Canonical: https://uhl.cloud/en/resources/ai-assisted-incident-response/
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# AI-Assisted Incident Response

First-response patterns: log triage, runbook augmentation and hypothesis generation - plus the points where a human must decide.

RunbookUpdated 2026-10-27

[Download the PDF](https://uhl.cloud/assets/resources/ai-assisted-incident-response-en.pdf?v=e74490dece77)

## The first fifteen minutes

When an alert fires, the scarce resource is not intelligence - it is organised attention. AI earns its place in incident response by compressing the search space: it reads the noisy logs, correlates the timeline and drafts candidate explanations while the responder keeps command of what is actually done.

- **Contain first, analyse second**
  Follow your standard containment runbook before any AI-assisted analysis - isolate, preserve, notify. Model time is cheap; the window where containment still works is not.

- **Triage logs through the model**
  Feed the alert window's logs - on self-hosted inference if they contain sensitive context - and ask for a structured summary: what changed, what is anomalous, what is likely noise. Verify every claim against the raw lines it cites.

- **Generate hypotheses, ranked**
  Ask for candidate root causes ordered by evidence in your data, not by generic likelihood. The model's job is to enumerate; your job is to falsify.

- **Augment the runbook**
  If the incident matches a known pattern, have the model pull the relevant runbook steps and fill in observed values. The responder executes - the model does not touch production.

- **Draft the timeline as you go**
  Let the model maintain the incident timeline from your verified actions. Post-incident review then starts from a written record, not from memory.

## Where the human decides - always

- Declaring an incident - severity and scope are accountability decisions, not text completion

- Any action on production systems - restarts, isolation, rollbacks run through human hands with change records

- Customer or authority communication - wording under legal and contractual weight is written by people

- Eradication and recovery steps - a model suggests order, a responder confirms each precondition before executing

- Closing the incident - the final call that systems are trustworthy again stays with a named person

We run our security operations on Wazuh with this split: detection and first drafts are automated, every consequential action is a human decision with a log.

## After the incident

The model's timeline and your verified actions become the post-incident review input. Ask the model to draft the report structure - facts, impact, contributing factors, preventive measures - then rewrite it until it is true. The pattern that pays off over time: feed confirmed findings back into detection rules and runbooks, so the next response starts further ahead than this one did.

## Chat with UHL

Start a guided chat. The assistant asks a few questions and saves your enquiry for our team to review.

## Continue exploring

- [Wazuh security operations](https://uhl.cloud/en/wazuh/)

- [Kubernetes & Talos](https://uhl.cloud/en/kubernetes-talos/)

- [Security & responsible AI](https://uhl.cloud/en/security/)

## Let’s discuss your project.

[Talk to UHL](https://uhl.cloud/en/contact/)