In practice, the decision to allow or prohibit AI scanning of a website is often tied not only to security or content control, but also to how the interests of the website owner and users are interpreted. This issue will become particularly prominent in 2024–2026 due to the rise of generative models trained on web data and increased attention to content and personal data rights. Technically, blocking is usually implemented through robots.txt, meta tags (such as noindex), or server-level headers/access. However, robots.txt itself is a guideline for legitimate bots, not an ironclad shield. Therefore, the key question is: what level of control do you need and what obligations do you wish to comply with? What practice shows: where the request actually works If the ban is properly implemented (correct user-agent rules, no URL access holes, correct HTTP responses), most legitimate systems and search tools will restrict crawling. For large ecosystems, this acts as a signal of the webmaster's preferences. But for AI models that may have previously obtained data, it's not just the current ban that matters, but also the moment the information became available on the open web, as well as the learning sources. Here, it's important for website owners to understand the difference between "do not collect further" and "remove what has already been used." Legal and reputational risks: a ban does not always resolve issues Even if you want to prevent unwanted use of content, blocking scanning isn't a one-size-fits-all solution. The risks depend on the specific content being affected (unique text, images, videos, user data) and how its status is interpreted—whether it's copyrighted, subject to contractual restrictions, trade secrets, or personal data. A separate issue is reputation: overly aggressive restrictions can impair page indexing, reduce search visibility, and lead to users finding your resource less easily. For brands, this sometimes means not only lost traffic but also a decline in trust. A pragmatic approach: targeted bans instead of a total lockdown Many teams opt for a more manageable strategy rather than a complete ban: limiting data collection to where it's truly needed and leaving access to pages that need to be indexed. This often means separating areas—for example, pages with publications, showcases, documentation, and news can be accessible, while personal accounts, forms, internal databases, and service pages are closed. Check your content inventory: where texts/media are protected by copyright and where personal data may be. Set up robots.txt carefully: correct rules for the required user agents, testing on real tools. Use targeted measures: noindex for pages that should not appear in search results, and server-side access restrictions for private sections. Consider transparency: data use policies and clear rules reduce conflict. If the goal is to protect unique content from copying, it's sometimes more effective to combine circumvention restrictions with watermarks, republishing requirements, and monitoring for stolen materials. If the goal is to reduce the processing of personal data, then it's more important to control what is shared and how it is stored/processed. Conclusion: “correct” means proportionate to the task Blocking AI from crawling a site "in general" or "for everyone" is usually not the best strategy. A more appropriate approach is usually considered to be proportionate restrictions: blocking what really needs to be unavailable while preserving what is needed for indexing and search. This gives the site owner control without undue impact on visibility and user experience. If you describe what kind of site you have (news, blog, e-commerce, portfolio), what exactly you want to protect (content, user data, commercial materials), and what models/bots you are concerned about, we can offer a more precise outline of technical measures and priorities.