There is a quote from a game called Watch Dogs 2; the game’s plot centers around a massive corporation that leases a surveillance system out to cities. It goes like
“You are now less valuable than the data you produce.” (Watch Dogs 2)
At a glance, this quote feels like gross overdramatization, almost insulting to reference. But by using intelligent AI analysis to reference your behaviors across databases, anyone with enough resources can draw extensive conclusions about your interests, behaviors, intentions, and more. Even if you don’t use an email or phone number, there are ways to, with a good enough degree of certainty, pinpoint you down and connect you to your existing database.
These same datapoints on millions of people give actors the capabilities to predict crime, protests, election turnouts, disease spread, unionizations, birth rates, marriage rates, and more. Using the analysis tools available and cross-referencing people’s behavior trends with known behaviors associated with these events, actions and thoughts can be predicted and could even be — if the person uses social media — subtly manipulated at a scale and with a precision that would be thought impossible even 15 years ago. The increasing push for surveillance by governments only increases the severity and likelihood of the risks posited before. Despite the very real risks, we’re seeing what I would argue is the last layer of user privacy and humanity in the data processing industry being eroded in favor of supposed safety and the absolvement of corporate responsibility. As mentioned, we’re watching the government dive headfirst into AI powered surveillance with various tools that show the current uses of our data and serve as a vector for the near future if these concerns are not addressed and initiatives are not curbed soon.
A Crash Course In Analytics
What Are Analytics?
Oracle is a company that was founded in 1977. They are a multinational tech company that hosts a wide variety of products, one of them actually being the java programming language. However, if you’re a tech company, you likely know them best from their product Oracle Database, their flagship product for the aggregation of data. As put on their website,
“Analytics is the process of discovering, interpreting, and communicating significant patterns in data… Business analytics focuses on using insights derived from data to make more informed decisions that will help organizations increase sales, reduce costs, and make other business improvements.” (“What Is Analytics”, n.p.)
In other words, analytics is correlating, or drawing connections, between a person’s observed behavior and sets of known behaviors that are tied to different actions or states. Then once you draw these connections, you could personalize your products behavior to them or make informed decisions for your business to make it run better.
The Power of AI Analytics
XOrbix is a company that specializes in AI and ML (Machine learning) development for automation, predictive analysis, and natural language processing. In other words, they are one of many companies that supply the tools necessary to perform AI analytical analysis. They aren’t a tech giant like Oracle, but I feel their listed explanations for the capabilities of Machine Learning (AI analysis) and Deep Learning are more than adequate.
“Unlike traditional analytics, which often rely on manual querying and static reports, AI analytics continuously learns from data patterns and adapts to new information, enabling predictive and prescriptive capabilities that go beyond descriptive statistics.” (Meraj, n.p.)
In layman’s terms, this means that AI analytics have the power to find patterns and draw conclusions beyond the pure data and what it was explicitly asked to find. In other words, it can process large amounts of data to read between the lines. Humans have limits to what they can remember and how fast they can work; a machine is only limited by its hardware. So as a result, it can draw conclusions that would take months for people to find the necessary data to backup or even draw connections that would just simply be too vast for a person to.
With this said, these conclusions can only be drawn as reliably as the data is accurate. The pretext to AI, or machine learning analysis is Deep Learning. XOrbix continues on to explain the capabilities and uses of deep learning as,
“Deep Learning (DL): A subset of ML [AI], deep learning uses neural networks to process vast amounts of unstructured data such as images, videos, and text, enabling sophisticated analyses like image recognition and sentiment analysis.” (Meraj, n.p.)
Deep learning is the process of having machines tag data. Things like facial recognition or sentiment analysis, with that being the process of scanning natural language and facial expressions to guess what the person is feeling. This tool is equally as powerful as the analysis itself because it is what enables these predictive algorithms to even have as much data as they have for processing. Think of it this way, the best engine in the world is no good if you give it not enough or low-quality gas. Gasoline here is the tagged data that comes from deep learning models, and the engine is the machine learning (AI) algorithm that drives the car. The automated tagging of data is an incredibly powerful tool that enables every device that listens or watches to feed algorithms without user input, rather than just explicit actions such as buying or liking.
You interact with these systems every day without realizing, the way TikTok knows to bring you back for part 2 or amazon knows you want an air conditioner even though you never said a word about it is because every application collects and sells data on you. The fact that people believe their devices are listening should show you how sophisticated these processes are. A much scarier truth than you are being listened to is that these companies can infer that you might want an air conditioner, even if you never searched for it, because after weighing your user behavior for the past hour against millions of others, you behaved in a similar way to someone who wanted an air conditioner. The behavior and spending data you produce is more valuable to these companies than you as an individual for this reason. Every time you interact with any of their systems, you make all of their algorithms that bit better; as a result, the next time someone scrolls the same way you do, or like similar posts as you – even if your previous actions were seemingly unrelated to what you were considering – their models can strengthen the correlation between that string of behaviors with your final desired action.
