SECOPS
Cloudflare Audit Anomaly Detector with Axiom Baseline
Hourly, ships Cloudflare audit logs to Axiom, queries the 30-day baseline of normal activity per actor.
How it runs
The automated pipeline, trigger to output.
- TriggerHourly, pull last hour of Cloudflare audit entriesCloudflare
- ActionIngest entries into Axiom datasetAxiom
- ActionQuery 30-day per-actor baseline in AxiomAxiom
- LogicFlag actors deviating from baseline
- ActionOpen Linear ticket per flagged actorLinear
- OutputReturn flagged-actor summary
What it does
Goes beyond simple time-of-day rules: it builds a behavioral baseline. Each hour it forwards Cloudflare audit entries into Axiom, then runs an APL query to compare the current hour's activity per actor against their trailing 30-day pattern. An actor who suddenly makes 10x their usual changes, or touches a resource type they've never edited before, is flagged as anomalous and routed to secops.
When to use it
Use this when fixed business-hours rules generate too much noise or miss subtle abuse, and you want statistical anomaly detection grounded in each operator's actual history.
How it works
- 1An hourly schedule pulls the last hour of Cloudflare audit entries.
- 2An action ingests those entries into an Axiom dataset for durable, queryable storage.
- 3An action runs an APL query computing each actor's hourly change count and resource-type set versus their 30-day baseline.
- 4A logic step flags actors whose volume exceeds the baseline threshold or who touched a never-before-seen resource type.
- 5For each flagged actor the workflow opens a Linear ticket summarizing the deviation and linking the Axiom query.
- 6The flagged-actor summary is returned as output.
Set it up
What you configure once, before turning it on.
- 1Connect CloudflareWorkers, Pages, R2, KV — the edge stack.
- 2Connect AxiomLog streams, queries, dashboards.
- 3Connect LinearIssues, projects, cycles, triage.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run this workflow in your colony.
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