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AI security investigation for cloud incidents

Turn suspicious activity into an evidence-backed incident narrative with likely impact, affected assets, and recommended response steps.

What Flarehawk gives this workflow

Each use case starts from Cloudflare telemetry and ends with evidence your team can use.

Incident narratives

Vigil explains what happened, which signals matter, and why the incident deserves attention.

Evidence clustering

Related requests, detections, IP behavior, assets, and timing are grouped into one investigation view.

Response-ready output

Each investigation can include practical actions a security or engineering lead can review.

How this workflow works in practice

Flarehawk keeps autonomous investigation concrete by connecting AI output to security evidence and reviewable action.

From alert to story

Instead of opening with a raw alert, Flarehawk starts by reconstructing the sequence of suspicious activity.

Evidence remains inspectable

AI output stays tied to Cloudflare evidence such as paths, IPs, Ray IDs, rule context, and historical baselines.

Built for accountable review

Recommendations are written for humans to approve, reject, or adapt before changes are made.

From setup to investigation

The same core loop supports analytics, autonomous investigation, and retention-driven evidence review.

1

Detection logic flags a suspicious behavior pattern.

2

Vigil gathers related telemetry and asset context.

3

The investigation explains the timeline, likely intent, and affected surface.

4

Flarehawk proposes response steps with supporting evidence.

5

Your team reviews the plan and tracks recurrence.

Use-case questions

Does Flarehawk replace a human investigator?

No. Flarehawk speeds up investigation by collecting context and writing the first evidence-backed narrative for review.

What evidence does Vigil use?

Vigil uses detections, Cloudflare event details, asset context, baselines, and related activity stored in Flarehawk.