Plan migration around that deadline and verify the current API guidance before changing a production feed.
Operational checks before enabling automatic triage
- Confirm whether the alert data is eligible for TIN or whether the workflow requires the configurable Vertex AI integration.
- Verify tenant eligibility, permissions, service-account access, and any compliance requirements for the selected route.
- Ensure Critical and High alerts are visible in an owned queue and that alerting is enabled for the relevant detections.
- Define which evidence and deterministic checks must be present before the playbook routes or escalates a case.
- Require analyst approval for actions whose false-positive impact could be material.
- Check the tenant’s current investigation quota and available capacity; the documented figures describe different contexts.
- If using Cloud Run ingestion examples, account for the missing checkpoint behavior and validate that the expected logs or indicators were received.
- Keep a record of the alert, evidence, AI output, branch taken, and any human approval so an analyst can review the decision trail.
What this setup does—and does not—establish
Google’s documentation describes product capabilities and operational limits, but does not establish a controlled quality comparison between Vertex AI playbooks and TIN, a latency SLA, or a pricing comparison. Nor does a documented feature guarantee that a particular model, region, tenant, or alert source is eligible. Validate those details in the target environment before committing to an automation design.
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