The Evolution of Technical Surveillance — From Cameras to Code and Artificial Intelligence (AI)

In the past, surveillance was simple to identify. Cameras were visible, installed in certain places or areas, and clearly marked where surveillance began and ended. It was mostly controlled by humans, deployed for specific objectives, and connected to actual areas. The majority of people knew when and why surveillance existed at first place.

Over the course of time, this conventional mode of surveillance started undergoing a process of transformation. Data started to travel across networks, cameras became data, and monitoring expanded into digital spaces in addition to physical ones. Today, surveillance is no longer limited to monitoring specific locations. The focus today is shifting more and more on watching behavior, patterns, and movement in everyday life.

This is my second article in the series on surveillance and privacy. This article looks at how technical surveillance changed from using visible, location-based systems to software-driven and AI-enabled platforms, and why this change transformed the nature of surveillance in a fundamental way.

To address the growing gap between the rapidly evolving technology, innovation and traditional regulatory process, the Financial Conduct Authority has developed a regulatory sandbox.
The UK’s regulatory sandbox received more than 700 application in the initial years and accepted around 40% from them. Many organizations gained approvals and started expanding globally. This engagement of collaboration was a learning also for FCA to modify their regulatory policies to cater the evolving technologies and innovation.

Technical surveillance’s initial stages were characterized by visibility and restriction. CCTVs were installed in certain areas such as streets, office buildings, banks, factories, and transportation hubs. Their purpose was simple. Cameras captured footage, and human operators observed displays or reviewed recordings following an incident.
During this time, surveillance had clearly defined boundaries. It was limited by human attention and linked to actual places. If someone was not physically present in front of a camera, they were not being watched. The number of screens a person could watch and the amount of time available for evaluation limited the scale.

Above all, surveillance seemed extraordinary rather than commonplace. Cameras were clearly visible and frequently accompanied by signage. There was clear justification for their presence in terms of safety, deterrence, or security. Although there was surveillance, it did not yet influence daily actions, behaviour or decision-making.
Human judgment was quite important. People made decisions about what seemed suspicious, when to step in, and how long to retain the data. Technology did not define surveillance; it only enabled it.

An important change in technical surveillance began with the transition from analog to digital systems. Videotapes were replaced with digital storage, making surveillance data easier to retain, copy, search, and share.

This shift fundamentally changed surveillance. Real-time observation was no longer the only form of monitoring. Information can now be stored, revisited, and analyzed long after it was initially gathered.

New opportunities were made possible by digitization. It is possible to keep surveillance records forever, integrate them with other datasets, and use them for various purposes. What was once a transient observation became permanent data.

At this point, surveillance started to resemble data management rather than merely observation. The focus shifted from single occurrences to historical data and emerging trends. Surveillance was no longer temporary. It started to accumulate.

Networking surveillance systems was the next step after they were converted to digital. Physical limitations that had previously restricted surveillance were eliminated by networks.
Remote access to cameras was now possible. It is possible to centralize databases. Cities, buildings, and organizations could all have surveillance systems. The location of the data collection was no longer relevant.
This growth brought about modest but important changes in surveillance. Monitoring was no longer limited to a particular site or operated by a single individual. Surveillance became decentralized, scalable, and continuous.
At this time, surveillance began to resemble interconnected systems rather than just tools. As information moved between institutions and departments, surveillance stopped being a single action and became part of a larger ecosystem.

As people’s daily lives went online, so did surveillance. The internet gave rise to a type of surveillance that was more concerned with conduct than physical presence.
Emails, search searches, browsing history, social activities, and communication metadata became important sources of information. Beyond buildings and streets, surveillance has extended to people’s online activities.
This was a significant shift. The use of surveillance was no longer restricted to security or law enforcement. It was transformed into a tool for efficiency, customisation, and influence.
Online surveillance also presented a distinct experience. No cameras could be seen. Monitoring was integrated into the daily services and platforms that individuals used. Data was gathered in background, frequently presented as necessary for convenience.
In addition, the visibility of surveillance decreased, which made it simpler to ignore.

The extensive proliferation of smartphones revolutionized surveillance to a greater extent than any preceding technological advancement. Mobile gadgets integrated location tracking, connection, sensors, and personal identity into a single unit that may be carried practically anywhere.

This was undoubtedly a turning point. Surveillance shifted from locations to individuals.
Smartphones enabled continuous location tracking, app-specific data collection, and sensor-based monitoring. Mobile devices, unlike fixed cameras, collected data wherever their users went.

