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

Presented by Capital One

Data security continues to be a critical area in enterprise cybersecurity, with data breaches often involving unmanaged data sources or “shadow data.” This highlights a lack of fundamental data awareness within organizations. Despite the availability of tools and investments, many struggle to answer basic questions about their data, such as its location, movement, and ownership.

The complexity of data sources, cloud platforms, and AI models further complicates the challenge of securing data. Closing the maturity gap in data security requires a cultural shift towards embedding protection throughout the data lifecycle, supported by robust inventory, clear classification, and scalable mechanisms for policy enforcement.

Visibility as the foundation

A key obstacle to achieving data security maturity is the lack of visibility into the data held by organizations. Understanding the composition of data, including sensitive information like PII or financial data, is essential for effective protection measures. Organizations must prioritize capabilities that enable the detection of sensitive data at scale and take action to secure or delete data as needed.

Organizations should approach data security as an “understanding your environment” problem, maintaining an inventory, classifying data, and aligning protections accordingly.

Securing chaotic data

Data security faces challenges due to the chaotic nature of data itself, which can exist in various formats and locations. Protecting data from risks introduced by human behavior requires embedding security measures from data capture through processing and publication.

A resilient security model assumes that sensitive data may appear unexpectedly and implements defense-in-depth strategies like encryption, tokenization, and access controls.

Scaling governance with automation

Operational sustainability in data security relies on automated governance mechanisms that enforce policies from the beginning. AI systems, in particular, require strong governance policies and automated protection measures to access and process data securely.

Centralized capabilities for implementing cybersecurity policy, detection engines, and automation tools are essential for enforcing governance at an enterprise scale.

Building for the future

Closing the data security maturity gap requires operational discipline, including establishing a data inventory, implementing clear classification and policy expectations, and investing in scalable protection schemes.

Business leaders should focus on visibility, classification, and automated protection to enhance data security, governance, and AI readiness.

Discover how Capital One Databolt, the enterprise data security solution, can help organizations secure sensitive data at scale and become AI-ready.

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

Sponsored content is produced by companies with a business relationship with VentureBeat and is clearly marked as such. For more information, contact sales@venturebeat.com.