OPINION: Data privacy is the new trust test

Last week’s commemoration of International Data Privacy Day provided a timely opportunity to reflect on how deeply data now influences our lives and the responsibilities that accompany it.

From the financial and insurance services we rely on to meet our financial goals (whether education for ourselves or our loved ones, ownership of assets and property, leaving a legacy for our families and ensuring ourselves a decent income in retirement), data sits quietly at the centre of trust between institutions and the people they serve.

Few industries understand this more acutely than the life insurance industry. Insurers are custodians of some of the most personal information individuals will ever share, including medical histories, family details, financial circumstances, and long-term life plans. The decision to entrust an insurer with this data is, at its core, an act of confidence. Protecting that confidence is no longer only a regulatory requirement. It is a leadership obligation.

At the heart of data privacy is trust. Trusted data enables innovation, better customer experiences, accurate commercial decisions, more relevant product design and offering, and sustainable growth. 

But trust cannot be patched together after a breach or reputational failure. It must be deliberately designed into systems, processes, and organisational culture from the outset. Once eroded, trust is expensive and time-consuming to rebuild, and in industries such as life insurance, the cost is measured not only in financial terms but in long-term credibility.

As business models become more interconnected, consistency in data protection standards has become critical. Life insurers increasingly operate within complex ecosystems that include technology partners, digital platforms, FinTechs, and traditional sales and distribution models.

Regardless of how services are delivered, every partner handling customer data must be held to the same privacy and protection standards. Embedding data privacy into process design, product development, automation, and system integrations both within enterprise and externally is no longer optional.

Equally important is the discipline of purpose. Organisations must continually ask a simple but often overlooked question: what data are we collecting, and why? Collecting data without a clear, defined use creates unnecessary exposure and risk. In many cases, large volumes of data sit unused in systems, offering little value while increasing vulnerability.

Related to why we collect data, personal data such as salaries and medical history should be safeguarded to protect privacy. This is done through security measures such as user access limitations and multi-factor authentication protocols.

Responsibility also remains firmly with the data controller. Rights such as the right to erasure and the right to be forgotten cannot be outsourced or transferred, even in highly outsourced or partner-driven models. Accountability must remain clear, visible, and enforceable.

As conversations around artificial intelligence gather pace, the quality of data becomes even more consequential. Artificial intelligence, while often portrayed as new, has existed as a formal field of study since 1956. What has changed is the scale of data, the availability of data and the speed of processing. AI can only generate meaningful outcomes if the data it is trained on is accurate, relevant, and responsibly governed. In a sector like life insurance, this distinction matters profoundly.

Data privacy is no longer a back-office concern. It is a measure of institutional integrity and leadership maturity. For industries built on long-term promises, such as life insurance, protecting data is inseparable from protecting trust. And in an increasingly data-driven world, trust may well be the most valuable asset of all.