It just seemed yesterday that we were explaining the advantages of deploying APIs in our applications so we could allow for machine-to-machine connections. And by 2018 over 70% of the internet traffic was API based. On the recent years a new player has entered the game, and this is the generative AI bringing to the table the AI crawler management topic.
AI has changed the game in many ways but nowadays most of the LLM models acknowledge that their information is not up to date and to close the gap they use retrieval processes that explore the internet retrieve the latest information. As of April 2026, we have seen that in certain industries most of the traffic is generated by these automated retrieval bots.[1]
What this means for most of the people that host websites is that your main visitor is not a human anymore but rather a mix of AI crawlers (ChatGPT, Anthropic, Meta, Apple, etc.), traditional search bots, Observability and automation agents, and even malicious bots and scrapers
When Crawlers Stop Behaving Like Crawlers
These new visitors generate a set of challenges that even if not new, as most of them have been something that we started seeing with bots and crawlers have been augmented with this new technology. AI driven crawlers can execute requests at large speeds that can exhaust your server resources. Moreover, when it came to search engine crawlers, they would retrieve and index the information periodically. But due to the nature of AI searches, we often see these entities retrieving data from your site at every request increasing not only the volume but also the frequency.
Unfortunately, most of this aggressive behavior is not intentional but a consequence of technology design. These AI crawlers can overwhelm infrastructure resources, degrade performance to legitimate users, and increase operational costs without delivering business value.[2]
AI Crawlers and the Hidden Cost of Visibility
These crawlers do not only have an impact on your resources from a technical perspective but also about intellectual property protection and competitive exposure. We should be aware that crawlers are extracting proprietary content, product data, pricing and competitive intelligence. Some crawlers ingest proprietary data to even train commercial AI models. [2]
Being used to regular search index bots you might consider adding certain restrictions to the robots.txt file but when it comes to AI crawlers, much like some malicious bots do not always respect these requests. One of the reasons for this behavior is that we have several types of AI crawlers. Unlike traditional search engines, some AI systems retrieve content dynamically rather than through structured crawling, which means they may not consistently check or respect robots.txt directives. Besides this there is the fact that sometimes we have actually malicious bots or crawlers that would choose to ignore those instructions anyway.
- AI Training Bot: Crawlers that collect data for training large language models (LLMs) and other AI systems.
- AI Indexing Bot: Bots that index web content for AI-powered search or retrieval systems.
- AI Retrieval Bot: Bots that fetch or retrieve information in response to AI-driven queries.
- AI Search Crawler: Crawlers that scan and index sites for AI-based search engines.
One of the default behaviors that most organizations, specially small and medium companies, adopted was the block of these crawlers to safeguard their resources. Unfortunately, these bots have started to use some evasion techniques, blurring the line between crawler and attacker. Observed techniques include the use of rotating IP addresses, residential proxies, and human-like browsing behavior to bypass traditional WAF protections
This identifies one of the core problems that we can see when it comes to AI crawler management the challenge is the lack of a standardized, verifiable identity framework for distinguishing legitimate AI agents from malicious or spoofed traffic.
A Layered Approach to Identifying AI Crawlers
As we do not have a standard way to identify crawlers’ organizations that want to stay ahead must adopt a multi-layered identification strategy. This strategy will need to be composed of a mix of declarative signals, network validation, behavioral analysis and fingerprinting and when available cryptographic identity.
Why is this layering needed?
When it comes to the easiest way to identify an AI, this is used with declarative signals like User-Agent strings or identifying its robots.txt compliance these are easy to implement but also easy to spoof. That is why we can complement those checks with network and infrastructure validation. Validating things like: IP reputation and ASN (the organization that owns an IP range) ownership or reverse DNS (verifying a domain name map back to an IP address) verification will improve confidence but are still tied to infrastructure assumptions.
A next level up from that would be to have some basic behavioral and fingerprinting techniques this will be able to give you the probability of a connection to be a bot analyzing its behavioral analysis, using request pattern analysis or using device fingerprinting. These approaches analyze hundreds of signals to distinguish humans, good bots, and bad bots. This detection layer is quite complex and often will require small and medium-sized business to use a third-party provider.
The best method that could also be one of the simplest as it follows a simple HTTP message signature pattern as outlined in RFC 9421. This is the emerging “gold standard” and it has been adopted by OpenAI and outlined in RFC. This newer approach introduces allows us to verify the identity generating non-spoofable requests. While still not widely adopted, this represents in my opinion the future direction of crawler identification.
Sources:
[2] https://www.radware.com/blog/application-protection/why-ai-crawler-management-matters/
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