Closing the data security maturity gap: Embedding protection into enterprise workflows

Data security is a crucial aspect of enterprise cybersecurity, yet it remains one of the least mature domains. A recent report by IBM revealed that 35% of breaches in 2025 involved unmanaged data sources or “shadow data,” highlighting a systemic lack of basic data awareness among organizations. Despite the availability of tools and investments in cybersecurity, many organizations still struggle with fundamental questions about their data: What data do we have? Where does it reside? How does it move? And who is responsible for it?

The complexity of today’s data ecosystem, with multiple sources, cloud platforms, SaaS applications, APIs, and AI models, makes answering these questions even more challenging. To bridge the gap in data security maturity, organizations need to undergo a cultural shift where security is integrated throughout the entire data lifecycle. This involves establishing a robust inventory, clear data classification, and scalable mechanisms that translate security policies into automated safeguards.

Visibility is key to building a strong foundation for data security. Many organizations focus on the quantity of data they possess rather than understanding the nature of the data itself. It is essential to identify sensitive information such as personally identifiable data, financial records, health information, and intellectual property to implement effective protection measures. By prioritizing enterprise capabilities that can detect sensitive data at scale and taking immediate action to secure or delete unnecessary data, organizations can enhance their data security posture.

Data security faces challenges due to the inherently chaotic nature of data. Unlike perimeter security, which relies on defined boundaries, data can exist in various formats and locations, making it unpredictable. Human behavior further complicates the situation, as data may be mishandled or exposed inadvertently. To address these challenges, organizations need to embed protection measures at every stage of the data lifecycle, including data ingestion, processing, analytics, and publishing. Defense-in-depth strategies such as encryption, tokenization, and access controls help mitigate risks associated with chaotic data environments.

Automation plays a crucial role in scaling governance and ensuring operational sustainability in data security. By enforcing governance policies through automation, organizations can create clear boundaries for data usage and access. This becomes particularly important in AI systems that require vast amounts of data to operate effectively. Implementing policy-as-code patterns, APIs, and automation tools can help organizations tokenize sensitive data, enforce retention policies, and dynamically manage access controls. By integrating automated protection mechanisms into their platforms, organizations can focus on innovating with data while maintaining robust security measures.

In conclusion, closing the data security maturity gap requires a shift towards operational discipline and proactive security measures. By establishing a comprehensive inventory, implementing data classification tied to clear policies, and investing in automated protection schemes, organizations can strengthen their data security posture and prepare for the future of AI-driven workflows. Capital One’s Databolt solution offers enterprise data security capabilities to help organizations secure sensitive data at scale and become AI-ready. Learn more about how Capital One Databolt can enhance your data security practices and enable business innovation securely.