That’s when surveillance stopped being occasional and started happening all the time. Being in a certain place was no longer a requirement for surveillance. It tracked individuals across various contexts.

It seemed that using surveillance systems was voluntary. Users installed applications, granted permissions, and shared data in exchange for services. Over time, it got really difficult to avoid this kind of surveillance, and the line between choice and necessity started to disappear.

As the number of Internet of Things devices increased, surveillance expanded into more areas. Sensors were integrated into homes, automobiles, workplaces, and public infrastructure.
Smart meters monitored energy use. Fitness devices trackes health-related data. Voice assistants listened to command from users. Movement patterns were detected using traffic sensors. Connected appliances gather usage data.
These devices did not resemble conventional surveillance techniques. They become part of everyday life.
Monitoring became pervasive. Data was gathered in a passive manner, frequently without explicit knowledge. Surveillance no longer required direct engagement or active participation.
At this point, monitoring was less visible, always present, and less reliant on human intervention.

Biometric technologies brought about yet another significant shift. Instead of only monitoring activities, surveillance systems could now identify specific people.
Facial recognition, biometric analysis, voice recognition, and gait analysis have enabled the direct correlation of behavior with individual identity. Badges, cards, and passwords are no longer necessary for identification.
This made it possible for surveillance systems to monitor people across different systems, identify them without interaction, and derive long-term behavioral patterns.
Unlike passwords, biometric characteristics cannot be easily modified. This makes biometric surveillance very effective & powerful. Monitoring was no longer limited to an individual’s actions, rater the focus shifted to their identities.

Artificial intelligence represents a significant shift in surveillance operations. Previously, systems were primarily used for information collection and storage. With artificial intelligence, surveillance extends beyond merely recording events to correlate data across various locations, contexts and period.
Machine learning models identify patterns, anomalies, and behavioral indicators that are difficult for humans to observe. Now surveillance shifts from focusing on isolated incidents to identifying recurring patterns and deviations that develop over extended period of time.
It is now possible to link several datasets, such as transactions, digital behavior, physical activity, and biometric data. Over time, individuals are represented through data profiles that illustrate their behaviors, connections, and behavioral patterns.
The focus gradually shifts from what occurred to what behavior implies over time. Surveillance now transitions into an interpretive process rather than merely observational, with infrence derived through correlation.
This transition establishes the foundation for the subsequent phase of surveillance, in which patterns are not only identified but also employed to predict future behavior.

The shift is subtle but profound. Probabilities may have a greater influence on decisions than actual actions. Surveillance is no longer responsive to reality. It expects it.

Prediction is becoming more and more important in modern surveillance.

Systems evaluate potential future events rather than what has actually occurred.

The systems has the capability to generate Credit scores and alerts, and recommend preventive actions

These days, this approach is widely used in a variety of fields, including workplace monitoring, fraud detection, border security, police, and credit evaluation.

Now, probabilities may have a greater influence on decisions than actual events. Surveillance is no longer just responding to actual events, it has the power to anticipate them beforehand.

With AI integrated across systems, surveillance is not just a standalone function. It becomes an infrastructure layer that supports decision-making across organization.

Cameras, sensors, applications, and databases feed information into centralized analytics platforms. Machine learning models process these inputs and generate outputs that helps to take informed decisions.

At this stage, surveillance is not always visible or recognised as surveillance. It often appears in the form of automation, convenience, or system efficiency. However, its core function remains unchanged. It observes people, interprets their actions, and influences how they behave.

This is the point at which surveillance becomes ubiquitous.

This evolution fundamentally changes the relationship between individuals and institutions.

Surveillance is no longer exceptional. It is continuous, integrated to our daily lives , and hence it is difficult to avoid. Those who collect and analyze data gain visibility and influence, while those being monitored often doesn’t have control over them.

Privacy is affected not only by what information is collected, but by how it is analyzed and used.

This evolution also explains why publicly available information has become so powerful. When combined with AI, even ordinary data can reveal far more than intended.

Surveillance did not become intelligent all at once. It evolved gradually through a series of technical shifts that reshaped how monitoring works.

Cameras became data.
Data became patterns.
Patterns became predictions.

Artificial intelligence is the inflection point that transformed surveillance from something that watches into something that interprets and predicts. In doing so, it expanded surveillance into everyday life.

Understanding this journey from cameras to code and AI is essential. It sets the foundation for understanding Ubiquitous Technical Surveillance, the pressures it places on privacy, and the growing role of OSINT in modern surveillance.