Presented by Capital One
Data security remains one of the least mature areas of enterprise cybersecurity. According IBM35% of breaches in 2025 involved unmanaged data sources or “shadow data.” This reveals a systemic lack of knowledge of basic data. It is not for lack of tools or investment. This is because many organizations still struggle with the most fundamental questions: What data do we have? Where you live? How does it move? And who is responsible for it?
In an increasingly complex ecosystem of data sources, cloud platforms, SaaS applications, APIs and AI models, those questions are increasingly difficult to answer. Closing the data security maturity gap requires a cultural shift where security is no longer treated as an afterthought. Instead, protection is integrated across the entire data lifecycle, based on robust inventory, clear classification, and scalable mechanisms that translate policies into automated security barriers.
Visibility as a basis
The most persistent barrier to data security maturity is basic visibility. Organizations often focus on the amount of data they have, but not what it is composed of. Does it contain personally identifiable information (PII)? Financial data? Health information? Intellectual property? Without this level of understanding and inventory, it is much more difficult to implement meaningful protection.
However, this can be avoided by prioritizing enterprise capabilities that can detect sensitive data at scale across a large and varied footprint. Detection must be accompanied by action, deleting data where it is no longer needed and protecting it where it is aligning law enforcement with well-defined policy.
Mature organizations should start by treating data security as an “understanding your environment” problem. Maintain inventory, classify what is in the ecosystem, and align protections with classification rather than relying solely on perimeter controls or point solutions at scale.
Protect chaotic data
One of the reasons data security has lagged behind other security domains is that data itself is inherently chaotic. Unlike perimeter security, which relies on explicit ports and defined boundaries, data is largely unpredictable. That is, the same underlying information can appear in very different formats: structured databases, unstructured documents, chat transcripts or analysis channels. Each may have slightly different encodings or transformations that introduce unforeseen and often undetected changes to the data itself.
Human behavior compounds the challenge, with different actions introducing risks in ways that perimeter controls simply cannot anticipate. This could be anything from a credit card number copied into a free-form comments field, a spreadsheet emailed outside of your target audience, or a data set reused for a new workflow.
When protection is built into the end of a workflow, organizations create blind spots. They rely on subsequent checks to detect earlier design flaws. Over time, complexity accumulates and the risk of exposure becomes a matter of when, not if.
A more resilient model means that sensitive data will appear in unexpected places and formats, so protection is built in from the moment the data is captured. Defense in depth becomes a design principle: segmentation, encryption at rest and in transit, tokenization, and layered access controls.
Fundamentally, these safeguards accompany the life cycle of data, from ingestion to processing, analysis and publication. Instead of modernizing controls, organizations design for chaos. They accept variability as a fact and build systems that remain secure even when data differs from expectations.
Expand governance with automation
Data security becomes operationally sustainable when governance is applied through automation from its genesis. When combined with clear expectations to create bounded contexts, teams understand what is allowed, under what conditions, and with what protections data can be used effectively.
This matters more than ever today. AI systems often require access to huge volumes of data across domains. This makes policy implementation particularly challenging. Doing so effectively and securely requires deep understanding, strong governance policies, and automated protection.
Security techniques such as synthetic data and token replacement allow organizations to preserve analytical context while making sensitive values more difficult to read. Policy patterns as code, APIs, and automation can handle tokenization, deletion, retention restrictions, and dynamic access controls. With guardrails built into the platforms they use, engineers can focus more on innovating with data and improving business outcomes securely.
AI systems must also operate within the same governance and monitoring expectations as human workflows. Permissions, telemetry, and controls over what models can access, along with what information they can publish, are essential. Governance will always introduce some degree of friction. The goal is to make that friction well understood, navigable and increasingly automated. Confirming purpose, registering a use case, and dynamically providing access based on function and need should be clear and repeatable processes.
At an enterprise scale, this requires centralized capabilities that implement cybersecurity policies in the data domain. This includes detection and classification engines, tokenization and detokenization services, retention compliance, and ownership and taxonomy mechanisms that cascade risk management expectations into daily execution.
When done well, governance becomes an enabling layer rather than a bottleneck. Metadata and classification drive protection decisions automatically while accelerating discovery and business use. Data is protected throughout its lifecycle through strong defenses such as tokenization and is deleted when required by regulation or internal policy. Teams should not need to manually “touch the data” for every control decision, with policies applied by design.
Building for the future
Simply put, closing the data security maturity gap is less about adopting a single innovative technology and more about operational discipline. Build the map. Classify what you have. Build protection into workflows so security is repeatable at scale.
For business leaders seeking measurable progress over the next 18 to 24 months, three priorities stand out.
First, establish a robust inventory and metadata-rich map of the data ecosystem. Visibility is non-negotiable. Second, implement a classification linked to clear and viable political expectations. Make it clear what protections each category requires. And finally, invest in automated, scalable protection schemes that integrate directly into data and development workflows.
When protection moves from built-in reactive controls to built-in proactive security barriers, compliance becomes simpler, governance is strengthened, and AI readiness becomes achievable, without compromising rigor.
More information how Capital One Data BoltCapital One Software’s enterprise data security solution can help your business become AI-ready by protecting sensitive data at scale.
Andrew Seaton is vice president of data engineering: enterprise data discovery and protection at Capital One.
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