The widely accepted notion that privacy must be traded for security arises from a short-term view of risk. Surveillance-based approaches emphasise visibility, often achieved through large-scale data collection and centralisation. While these methods may appear effective in the immediate term, they introduce systemic vulnerabilities that are frequently overlooked. Centralised data repositories become high-value targets, creating concentrated points of failure, the compromise of which can have cascading effects. The more data a system accumulates, the greater the potential damage when it is breached. In this sense, weakening privacy expands the attack surface rather than reducing it. What is often described as a trade-off is, in reality, a misalignment between short-term control and long-term resilience.
This structural misalignment is particularly visible in artificial intelligence systems, which depend heavily on large-scale data aggregation, often without clear mechanisms for consent or accountability. Beyond ethical concerns, this practice introduces technical risks. Models trained on sensitive data can inadvertently expose that data through adversarial probing, demonstrating that abstraction does not guarantee protection. Similarly, centralised digital identity infrastructures promise efficiency but do so by consolidating sensitive information into singular systems, thereby increasing systemic risk. Failures in such systems are not isolated events; they affect entire populations.
The economic dimension further complicates the narrative. As Shoshana Zuboff has argued, modern digital markets are increasingly structured around the extraction and monetisation of behavioural data. This model incentivises maximal data collection irrespective of necessity, leading to the concentration of information within a small number of entities. Such concentration not only distorts market competition but also amplifies systemic risk.
Privacy-preserving systems, by contrast, distribute both data and risk, fostering trust as a core economic asset rather than a regulatory constraint. Regulatory responses, including the General Data Protection Regulation, have established important principles such as data minimisation and purpose limitation. However, these frameworks often operate at the level of compliance rather than system architecture.
Organisations may adhere to regulatory requirements while continuing to collect extensive amounts of data. This gap underscores the need to embed privacy directly into system design. Techniques such as end-to-end encryption, federated learning, and secure computation demonstrate that systems can function effectively while minimising data exposure. These approaches reflect a broader design principle: systems should compute on data without requiring direct access to it.
Common arguments against strong privacy protections”such as the need for surveillance to ensure safety, the existence of user consent, or the assumption that innovation requires maximal data”fail to hold under scrutiny. Indiscriminate data collection often introduces more vulnerabilities than it mitigates. Consent is frequently undermined by opaque interfaces and asymmetrical power structures. Innovation, in practice, depends on relevant and high-quality data, not on indiscriminate accumulation.
The central question, therefore, is not whether privacy can coexist with advanced technological systems, but whether such systems can remain stable without it. Privacy delineates the boundary between observation and control, between participation and exploitation. When this boundary erodes, systems become not only technically vulnerable but also socially and economically unsustainable.
To describe privacy as non-negotiable is not to advance moral absolutism, but to recognise a structural necessity. Systems that rely on continuous data extraction may achieve short-term gains in efficiency or control, but they do so at the cost of long-term stability. A technologically mature society will be defined not by the extent of its surveillance capabilities, but by its ability to design systems that minimise the need for surveillance altogether. Privacy, in this sense, is not a constraint to be balanced; it is a condition that must be satisfied.
