Introducing AI traffic analysis dashboards for AWS WAF

As AI agents, bots, and programmatic access become an increasingly significant portion of web traffic, organizations need better tools to understand, analyze, and manage this activity. Today, we’re excited to announce AI Traffic Analysis dashboards for AWS WAF protection packs—also known as web access control lists (web ACLs)—providing comprehensive visibility into AI bot and agent behavior across your applications.

The challenge: Understanding AI bot traffic

The rapid proliferation of AI bots—from search engine crawlers to research agents—has fundamentally changed the nature of web traffic. Organizations across industries are discovering that AI agents now represent 30–60% of their total traffic, driving significant infrastructure costs without always generating business value.

Traditional bot management tools weren’t designed for the nuances of AI traffic. Teams need to answer critical questions such as: Which AI organizations are accessing our content? What are they trying to accomplish? Which endpoints are most frequently targeted? How has this activity changed over time? Most importantly, how can we turn this visibility into actionable business decisions?

Introducing the AI Traffic Analysis dashboard

The new AI Traffic Analysis dashboard provides specialized visibility into AI bot and agent activity, available directly within your AWS WAF protection pack (web ACL) console. With this launch, AWS WAF Bot Control expands its detection coverage to track more than 650 unique bots and agents, offering one of the most comprehensive AI bot detection catalogs available. A detection catalog that will keep growing and be updated to align with the pace of the industry’s changes.

This dashboard goes beyond standard security metrics to deliver AI-specific insights that help you understand and manage this critical traffic segment.

Key capabilities

• Bot identification and verification: See which AI bots are accessing your applications, including bot names, owning organizations, and verification status. Quickly distinguish between legitimate AI agents from known organizations and potentially suspicious activity.
• Intent classification: Understand the purpose behind AI bot requests. The dashboard categorizes bot behavior patterns—whether crawling for search indexing, conducting research, gathering training data, or other activities—helping you align access policies with business objectives.
• Access pattern analysis: Identify your most frequently accessed URLs and endpoints by AI agents. This visibility helps you understand which content is most valuable to AI organizations and optimize your infrastructure accordingly.
• Temporal trends and historical analysis: Track AI bot activity patterns by time of day and analyze historical trends over the past 14 days. Detect anomalies, understand peak usage periods, and identify emerging patterns in AI traffic.
• Organization breakdown: View traffic volume segmented by bot owner organization, giving you clear visibility into which AI companies are accessing your content and at what scale.

How it works

AI Traffic Analysis dashboards integrate seamlessly with AWS WAF Bot Control for common bots using the same traffic evaluation engine while providing specialized analytics for AI-specific patterns. The dashboards display near real-time summaries based on Amazon CloudWatch metrics collected as AWS WAF evaluates your web traffic.

To access the AI Traffic Analysis dashboard:

1. Navigate to your protection pack (web ACL) in the AWS Management Console for AWS WAF.
2. Select the AI Traffic Analysis tab.
3. Apply filters for bot organization, intent type, or verification status as needed.
4. Analyze the comprehensive visualizations across bot identity, intent classification, access patterns, and temporal trends.

The dashboard populates automatically once your protection pack begins receiving AI bot traffic, so you have visibility exactly when you need it.

From visibility to action

This new capability addresses a critical need as organizations navigate the evolving landscape of AI-driven web traffic. With detailed insights into AI bot behavior, you can:

• Make informed access decisions: Understand bot intent before implementing allow or block rules.
• Optimize infrastructure investment: Identify high-traffic endpoints and plan capacity accordingly. Know whether your infrastructure costs are supporting business value or used without programmatic compensation mechanism.
• Implement tiered access strategies: Serve different content or pricing based on AI agent verification and intent.
• Detect anomalies and emerging patterns: Spot unusual patterns that might indicate emerging threats or opportunities. Real-time visibility helps you respond quickly to changes in AI bot behavior.
• Support cross-organizational strategy: Provide data to stakeholders across security, product, and business teams for informed decisions about AI bot access policies and monetization opportunities.
• Customize as needed: AI Traffic analyses are emitted as CloudWatch metrics that an organization can use to customize CloudWatch or another supported observability product as needed. Moreover, by using CloudWatch metrics, an organization can build proactive measures such as alerts or business actions such as rate or limit changes.
• Monetize AI traffic at the edge: For a reference architecture that combines WAF Bot Control AI visibility, traffic control, and content monetization using the x402 payment protocol, see the sample-x402-content-monetization-with-cloudfront-and-waf project on GitHub. It demonstrates how to classify AI bot traffic, enforce per-path pricing policies, and settle payments at the edge using Amazon CloudFront and Lambda@Edge – with zero changes to your existing origins.
  

  Note: This AWS Samples solution is not a supported product in their own right, but educational examples to help our customers use our products for their applications. As our customer, any applications you integrate this example into should be thoroughly tested, secured, and optimized according to your business’s security standards & policies before deploying to production or handling production workloads. Deploying it will provision resources that incur additional AWS charges, so review costs before deploying and delete the stack when no longer needed.

Programmatic access: Automate your AI traffic insights

In addition to the console dashboard, you can programmatically query AI bot traffic data using the GetTopPathStatisticsByTraffic action, available through the AWS WAF API, AWS SDKs, and AWS CLI. This action returns the top URI paths by bot traffic volume for a given web ACL and time window. Each path in the response includes request counts, traffic percentages, and the top bots accessing it. You can filter results by bot category (for example, ai), organization, or specific bot name, and use a URI path prefix (for example, /api/) to drill down into specific areas of your application. The following AWS CLI example shows how to query the top paths accessed by AI bots for a specific web ACL.

The following AWS CLI example shows how to query the top paths accessed by AI bots for a specific web ACL:

A sample response:

Programmatic access enables you to:

• Build custom dashboards or integrate AI traffic data into existing observability platforms.
• Automate alerting when specific paths see unusual bot traffic spikes.
• Feed traffic data into business intelligence pipelines for content monetization decisions.
• Investigate and debug AI bot activity within a specific timeframe to identify the root cause of traffic anomalies or incidents.

For detailed usage information, see the GetTopPathStatisticsByTraffic API reference and the AWS CLI command reference. This API pairs naturally with the CloudWatch metrics approach described above, giving you both real-time metric streams and on-demand path-level analytics for comprehensive AI traffic management.

Availability

For customers on flat-rate pricing plans, the AI Traffic Analysis dashboard is included with all paid plans. Read more about CloudFront flat-rate pricing in the launch blog post. For AWS WAF customers not subscribed to flat-rate plans, the AI traffic analysis dashboard is available at no additional cost. See AWS WAF pricing for details.

Get started today

The AI Traffic Analysis dashboard represents a significant step forward in managing the intersection of AI and web security. As AI agents continue to grow as a percentage of overall web traffic, having the right visibility tools becomes essential for both security and business success.

To learn more about AWS WAF Bot Control and AI Traffic Analysis dashboards, visit the AWS WAF Developer Guide or explore the feature directly in your AWS WAF console.

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