# DataDome report finds malicious automated traffic growing 9x faster than human traffic and targeting deeper customer journey stages

> DataDome's State of Bot & Agent Security Report 2026 reveals that malicious automated traffic grew 9x in 12 months and is increasingly targeting deeper parts of the customer journey.

- Canonical: https://extractfeed.io/story/datadome-report-finds-malicious-automated-traffic-growing-9x-8ab486a/
- JSON: https://extractfeed.io/api/v1/stories/datadome-report-finds-malicious-automated-traffic-growing-9x-8ab486a.json
- Beat: Anti-bot & Blocking · Evidence: Primary source · Type/significance: analysis/3 · First seen: 2026-09-22T23:45:17.273043+00:00 · Updated: 2026-09-22T23:45:17.273043+00:00 · Edition: 2026-11-05
- Framing: model-written (headline, standfirst, why it matters, tags); source facts deterministic

## Briefing
- DataDome: DataDome's State of Bot & Agent Security Report 2026 reveals that malicious automated traffic grew 9x in 12 months and is increasingly targeting deeper parts of the customer journey.

## Why it matters
The 9x growth rate signals that bot operators are scaling attacks far faster than legitimate traffic grows, putting pressure on defenses that rely on volume-based heuristics. Targeting deeper into the customer journey means bots are no longer just scraping front-end content but are probing checkout flows, account management, and post-login endpoints, which raises the stakes for fraud detection and requires more granular monitoring across the full application stack.

## Sources
- Primary source · DataDome · 2026-09-22 — [DataDome Report: Malicious Automated Traffic Is Growing 9x Faster Than Human Traffic — and Targeting Deeper Into the Customer Journey](https://datadome.co/press/state-of-bot-and-agent-security-report-2026/)

## Watch next
Will other anti-bot vendors report similar acceleration in bot traffic targeting post-login and checkout flows in their own 2026 data?

Topics: DataDome, bot-traffic, malicious-automation, customer-journey, security-report, fraud-detection

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