Presented by Capital One
Data security is one of the least developed areas in enterprise cybersecurity. IBM reports that by 2025, 35% of data breaches will involve unmanaged data sources, also known as “shadow data.” This highlights a fundamental lack of data awareness. This isn’t due to a lack of tools or investment but rather because organizations often grapple with basic questions: What data exists? Where is it stored? How is it transferred? Who is accountable for it?
In a landscape filled with diverse data sources, cloud platforms, SaaS applications, APIs, and AI models, these questions are increasingly difficult to address. Bridging the gap in data security maturity requires a cultural shift where security is prioritized throughout the entire data lifecycle. This involves a solid inventory, clear classification, and scalable measures that convert policies into automated protective measures.
Visibility as the foundation
The primary obstacle to achieving data security maturity is a lack of visibility. Organizations tend to focus on the quantity of data rather than its composition. Does it include personally identifiable information (PII), financial data, health information, or intellectual property? Without this depth of understanding and inventory, implementing effective protection is challenging.
This challenge can be overcome by prioritizing enterprise capabilities that can detect sensitive data on a large scale across various environments. Detection must be followed by action—deleting unnecessary data and securing necessary data by aligning enforcement with a clearly defined policy.
Mature organizations should approach data security by first understanding their environment. Maintain a comprehensive inventory, classify the data within the ecosystem, and align protections according to classifications instead of relying solely on perimeter controls or targeted solutions.
Securing chaotic data
Data security often lags because data itself is unpredictable. Unlike perimeter security that uses defined boundaries, data can appear in various formats—structured databases, unstructured documents, chat logs, or analytics pipelines. These formats may differ in encoding or transformations, leading to unexpected changes that are often undetected.
Human behavior adds another layer of complexity, introducing risks that perimeter controls cannot foresee. This includes instances like credit card numbers in comment fields, spreadsheets sent to unintended recipients, or datasets used in new workflows.
When protection is added at the end of a workflow, blind spots are created. Organizations depend on downstream checks to identify upstream design flaws. Over time, this complexity grows, making data exposure a matter of when, not if.
A more resilient approach assumes sensitive data will appear in unexpected places and formats, embedding protection from the moment data is captured. Defense-in-depth becomes essential, employing segmentation, encryption, tokenization, and layered access controls.
These protections should follow data throughout its lifecycle, from ingestion to processing, analytics, and publishing. Instead of adding controls later, organizations should design systems that remain secure despite data variability.
Scaling governance with automation
Data security becomes manageable when governance is automated from the start. Clear expectations establish bounded contexts, helping teams understand permissible actions, conditions, and protections for effective data use.
In today’s environment, AI systems often require large data volumes across domains, complicating policy implementation. Effective and safe implementation requires deep understanding, robust governance policies, and automated protection.
Techniques like synthetic data and token replacement help maintain analytical context while obscuring sensitive values. Policy-as-code patterns, APIs, and automation handle tokenization, deletion, retention constraints, and access controls. Engineers can then concentrate on securely innovating with data, enhancing business outcomes.
AI systems must adhere to the same governance and monitoring standards as human workflows. Permissions, telemetry, and controls over model access and information publication are essential. Governance introduces some friction, but the aim is to make it understandable, navigable, and increasingly automated. Processes like confirming purpose, registering use cases, and dynamically provisioning access based on role and need should be repeatable and clear.
At an enterprise level, this necessitates centralized capabilities to implement cybersecurity policies in the data domain. This includes detection and classification engines, tokenization services, retention enforcement, and ownership mechanisms that integrate risk management expectations into daily activities.
When executed well, governance becomes an enabling layer rather than a hindrance. Metadata and classification automatically drive protection decisions while facilitating business discovery and use. Data is safeguarded throughout its lifecycle with robust defenses like tokenization, and deleted when required by regulation or internal policy. There should be no need for manual data handling for every control decision, as policy enforcement is designed into the system.
Building for the future
Closing the data security maturity gap is more about operational discipline than adopting a single breakthrough technology. It involves mapping data, classifying it, and embedding protections into workflows to ensure security is scalable.
Business leaders aiming for measurable progress in the next 18–24 months should focus on three priorities. First, create a comprehensive inventory and metadata-rich map of the data ecosystem. Visibility is essential. Second, establish classification linked to clear, actionable policy expectations, making protection requirements for each category evident. Finally, invest in scalable, automated protection schemes that integrate into development and data workflows.
When protection shifts from reactive add-ons to proactive built-in safeguards, compliance becomes easier, governance becomes stronger, and AI readiness is achievable without sacrificing rigor.
Learn more how Capital One Databolt, the enterprise data security solution from Capital One Software, can help your business become AI-ready by securing sensitive data at scale.
Andrew Seaton is Vice President, Data Engineering – Enterprise Data Detection & Protection, Capital One.
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