Monitor automated traffic against a normal baseline, then investigate meaningful changes by time, endpoint, WAF rule or label, source, and response outcome. A bot is not automatically malicious: crawlers, accessibility tools, health checks, uptime monitors, and partner integrations may all be expected. No single score, user-agent, source, or request-rate threshold proves intent.
What to monitor
Use application, CDN, and web application firewall (WAF) logs or dashboards to establish typical request rates, paths, response outcomes, and known automated sources. When those records are spread across multiple systems, correlate them centrally; AWS recommends centralized logging for environments with multiple sources.
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Compare changes over time rather than relying on raw counts alone. Useful dimensions include:
- Request rates and share: measure requests over time and in relation to all site requests.
- Paths and endpoints: track which pages and APIs receive automated traffic, especially sensitive operations.
- WAF rules and labels: watch the rules or classifications matching traffic and how their volume changes.
- Sources and request characteristics: review source distribution, user-agent patterns, and geographic concentration where available.
- Outcomes: compare responses and application results, such as login failures, rather than viewing request volume in isolation.
A rise in a rule match or label can reflect targeted activity or a false positive. AWS recommends tracking top-hit rules and labels, then investigating changes in context: AWS WAF Bot Control dashboard guidance.
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Where unusual automation deserves closer review
Concentrate monitoring on paths where automated misuse could affect accounts, transactions, or service reliability. Login, account creation, checkout, and APIs often merit narrower monitoring than low-impact public pages. Review activity in application context: a burst of login requests matters more when paired with repeated failures or other signs of account abuse.
Set endpoint-specific thresholds rather than assuming one site-wide rate is suitable for every path. AWS documents rate-based rules for sensitive URIs such as login and account creation. Cloudflare provides path-specific examples and recommends reviewing security events to see whether a threshold would affect legitimate users. These are configuration approaches, not universal threshold values.
How to investigate a suspicious change
- Compare with the baseline. Check when the change began, how large it is relative to normal traffic, and whether it is limited to a path, rule, label, source, or response outcome.
- Check what is being targeted. Look for concentrated activity on login, account creation, checkout, or API routes, and compare request patterns with application outcomes such as failed logins.
- Corroborate signals. Review more than one dimension—for example, rate changes alongside path concentration, WAF labels, source patterns, and response results. A seemingly ordinary request at one layer does not establish that a sequence is harmless.
- Identify expected automation. Check whether the traffic matches known search crawlers, accessibility tools, internal monitors, health checks, or partner integrations before treating it as hostile.
- Choose a proportionate response. If evidence supports intervention, begin with a scoped action and observe its effect rather than applying a broad site-wide block.
OWASP describes anti-automation defenses across edge, application, and backend or business layers. Rate limits and static checks can help with simple or high-volume patterns; behavioral and client signals can add context. The purpose is not to block every bot, but to raise the cost of abusive automation while keeping legitimate users and bots working. See the OWASP Bot Management and Anti-Automation Cheat Sheet.
Keep legitimate bots and monitoring tools working
Make an inventory of known automation before enforcing rules. Include public crawlers you intend to allow, internal uptime monitors and health checks, accessibility tools, and partner integrations. AWS notes that health checks and uptime monitors may be identified as bots; Cloudflare advises allowing verified bots and expected internal tools.
Review events for false positives and create appropriate exceptions for known sources or traffic. A user-agent string or apparent identity alone should not settle whether activity is safe; assess it alongside the source, destination path, observed behavior, and your knowledge of the integration.
Roll out mitigations gradually
Where the service supports it, start with observation or count mode so you can see what a rule would match without immediately blocking requests. Inspect affected events, tune the rule, and add exceptions for expected traffic. Then use an action proportionate to the risk—for example, a challenge or step-up verification—before considering a block. Continue reviewing events and false positives after enforcement begins.
AWS specifically recommends count mode before switching a WAF rule to blocking. It also describes forwarding suspicious labels to an application for additional verification. Cloudflare recommends inspecting security events and tuning rules to catch false positives.
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Start with the application logs and CDN or WAF analytics already in use. The right fit depends on whether your existing stack exposes path-level activity, request signals, logs for correlation, endpoint-specific controls, and a way to observe rules before enforcement.
| Option | Documented monitoring capabilities | Availability and qualification |
|---|---|---|
| AWS WAF and CloudWatch | AWS documents traffic overviews, detailed WAF logs, labels, CloudWatch metrics, and anomaly detection. Its guidance recommends watching rule and label patterns and investigating anomalies in context. | See AWS WAF dashboard documentation for the described capabilities. |
| Cloudflare bot analytics and security controls | Cloudflare documents bot analytics, targeted-path views, security events, rate limits, and rule review. | Cloudflare’s guide says full bot analytics require Business or above, while basic security metrics are available to Free and Pro users. Availability and functionality depend on plan and can change; consult Cloudflare Bot Analytics documentation. |
These are examples of documented features, not independent performance comparisons. When evaluating a tool, check whether it integrates with your existing CDN, WAF, and application stack; supports log export and correlation; lets you scope thresholds to endpoints; recognizes verified bots and monitoring tools; and offers observation or count mode for gradual rollout.
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