Surveillance is no longer limited to government agencies, CCTV cameras, or intelligence operations. Today, it’s increasingly woven into the digital systems that people use every day. From AI and cryptocurrency to robotics, modern systems often rely on continuous data collection, behavioural analysis and predictive insights to function effectively.
As a result, surveillance has evolved from a security tool into an increasingly important component of the digital economy.
What does modern surveillance look like?
Modern surveillance extends far beyond simply watching people. It involves collecting, analysing and interpreting data to improve efficiency, manage risk, ensure compliance and support decision-making. Smartphones track locations, online platforms monitor behaviour, blockchain networks record transactions and AI systems identify patterns across massive datasets.
Some researchers have used terms such as "surveillance society" to describe the growing role of data collection in economic, technological and social systems.
How is AI expanding surveillance?
Artificial intelligence has expanded surveillance capabilities by enabling machines to analyse large volumes of information in real time. AI-powered systems can recognise faces, monitor communications, detect anomalies and identify patterns across vast datasets that would be difficult for humans to review manually at the same scale.
These capabilities are now being applied across a wide range of sectors, from public services and transport networks to financial markets and corporate compliance functions.
One of the clearest examples is financial trade surveillance, where AI helps firms monitor trading activity and identify patterns that may warrant further review. As markets become faster and more complex, AI-driven monitoring is increasingly used for maintaining market integrity and meeting regulatory requirements.
However, one of the most significant challenges surrounding AI surveillance is bias. Because AI systems learn from historical data, researchers and policymakers have raised questions about whether some systems can reproduce patterns present in those datasets, potentially affecting fairness and consistency in decision-making.
This has prompted debate about whether AI systems trained on historical datasets can deliver consistently objective outcomes across different contexts. As AI becomes more deeply embedded in decision-making processes, many organisations focus on transparency, explainability and fairness testing as part of AI governance frameworks.
The significance of AI lies not only in its analytical power but also in how it embeds surveillance into everyday business processes. Similar patterns are emerging across other technologies, from blockchain networks to autonomous systems. Questions about governance are starting to focus more on transparency, privacy and understanding how automated systems use historical information.
Is cryptocurrency anonymous?
Cryptocurrency is often perceived as anonymous, though many blockchain networks create permanent transparent transaction records that can be analysed. Every transaction recorded on a public blockchain becomes part of a public ledger that can help investigators and analysts trace financial flows and identify patterns of activity.
Over time, blockchain analytics has made some cryptocurrency activity more traceable. Governments, regulators and financial institutions now use tools such as wallet clustering, transaction tracing and risk scoring to monitor financial crime and enforce compliance requirements.
This highlights the ongoing balance between financial privacy and regulatory transparency, a key consideration in the evolution of digital assets.
How is robotics changing surveillance?
While blockchain makes activity traceable in digital environments, robotics extends surveillance into physical spaces, bringing data collection and analysis into the environments where people live and work.
Robots can move, adapt, interact with people and actively collect information from their surroundings. This concept, known as embodied surveillance, expands observation beyond fixed cameras and sensors.
As robotics enters workplaces, healthcare, retail environments and homes, surveillance can become increasingly integrated into everyday life. Future systems may combine AI, environmental sensing and autonomous decision-making to create continuous awareness of the spaces they operate in.
What comes next in surveillance?
Many emerging surveillance technologies are being developed with predictive capabilities, allowing organisations to analyse patterns that may help inform assessments of potential future risks or events. Predictive analytics, biometric monitoring, autonomous systems and interconnected sensor networks are being explored as tools that can support more proactive forms of monitoring.
While these technologies can improve security, fraud prevention and operational efficiency, they also raise questions about privacy, autonomy and governance.
What makes modern surveillance different is not simply its scale but its integration into everyday systems. As AI, blockchain technologies and robotics become more common, organisations and individuals need to consider how automated decisions are understood, monitored and governed.