The Government’s And Big-Tech’s Bet
Now that you have a better understanding of how data is collected, processed, and ultimately used, it should be very clear that privacy protections should be a priority. Instead, we’re seeing the opposite across the board; the groundwork is being laid for an ease of surveillance never before seen in such a way that not only connects all your digital profiles, but your real-life identity as well. Additionally, we are also seeing the various applications different agencies and companies have in mind for this extensive catalog of user data in regard to policing.
The Government’s Relationship to Data
You might be asking at this point, isn’t a warrant required to get companies to turn over data? The answer is yes, to strong arm companies into turning over user data, a warrant is required. However, the purchasing user data has no such protections and can be freely done. As TechCrunch puts it,
“Because this data is sold on the open market, the government doesn’t need to compel anyone to provide it. They can simply purchase it without any oversight or legal ramifications.” (Kovacevich, n.p.)
Essentially what this means is that any data collected by corporations can be freely acquired by the government, every piece of information Google or Facebook has on you that is being sold can be acquired by advertisers and the government with the same degree of ease.
In addition to simply being allowed to freely acquire data, there is many documented cases of the US Government weaponizing it domestically. One such example is law enforcement enlisting the help of third parties to perform facial scanning to assist in ICE arrests. As Tech Crunch goes on to explain,
“Police departments have been circumventing facial recognition bans by going to third-party vendors for their facial recognition search results. In 2018, ICE claimed to make an arrest and deportation from a “routine traffic stop,” but had also coincidentally purchased specific cellular phone tower data that could have helped make the arrest.” (Kovacevich, n.p.)
Because facial recognition is not allowed to be performed by law enforcement, they instead gave the data to a third party and bought it back to circumvent this restriction. This data ultimately lead to an arrest that was disguised as a routine traffic stop. While I acknowledge there is much discourse over the necessity and validity of ICE as an agency in the government, this case serves as a temperature check on the practices and morals that agencies within the government have and likely will hold themselves to. If ICE is allowed to purchase data to catch undocumented immigrants, there is nothing objectively stopping the government from watching you in much the same way.
Preemptive Policing
Policing citizens is no longer done through patrols and calls. We’re seeing a rapid shift in the way policing is conducted across the country. Today’s policing initiatives not only use AI to recognize people, but increasingly use it to predict where crime will be before it happens. A leak of hacked data from the Department of Homeland Security exposed such efforts as explained in an article from The Guardian,
“Its proposal describes “a high-availability data lake integrated with AI-driven analytics” that would collect and anonymise 911 call and incident data from public safety answering points nationwide, generating “geospatial heat maps” and using AI models to “predict incident trends” and “deliver actionable insights to responders.”” (Wilson, n.p.)
The government is contracting a company to produce a product to use AI analytics to generate a ‘geospatial heat map’ of crime to allow selective action to be taken. Right now this data is anonymized before being used, only being used to send out work orders on the magnitudes of cities. However, allowing 911 call data to be processed by a machine to preemptively launch police efforts is a dangerous box to open. This data could very easily, using technologies similar to the facial recognition vendor in the ICE case, be used to recognize voices and draw on known user data and use this to modify the police efforts sent or brief police officers with likely offenders in the case depending on if the call came from their residence, work, or other place. This is a form of predictive policing and prevents fair judgements from being made as police will come in with prior convictions as gathered from their relative police force size or even in the near future full AI overviews on the individual.
Where We’re Headed
New technologies are allowing for AI assisted policing at a massive scale. For the sake of brevity I won’t get into every innovation within this industry. However, these examples provided with context should give a clear point of reference as to where we’re headed. I believe if we continue on this track, there is a not so distant future where privacy is largely a thing of the past. This is a scary future as privacy is essential to maintaining personal freedoms. Protestors and activists could easily get their faces scanned and be tracked down just as ICE can track down immigrants. It is unreasonable to hand agencies extremely powerful tools with no strings attached and expect them to not abuse them. The plain truth is these tools do make policing and surveillance easier. For this reason they should be restricted to extremely important use-cases, as to make room for ordinary citizens to more easily exercise rights and maintain a level of breathing room. It is important to keep a safe society; however, safety should not be coupled with a complete loss of privacy, there are numerous other ways to maintain a safe society without watching everyone and analyzing their behavior constantly.
Sources:
Image is under Public Domain
Watch Dogs 2. Developed by Ubisoft Montreal, Ubisoft, 2016. PlayStation 4, PC Release.
Oracle. “What Is Analytics?” Oracle.com, Oracle, 16 Mar. 2021, www.oracle.com/analytics/what-is-analytics/.
Meraj, Laila. “AI Analytics and How It Harness AI in Transforming Data Analysis.” Xorbix Technologies, 14 May 2025, xorbix.com/insights/what-is-ai-analytics-harnessing-the-power-of-artificial-intelligence-for-data-analysis/. Accessed 4 May 2026.
Kovacevich, Adam. “The Government Can’t Seize Your Data — but It Can Buy It.” TechCrunch, 21 May 2023, techcrunch.com/2023/05/21/the-government-cant-seize-your-data-but-it-can-buy-it/.
Wilson, Jason. “Hacked Data Shines Light on Homeland Security’s AI Surveillance Ambitions.” The Guardian, The Guardian, 15 Mar. 2026, www.theguardian.com/us-news/2026/mar/15/hacked-data-homeland-security.
