In today’s digital-first economy, data is the lifeblood of growth. Companies create, gather, and analyze vast amounts of information daily, harnessing insights that fuel innovation and drive competitive advantage. Yet, as data volumes soar, so does the risk of exposure. This insatiable need to consume data has created a dangerous imbalance: While companies rush to harness the value of their data, they often overlook the vital need to secure it effectively or simply accept the increased risk. 

The battle between data consumption and data protection has become a core challenge for enterprises, one with significant implications for both their bottom lines and their customer relationships. A closer look reveals that many organizations, though investing heavily in cybersecurity, stop short of implementing embedded data-centric protections. Relying instead on traditional infrastructure defenses and access controls that only secure around the data, their sensitive data is often left vulnerable to exposure – particularly when breaches occur. The solution lies in data and cybersecurity teams learning to work together and communicate effectively, enabling them to reframe data security strategies to better align with the business’s data needs.

Data Consumption: Fueling Growth, Driving Risk

Businesses today are avid consumers of data. From transaction histories to customer preferences and behavioral insights, data has become the foundation of operational efficiency, personalization, and customer engagement. For sectors like finance, healthcare, and retail, data is an invaluable resource for identifying trends, improving experiences, and enhancing decision-making.

Yet, with this data-driven approach comes mounting risk. The more data a company collects, the greater its obligation to protect it – and the greater the damage if a breach occurs. A single data breach can cost companies an average of $4.88 million as of 2024, according to IBM’s latest Cost of a Data Breach report. Even more concerning, nearly half of these breaches involve sensitive customer information, which raises questions about the adequacy of the status quo for data security practices across industries.

Data consumption can no longer be seen as a low-risk, high-reward strategy. Each new data set introduces another layer of risk, making it imperative that companies collect data while ensuring it remains protected. Businesses that fail to adapt to this reality risk financial and reputational damage. Beyond the immediate costs of a breach, organizations risk eroding customer trust – an intangible but critical asset that may take years to rebuild.

Data Protection: An Often-Overlooked Pillar of Data Strategy

As the volume of data grows and new data sharing or AI pipelines emerge, traditional security approaches – that rely on keeping unauthorized users out – have become insufficient. Many companies believe that strong infrastructure defenses or access controls are enough to secure their data, but this assumption falls short in today’s threat landscape and fails to truly protect sensitive data. Cybersecurity must evolve from an infrastructure-based approach to a data-centric model, where the data itself is protected, regardless of its location.

This shift requires organizations to adopt strategies that prioritize de-identification and pseudonymization, stripping personal data of its sensitivity while still retaining its analytical value. Techniques such as tokenization and anonymization render stolen information useless to bad actors, but maintain usability for business user consumption, significantly reducing the impact of breaches without slowing down business performance or hampering innovation.

By focusing on proactive measures and evolving alongside technological advancements, organizations can implement forward-looking data protection strategies that deliver robust security in an ever-changing landscape.

Balancing Data Consumption with Protection

The tension between data consumption and data protection is more than just a technical hurdle. Security controls can sometimes be seen as barriers to data access, slowing down analytics and limiting insights. However, as mentioned before, lack of strong data protection can lead to severe financial and reputational damage in the event of a breach. 

Key strategies for protecting data include:

• Invert the Security Model: Shift from protecting perimeters, infrastructure, and access, to embedding protection within the data itself. This reduces risks and attack surfaces associated with exposing sensitive information in clear text.
• Customize Protections: Not all data is the same, so all data protection can’t be the same. Tailor security controls to align both the unique risks and usage requirements of different data types.
• Adopt Zero-Trust Principles: Implement a zero-trust, least-privilege approach that evaluates security risk and data usage to ensure robust protection at all levels while also maintaining high data utility.
• Focus on High-Risk Data: Identify, classify, and prioritize protections for the most at-risk yet least accessed data, reducing unnecessary clear-text visibility across a majority of systems and users.
• Minimize Clear-Text Dependencies: Enable sensitive data to remain protected by default, limiting reidentification to only authorized individuals or processes.
• Enhance Business Value: Set de-risked data free for real-time business usage, which reduces security costs, streamlines data pipelines, and drives more analytics or new innovations while ensuring compliance and trust.

Each of these strategies allows for a data-centric approach to security, empowering organizations to leverage data safely while maintaining resilience against breaches.

Striking a Delicate Balance – Future-Proofing Data Strategies

Organizations need to recognize that data consumption and data protection are not opposing forces but complementary goals. IBM’s report reveals that nearly two-thirds of companies increase security spending only after experiencing a breach, emphasizing the need for more proactive strategies and overreactive measures.

Integrating security directly into data systems and pipelines allows companies to safeguard sensitive information without compromising insights or agility. This approach fosters customer trust, meets regulatory standards, and enables businesses to fully harness the value of their data.

A Path Forward – Data as an Asset and a Responsibility

To thrive in the data-driven era, companies must balance accessibility and protection by treating data as both a valuable asset and a shared responsibility. This dual perspective ensures that security measures empower, rather than hinder, innovation and decision-making.

Organizations that embrace this mindset will lead the way and have a competitive advantage with digital innovation, proving that strong protection and data utility can coexist. By prioritizing both, businesses can drive growth, retain trust, and unlock the potential of a secure data-driven future.