Over 60% of American adults interact with AI several times a week, but do they know how much AI tracks them?
Through pattern recognition arguably its biggest strength AI can analyze an internet user’s behavior and form a persistent digital profile.
This profile can then be used for personalization and marketing purposes.
And in some cases, by linking other account data, a system can cross-reference it with a real-world person, including details like their name, address, and other demographic information.
So how does this tracking work? And what can or should you do about it?
Anonymity vs. Identifiability
Anonymity isn’t simply the absence of a name, as it is often understood.
In practice, modern tracking systems don’t track who you are. What they care about is whether they can reliably recognize you again. After all, one of the main purposes of AI tracking is to be able to accurately predict what a user does next.
As long as a system can consistently link activity over time, it can build a useful profile. Names are optional if not useless on their own. Patterns are not.
Device Fingerprinting: Identity Without Login
Every device exposes small technical details when it connects to a website or app. Among many others, tracking algorithms can see:
- Version of the operating system.
- Browser type.
- Screen resolution and font.
- Time zone and language preferences.
- Supported hardware features.
Individually, these traits are common. Millions of people use the latest version of Windows 11, for example. Mixed and matched, however, they can be highly distinctive.
Thus, with this information, AI models can create device fingerprints—probabilistic identifiers that allow systems to recognize a device across sessions and even when cookies are cleared.
And from this device profile, they can create a more accurate user portrait.
Behavioral Patterns
Beyond the device itself, AI systems track how you simply behave on a website. These systems can track even seemingly inconsequential mouse clicks or even idle activities. The very tracking basics start with:
- Your viewed pages.
- How far down you scroll.
- The amount of time spent on a single page.
- Typed entries into the search bar.
Machine learning Tools has developed tremendously in recognizing these patterns over time.
Take, for example, two people reading the same article. Their behavior will likely be very different.
Tracking models then cluster users with similar behavior and use those clusters to infer their interests, intent, and likely future actions. This is how recommendation systems and targeted advertising work without needing explicit personal details.
What makes behavioral tracking particularly powerful is its ability to recognize small changes and adjust to them. People may change their habits, but AI can simply relearn these habits and update its models accordingly.
Cross-Site Tracking and Data Aggregation
Modern data tracking systems cannot function without using third-party scripts to understand your activity across various websites. These scripts are run by data brokers and some organizations to gather various details from multiple different sources, from a social network to a random ecommerce site you’ve visited twice.
This allows them to aggregate their data at scale. With such data, they gain visibility into browsing behavior across large portions of the web and run targeted advertising campaigns.
This concentration is what allows such systems to learn from patterns that span contexts rather than isolated visits.
Network and Location Signals
Network details are another factor that contributes to a digital outline.
IP address, for example, while not a precise identifier, still reveals approximate geographic location and network affiliation.
Even without names or other direct identifiers, tracking systems can observe that a particular pattern of activity tends to come from a given IP address.
For example, tracking systems can see that a user from a UK residential network, using Windows 11, often logs in at 10:30 AM and performs a recognizable pattern of activity. This becomes part of a user profile.
But what is my IP, and can I change it? Yes, and it can help reduce tracking by making it harder for AI to link activity across locations and sessions.
Why Your Online Behavior Can Reveal More About You
One might ask: Does it matter if AI systems track my browsing behavior if they’re anonymous anyway?
The answer is yes. Aside from potentially intrusive personalized advertising and recommendations, these details can also reveal much more private data.
Academic research has shown that a sufficient amount of behavioral and contextual data can accurately infer an otherwise anonymous user’s demographic and social information, such as age range, education level, and even political views.
Now, it’s important to note that tracking systems don’t “know” these things about people. They probably can’t pinpoint you from a group.
They simply assess the likelihood of a profile being you, and for prediction and targeting, probability is enough.
Are There Regulations on AI Tracking?
While data regulations like Europe’s GDPR or California’s CCPA have made companies become more transparent and given users more rights regarding their data, they can’t eliminate tracking alone.
Unfortunately, since these laws were introduced, companies have adapted and shifted to pseudonymous profiling, which, under these laws, is somewhat of a grey area.
How Can Users Control AI Tracking?
There are many tools out there that can help block cookies or limit third-party access. These help, but they can’t change how you behave as a person.
AI systems have become highly sophisticated; they can find the tiniest identifiers that will signal them to your online persona.
As a result, one needs to strike a balance between privacy and usability. A way through is to wisely choose or even reduce the tools you use, such as limiting browser extensions, using privacy-aware browsers, and restricting third-party scripts where possible.
Collectively, however, people need to vote with their wallets to create market pressure.
Only give your money (and time and clicks) to companies that are more proactive about respecting user data—those that clearly explain their data policies, offer a greater degree of control, and build privacy into their products from the start.
Other companies will then follow suit.
Conclusion
AI does not need to know who you are to track you. It only needs patterns of what you do.
The question has never been whether tracking exists. It does.
What needs to be asked and discussed as a society is: how visible, accountable, limited, and controllable should it be?
Understanding how it works is the first step to doing that.
Pre-AI tracking models did not need to know who to track; they only needed activity patterns to complete an accurate profile. AI tracking models only enhance this capability further.
The proof of your very accurate persona is shown to us every day through our devices.
