SECOPS

Correlate WAF Blocks with Datadog App Errors to Find Noise

Cross-references Cloudflare WAF blocks against Datadog application logs to separate real attacks from blocked-but-benign requests, surfacing rules that are hurting healthy users.

CategorySecOps
Enginesim
Difficultyadvanced
Triggerschedule
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerSchedule triggers the correlation run
  • ActionFetch Cloudflare WAF block eventsCloudflareCloudflare
  • ActionQuery Datadog for matching traces and session logsDatadogDatadog
  • LogicClassify each block as benign or hostile from joined evidence
  • OutputPost ranked noisy rules to Slack with correlation detailSlack

What it does

Pulls recent Cloudflare WAF blocks and joins them against Datadog APM and log data for the same requests. Blocks tied to authenticated sessions, successful prior calls, or normal user journeys are flagged as probable false positives; blocks matching error storms or scanner patterns are confirmed as legitimate. The output is a ranked list of rules generating benign noise.

When to use it

Use it when you can't tell from Cloudflare alone whether a blocked request was a real customer or an attacker. Datadog's view of what the app actually did gives the missing context to tune confidently.

How it works

  1. 1A schedule triggers the correlation run periodically.
  2. 2The workflow fetches Cloudflare WAF block events for the window.
  3. 3For each block it queries Datadog for matching traces, sessions, and surrounding log activity.
  4. 4Logic classifies each block as benign or hostile from the joined evidence.
  5. 5Rules driving benign blocks are ranked and posted to Slack with correlation details for the team to action.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect CloudflareWorkers, Pages, R2, KV — the edge stack.
  2. 2
    Connect DatadogMetrics, traces, log search.
  3. 3
    Connect SlackChannels, DMs, threads, mentions.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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