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Let’s talk about data security. It’s a crucial aspect of enterprise cybersecurity, yet it remains one of the least mature domains. According to IBM, 35% of breaches in 2025 involved unmanaged data source or “shadow data.” This highlights a systemic lack of basic data awareness. Many organizations struggle with fundamental questions like: What data do we have? Where does it live? How does it move? And who is responsible for it?
In today’s complex data landscape, with various sources and platforms, answering these questions is getting even harder. Closing the gap in data security maturity requires a cultural shift where security is integrated throughout the data lifecycle, based on a solid inventory, clear classification, and scalable mechanisms that automate protection.
Let’s start with visibility
Basic visibility is often the biggest hurdle in achieving data security maturity. Organizations focus on the quantity of data they hold, but not on its composition. Is it personal data? Financial information? Intellectual property? Without this understanding, implementing effective protection becomes challenging.
Organizations can overcome this challenge by prioritizing capabilities that can detect sensitive data at scale and take action accordingly. Deleting unnecessary data and securing important data based on a defined policy is crucial.
Mature organizations treat data security as an “understanding your environment” problem. They maintain an inventory, classify data, and align protections accordingly, rather than relying solely on perimeter controls.
Dealing with chaotic data
Data security lags behind because data itself is chaotic. Unlike perimeter security, data is unpredictable and can appear in various formats. Human behavior adds to the complexity, introducing risks that perimeter controls can’t anticipate.
To address this, protection should be embedded from the start of data capture. Defense-in-depth principles like segmentation, encryption, tokenization, and access controls should be implemented throughout the data lifecycle.
Scaling governance with automation
Operational sustainability in data security comes from enforcing governance through automation. Clear expectations and automated protections enable teams to understand what data can be used and under what conditions.
AI systems, which require access to vast amounts of data, make policy implementation challenging. Techniques like synthetic data and token replacement can help protect sensitive information while enabling effective data usage.
Building for the future
Closing the data security maturity gap requires operational discipline. Establish a data inventory, implement classification tied to clear policies, and invest in scalable, automated protection schemes.
By shifting from reactive controls to proactive guardrails, compliance becomes easier, governance strengthens, and AI readiness is achievable without compromising security.
Curious to learn more? Check out Capital One Databolt, the enterprise data security solution from Capital One Software, to secure sensitive data at scale and prepare your business for AI.
Written by Andrew Seaton, Vice President, Data Engineering – Enterprise Data Detection & Protection, Capital One.
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