What does this mean for finance professionals?
- Surveillance is becoming a core part of modern technologies, including AI, cryptocurrency and robotics, supporting risk management, compliance and decision-making.
- AI is expanding the capabilities of surveillance systems, while also raising important questions around bias, fairness, transparency and accountability.
- Surveillance is evolving from monitoring activity to anticipating potential risks, creating a growing need to balance innovation with responsible oversight and privacy protections.
Further learning on surveillance
For readers interested in how surveillance operates within financial markets, the Trade Surveillance learning path explores market monitoring, regulatory expectations, AI-driven detection techniques and emerging surveillance technologies.
Legal Disclaimer
Republication or redistribution of LSE Group content is prohibited without our prior written consent.
The content of this publication is for informational purposes only and has no legal effect, does not form part of any contract, does not, and does not seek to constitute advice of any nature and no reliance should be placed upon statements contained herein. Whilst reasonable efforts have been taken to ensure that the contents of this publication are accurate and reliable, LSE Group does not guarantee that this document is free from errors or omissions; therefore, you may not rely upon the content of this document under any circumstances and you should seek your own independent legal, investment, tax and other advice. Neither We nor our affiliates shall be liable for any errors, inaccuracies or delays in the publication or any other content, or for any actions taken by you in reliance thereon.
Copyright © 2026 London Stock Exchange Group. All rights reserved.
The content of this publication is provided by London Stock Exchange Group plc, its applicable group undertakings and/or its affiliates or licensors (the “LSE Group” or “We”) exclusively.
Neither We nor our affiliates guarantee the accuracy of or endorse the views or opinions given by any third party content provider, advertiser, sponsor or other user. We may link to, reference, or promote websites, applications and/or services from third parties. You agree that We are not responsible for, and do not control such non-LSE Group websites, applications or services.
The content of this publication is for informational purposes only. All information and data contained in this publication is obtained by LSE Group from sources believed by it to be accurate and reliable. Because of the possibility of human and mechanical error as well as other factors, however, such information and data are provided "as is" without warranty of any kind. You understand and agree that this publication does not, and does not seek to, constitute advice of any nature. You may not rely upon the content of this document under any circumstances and should seek your own independent legal, tax or investment advice or opinion regarding the suitability, value or profitability of any particular security, portfolio or investment strategy. Neither We nor our affiliates shall be liable for any errors, inaccuracies or delays in the publication or any other content, or for any actions taken by you in reliance thereon. You expressly agree that your use of the publication and its content is at your sole risk.
To the fullest extent permitted by applicable law, LSE Group, expressly disclaims any representation or warranties, express or implied, including, without limitation, any representations or warranties of performance, merchantability, fitness for a particular purpose, accuracy, completeness, reliability and non-infringement. LSE Group, its subsidiaries, its affiliates and their respective shareholders, directors, officers employees, agents, advertisers, content providers and licensors (collectively referred to as the “LSE Group Parties”) disclaim all responsibility for any loss, liability or damage of any kind resulting from or related to access, use or the unavailability of the publication (or any part of it); and none of the LSE Group Parties will be liable (jointly or severally) to you for any direct, indirect, consequential, special, incidental, punitive or exemplary damages, howsoever arising, even if any member of the LSE Group Parties are advised in advance of the possibility of such damages or could have foreseen any such damages arising or resulting from the use of, or inability to use, the information contained in the publication. For the avoidance of doubt, the LSE Group Parties shall have no liability for any losses, claims, demands, actions, proceedings, damages, costs or expenses arising out of, or in any way connected with, the information contained in this document.
LSE Group is the owner of various intellectual property rights ("IPR”), including but not limited to, numerous trademarks that are used to identify, advertise, and promote LSE Group products, services and activities. Nothing contained herein should be construed as granting any licence or right to use any of the trademarks or any other LSE Group IPR for any purpose whatsoever without the written permission or applicable licence terms.
