Presented by Capital One
Data security remains one of the least mature domains in enterprise cybersecurity. According to IBM, 35% of breaches in 2025 involved unmanaged data sources or “shadow data.” This reveals a systemic lack of basic data awareness. This is not due to lack of tooling or investment. That’s because many organizations are still struggling with the most basic questions: What data do we have? Where does he live? How does it work? And who is responsible for this?
In an increasingly complex ecosystem of data sources, cloud platforms, SaaS applications, APIs, and AI models, those questions are becoming more difficult to answer. Bridging the maturity gap in data security demands a cultural shift where security is no longer treated as an afterthought. Instead, security is built into the entire data lifecycle, based on a robust inventory, clear taxonomy, and scalable mechanisms that translate policy into automated guardrails.
Visibility as a basis
The most persistent barrier to data security maturity is basic visibility. Organizations often focus on how much data they have, but not on what that data is made of. Does it include personally identifiable information (PII)? financial data? Health related information? Intellectual property? Without this level of understanding and inventory, it is very difficult to implement meaningful security.
However, this can be avoided by prioritizing enterprise capabilities that can trace sensitive data at scale across large and diverse footprints. Investigations should be paired with action, data should be deleted where it is no longer needed, and data where it is should be protected by aligning enforcement with a well-defined policy.
Mature organizations should start by treating data security as a “understand your environment” problem. Maintain an inventory, classify what is in the ecosystem, and align security with the classification rather than relying solely on perimeter control or point solutions of scale.
securing disorganized data
One reason data security lags behind other security domains is that data itself is inherently disorganized. Unlike perimeter security, which relies on clear ports and defined boundaries, data is largely unpredictable. That is to say, the same underlying information may appear in very different formats: structured databases, unstructured documents, chat transcripts, or analytics pipelines. Each may have slightly different encoding or transformations that introduce unexpected, and often unknown, changes to the data.
Human behavior adds to the challenge, with various actions introducing risks that perimeter controls cannot easily predict. This could be anything from a credit card number copied into a free-form comment field, a spreadsheet emailed outside your intended audience, or a dataset repurposed for a new workflow.
When security is implemented at the end of a workflow, organizations create blind spots. They rely on downstream investigation to catch upstream design flaws. Over time, complexity increases and risk exposure becomes a question of when, not when.
A more flexible model recognizes that sensitive data will be exposed in unexpected places and formats, so security 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.
Critically, these security measures travel along the data lifecycle from ingestion to processing, analysis and publishing. Instead of re-establishing controls, organizations plan for chaos. They accept variability as a given and create systems that remain secure even when data varies from expectations.
Scaling governance with automation
Data security becomes operationally sustainable when governance is implemented through automation from its inception. When combined with clear expectations to create a bounded context: Teams understand what is allowed, under what circumstances, and with what protections the data can be used effectively.
This matters today more than ever. AI systems often require access to large amounts of data across all domains. This makes policy implementation particularly challenging. Doing this effectively and securely requires deep understanding, strong governance policies, and automated security.
Security technologies such as synthetic data and token replacement enable organizations to preserve analytical context while making sensitive values harder to read. Policy-as-code patterns, APIs, and automation can handle tokenization, deletion, retention constraints, and dynamic access controls. With guardrails built into the platforms they use, engineers can focus more on innovating with data and safely driving business outcomes.
AI systems must also operate within the same governance and monitoring expectations as human workflows. Permissions, telemetry, and controls over what models can access, as well as 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. Validating the purpose, registering the use case, and dynamically provisioning access based on role and need should be clear, repeatable processes.
At enterprise scale, this requires centralized capabilities that enforce cybersecurity policy across the data domain. This includes identification and classification engines, tokenization and detokenization services, retention enforcement, and ownership and classification mechanisms that incorporate risk management expectations into daily execution.
When done well, governance becomes an enabling layer rather than a hindrance. Metadata and taxonomy automatically make security decisions, accelerating business discovery and use. Data is protected by strong security such as tokenization throughout its lifecycle and is deleted when required by regulation or internal policy. With an enforced policy by design, there should be no need for teams to manually “touch the data” for every control decision.
building for the future
Simply put, closing the data security maturity gap is less about adopting a single breakthrough technology and more about operational discipline. Create a map. Sort out everything you have. Embed security into workflows so security can be replicated at scale.
For business leaders seeking measurable progress over the next 18-24 months, three priorities stand out.
First, establish a robust inventory and metadata-rich map of the data ecosystem. Visibility cannot be compromised. Second, implement a taxonomy tied to clear, actionable policy expectations. Explain what type of protection each category requires. And finally, invest in scalable, automated security plans that integrate directly into development and data workflows.
When security shifts from reactive bolt-on controls to proactive built-in guardrails, compliance becomes simpler, governance becomes stronger, and AI readiness becomes achievable without compromising rigor.
Learn more how Capital One DataboltCapital One Software’s enterprise data security solution can help your business become AI-ready by securing sensitive data at scale.
Andrew Seaton is Vice President, Data Engineering – Enterprise Data Detection and Protection, Capital One.
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