AI is reshaping the internet in real-time. Every day, internet users are getting information faster than ever as they incorporate AI-powered chatbots and search summaries into their daily lives. As large-language model (LLM) based applications like ChatGPT and Claude continue to advance, these apps, which require online content to pull from, strain networks with AI scrapers and introduce performance, security, and cost challenges for companies. 

Today’s bots have gotten very good at blending in — and that’s not an accident. For years, bots have been part of how the internet operates, whether it’s helping users find content or quietly powering workflows in the background. What’s changed is the scale and the stakes, with recent research revealing that AI bots account for nearly half of web traffic, 99% of which sit in an unverifiable ‘gray area.’

Bots can pretend to be real users, posing risks ranging from fraud to account takeovers. These bots carry out nefarious activities under the guise of legitimate web traffic, which can impact business decisions due to false bot traffic insights. 

The Bot Impersonation Problem

Unwanted bots are routinely disguising themselves as trusted and legitimate services to slip past policy controls that were designed with good bots in mind. Organizations believe they’re letting in known, good bots when they’re actually granting access to bots that are impersonating well-known and verifiable bots.

The consequences of bot impersonation are surfacing across every industry, impacting publishing, e-commerce, and beyond. The publishing industry is navigating new infrastructure and operational challenges as bots shift their operating model. Publishers create content through a business model that is reliant on revenue generated through clicks. As AI bots consume and scrape publishers’ content, the original content can be served directly through an LLM, which means publishers lose out on the desired traffic and revenue from users. In e-commerce, bots can simulate human behavior, from cart activity to product page visits. Automated traffic distorts genuine customer journeys and conversion metrics, making it harder for businesses to accurately measure demand and optimize spend. Across industries, AI-driven bot traffic is fundamentally shifting how companies operate and make business decisions.

Ghost Traffic Has Real Costs

Without understanding the intent behind bot requests, organizations absorb the cost and risks associated with unwanted bot scrapers. AI bot scrapers can lead to massive, unwanted, and unplanned spikes in traffic. This can degrade performance and experiences for legitimate users and result in bandwidth overage charges for organizations across industries, as their infrastructure is typically built to manage human traffic spikes. 

The costs and risks associated with AI content scraping can quickly skyrocket if left unchecked. It is difficult to pinpoint and mitigate specific AI scraper activity, even with traditional bot detection strategies. Security and IT teams need to be able to detect and identify the presence of bots before they either block them or launch more sophisticated countermeasures to intercept or enforce monetization. This unwanted bot traffic does not provide real value to the business, and instead drains it with little visibility into what organizations are actually paying for. 

Assess Bots by Behavior, Not Identity

Understanding bot behavior is no longer just about defense, and requires more than a broad assessment that asks “is it a bot?”. It’s about making and demanding strategic business decisions that protect your networks, while still allowing your business to be agile. As bots increasingly use machine learning to mimic human behavioral patterns to bypass traditional defenses, organizations need to consider how bots are interacting with their website or application. 

This is where bot behavioral analysis comes in. This method includes analyzing user behavior patterns to identify and distinguish between human users and bots. Bots are more likely to exhibit unusual or suspicious activities, including uniform browsing patterns, rapid page requests, or unusual click patterns. 

Analyzing this type of behavior empowers organizations to make decisions grounded in what is actually happening at the tactical level by examining access patterns and request cadence. This approach does not assume all unverified bots are hostile, but instead seeks to understand a bot’s intent to better inform decisions. With this context, organizations are able to protect content, control costs, and maintain data integrity with confidence. 

Adapting in the Era of AI

Bots aren’t a niche security problem anymore. They’re embedded in how your content gets consumed, how your infrastructure gets taxed, and how your brand gets represented online. The organizations that recognize this and build a real strategy around it will be in a fundamentally better position than those still treating bot traffic as a background concern.