In my previous blog, we explored what AI crawlers are, how it differs from traditional search engine crawlers, the different types of AI crawlers, and the impact on businesses. We established that these crawlers represent a new category of web traffic with distinct behaviors and business implications. In this blog, we will address the critical challenge that organizations now face: managing these AI crawlers strategically without risking infrastructure and performance concerns, competitive advantage, or compliance issues. The answer requires moving beyond the binary ‘block or allow’ framework to a more nuanced approach to AI crawler management.
The Cost of Blanket Blocking
When faced with aggressive AI crawler traffic that consumes bandwidth and extracts proprietary content for commercial use, a ‘block all’ approach may seem straightforward, but in the age of AI-mediated discovery, this extreme approach comes with hidden costs.
Organizations that enforce a ‘block all’ strategy on AI crawlers to safeguard their content simultaneously remove themselves from the AI-generated summaries and contextual answers that millions of users receive daily through major AI platforms such as ChatGPT, Claude, and Perplexity, among others. With AI-powered search already being the most preferred source of information for 44% of users1, being absent from these results means being absent from nearly half of all discovery moments, and subsequently, the AI economy itself.
The strategic consideration is whether the protective benefits of blocking outweigh the opportunity cost of reduced visibility on AI platforms that are reshaping how users find information and make decisions.
The Risk of Unlimited Access
The opposite extreme - allowing all AI crawlers unchecked access - presents equally serious problems. Different types of AI crawlers have vastly different purposes and value propositions.
A retrieval crawler that cites the source content and drives attribution delivers clear value. A training crawler that ingests proprietary data without authorization to build a commercial LLM model extracts value. From a strategic perspective, treating all AI crawlers identically is not the right approach.
Moreover, unrestricted access can lead to a substantial operational impact. AI crawlers can be aggressive in nature - repeatedly and rapidly executing requests, unlike human visitors. This activity can overwhelm infrastructure resources, degrade performance for legitimate users, and increase operational costs without generating corresponding business value.
The Need for Granular Control
What organizations actually need is the ability to make nuanced, informed decisions on AI crawler activity. This could mean allowing certain types of AI crawlers while restricting others, or allowing access to AI crawlers from certain preferred platforms while blocking or rate-limiting others. Effective AI crawler management enables contextual decision-making while ensuring alignment with business objectives.
This begins with visibility. You can’t manage what you can’t see, and most organizations lack clear insight into which AI crawlers are accessing their properties, for what purposes, and how aggressively they’re crawling. AI crawlers don’t always identify themselves clearly, while new AI services emerge constantly, each with different behaviors and data practices.
It also requires granular control. Organizations need the capability to set crawler-specific controls that allows differentiation between AI crawling activity or platforms that provide value, and unknown or aggressive crawling activity that may pose risks to infrastructure performance and compliance requirements.
Introducing AI Crawler Management with Radware Bot Manager
The AI crawler management capability as part of the Radware Bot Manager was built to address this challenge - providing the deep visibility and granular control required to manage AI crawler activity across applications in alignment with business objectives.
Organizations gain real-time visibility into which AI crawlers are accessing their applications, with intent-based classification, request volumes, and access patterns. This is provided through a centralized dashboard, with interactive visualizations that transform AI crawling activity into actionable analytics.
More importantly, the solution enables flexible, granular control options. Organizations can apply precise controls at the individual crawler level to enforce access controls based on strategic decisions. AI crawler activity can be managed by either allowing, blocking, or rate-limiting request volumes to balance access with infrastructure limitations.
The result is a strategic approach to AI crawler management that enables organizations to participate in the AI economy on their own terms - protecting proprietary content from unauthorized AI training use, controlling operational costs by preventing resource-intensive crawler activity, maintaining regulatory compliance, and ensuring flexibility as the AI ecosystem and business priorities evolve.
Ready to take control of AI crawling activity and position your business for the AI-driven digital era? Contact us to learn more and connect with our security experts.
1https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/new-front-door-to-the-internet-winning-in-the-age-of-ai-search