Securing Enterprise Workflows: Closing the Data Protection Gap

Data Security Maturity: Closing the Gap

Presented by Capital One

Data security is a critical aspect of enterprise cybersecurity, yet it remains one of the least mature domains. According to IBM, a significant 35% of breaches in 2025 involved unmanaged data sources or “shadow data,” indicating a systemic lack of basic data awareness. The challenge lies not in the absence of tools or investments but in organizations struggling with fundamental questions: What data do we possess? Where is it located? How does it flow? And who is accountable for it?

In the evolving landscape of data sources, cloud platforms, SaaS applications, APIs, and AI models, answering these questions is increasingly complex. Bridging the maturity gap in data security necessitates a cultural shift where security is integrated throughout the entire data lifecycle, underpinned by a robust inventory, clear classification, and scalable mechanisms translating policy into automated safeguards.

Visibility as the Cornerstone

A major obstacle to achieving data security maturity is the lack of basic visibility. Organizations often focus on the quantity of data rather than its composition. Understanding whether data contains personally identifiable information (PII), financial data, health information, or intellectual property is crucial for implementing effective protection measures.

By prioritizing enterprise capabilities that can identify sensitive data across a broad spectrum, organizations can take proactive steps to delete unnecessary data and secure essential data by enforcing a well-defined policy.

Establishing and maintaining an inventory, categorizing the ecosystem, and aligning protection measures with the classification are essential steps for mature organizations.

Securing Diverse Data

Data security lags behind other domains due to the inherent chaos of data itself. Unlike perimeter security, which relies on defined boundaries, data is unpredictable and can manifest in various formats. Human behavior adds complexity, introducing risks that traditional controls may not anticipate.

To address this challenge, protection measures should be integrated from the data capture stage, implementing defense-in-depth strategies such as encryption, segmentation, and layered access controls that travel with the data throughout its lifecycle.

Organizations must design systems that remain secure even when data deviates from expectations, accepting variability as a norm.

Scaling Governance with Automation

Operational sustainability in data security hinges on enforcing governance through automation from inception. Clear expectations and defined boundaries empower teams to understand permissible actions, conditions, and necessary data protections.

Amidst the growing reliance on AI systems, implementing effective governance policies becomes even more critical. Techniques like synthetic data and token replacement enable organizations to preserve analytical context while safeguarding sensitive information.

Automation through policy-as-code patterns, APIs, and tokenization can handle data protection tasks efficiently, enabling engineers to focus on innovation securely.

Building for the Future

Closing the data security maturity gap requires operational discipline rather than a singular technological breakthrough. Establishing a comprehensive data inventory, implementing clear classification tied to actionable policies, and investing in automated protection schemes are key priorities.

By shifting from reactive security controls to proactive, built-in safeguards, organizations can simplify compliance, strengthen governance, and enhance AI readiness without compromising security.

Discover how Capital One Databolt, the enterprise data security solution, can help businesses secure sensitive data at scale for AI readiness.

Andrew Seaton, Vice President, Data Engineering – Enterprise Data Detection & Protection, Capital One.

For more information, contact sales@venturebeat.com.