Why is sensitive data exposure the biggest security gap?

Data is the invaluable fuel that propels modern businesses. So, organizations have made it a priority to heavily invest in sophisticated security tools.

However, according to Varonis’ 2025 State of Data Security Report, 90% of organizations still have exposed sensitive cloud data. Similarly, 88% of organizations have stale but enabled ghost users.

But that’s not all. According to IBM’s Cost of a Data Breach Report 2025, 53% of breached organizations reported compromised customer PII. These statistics paint a vivid image: though data is central to businesses, visibility and overall security remain critical issues. 

Data is no longer confined to structured databases. It exists across files, emails, cloud platforms, SaaS applications, and endpoints. Much of it is unstructured, duplicated, or unmanaged, making it difficult to track and protect.

AI is amplifying this problem. About 40% of files uploaded to generative AI tools contains sensitive information, often without governance or oversight. As AI adoption grows, so does the number of systems and identities interacting with data.

Without visibility into what data exists and where it resides, organizations cannot effectively secure it. This lack of visibility is the root of the modern data security problem.

What are the pillars of data and AI security?

To address the challenge of data exposure, organizations need a structured approach that brings consistency and control to how data is managed. Data and AI security is built on three core pillars: Data Discovery, Data Classification, and Data & AI Access Governance.

Each pillar addresses a prominent gap:

• Discovery provides visibility into where data resides across environments. This includes structured systems such as databases, as well as semi-structured and unstructured sources that are often overlooked.
• Classification adds context by identifying the type and sensitivity of data. It enables organizations to distinguish between operational data, sensitive personal information, financial records, intellectual property, and other high-risk categories.
• Access governance enables organizations to verify that data is used appropriately. It defines who or what can access data, under what conditions, and with what level of control.

These three pillars do not exist independently. They create a connected system that fully covers data and AI security. Discovery identifies the complete data landscape, classification defines the appropriate sensitivity, and access governance enforces control based on that context.

This model even extends beyond human users to include machine identities such as AI models. In modern environments, these non-human identities often represent a significant portion of data access activity. Bringing these pillars together can help organizations move from fragmented security controls to a unified, policy-driven approach.

How can organizations discover and classify sensitive data?

Discovery and classification are foundational to a successful data security model. Yet, they are often the most difficult to implement effectively.

This is because modern data environments are highly fragmented. Sensitive information is spread across multiple cloud platforms, on-prem systems, SaaS applications, and endpoints. A significant portion of this data is unstructured, making it harder to identify and categorize.

Some of the most notable challenges include:

• Shadow data that exists without knowledge, approval, or security oversight.
• Inconsistent formats across structured and unstructured data.
• Rapid data growth due to AI adoption that outpaces manual classification efforts.

To address this, organizations need scalable discovery capabilities and classification frameworks. Proper classification can assign meaning to the vast amounts of existing data. This typically includes categories such as PII, protected health information (PHI), PCI, intellectual property, and keys and secrets.

The value of classification comes from how it is used. Once data is classified, organizations can effectively apply retention and deletion policies, restrict or monitor access, and enable masking or redaction for sensitive fields.

At scale, a mature discovery and classification approach does not just fulfill coverage but also helps produce meaningful outcomes. This can include reduced exposure, improved policy enforcement, and measurable risk reduction.

What are the major risks of overpermissive access?

According to research by ReliaQuest, 99% of cloud identities are over-privileged. On a similar note, a 2025 study by the Ponemon Institute highlights that 61% of US firms have suffered from insider data breaches in the past two years, with the average cost of such incidents being a staggering $2.7 million.

This proves that even when organizations understand their data, access remains one of the weakest points in security.

Overpermissive access occurs when users, applications, or service accounts have more access to data than they need. This issue is widespread because access controls are often granted broadly for convenience and rarely revisited.

The impact is significant. Excessive access increases the likelihood of accidental exposure, insider risk, and exploitation during a breach.

To address this, organizations must first carefully inspect access patterns. This includes finding out who is accessing sensitive data, what systems or identities are involved, and whether that access aligns with business needs.

Particular attention must be given to privileged accounts and service identities. These often have extensive permissions and can access large volumes of sensitive data across systems.

In this landscape, effective access governance is key. This requires:

• Aligning access policies with data classification.
• Continuously monitoring usage patterns.
• Identifying and remediating access drift over time.

By reducing unnecessary access, organizations help limit their attack surface and improve overall data protection.

How should organizations govern data used by AI systems?

The adoption of AI is rapidly spreading across every facet of modern businesses. This introduces a new layer of complexity in how data is accessed, processed, and exposed.

Training datasets often include large volumes of data sourced from across the organization. Without proper classification and governance, these datasets may contain sensitive or regulated information.

This creates risk at multiple stages:

• During data preparation and training.
• When models interact with live data.
• Through outputs that may unintentionally expose sensitive information.

So, classification must precede model training. This means validating and classifying all data used in datasets and removing sensitive information when necessary.

Likewise, after deployment of AI tools, data teams must continuously assess how models use and expose data. They also should apply appropriate control mechanisms such as masking or redaction where needed.

AI systems should not be treated as separate from data security. They are an extension of how data is used and must be governed accordingly. By integrating such data and AI security capabilities into the broader AI development lifecycle, organizations can help reduce risk while still enabling innovation.

How does data classification power regulatory compliance?

Regulatory compliance depends on the ability to identify and control sensitive data. Frameworks such as GDPR, HIPAA, and PCI DSS define specific requirements for how data must be handled. However, these requirements cannot be enforced without first understanding where regulated data exists.

This is why compliance programs fail without a proper data foundation.

In such cases, data classification acts as the backbone for compliance, mapping data to regulatory categories. It allows organizations to apply targeted controls based on data sensitivity and enforce critical data lifecycle policies.

This opens up a myriad of essential capabilities:

• Enforcement of retention and deletion policies
• Restriction of access to regulated data
• Implementation of crucial privacy controls

It also simplifies audit processes. Organizations can demonstrate where sensitive data resides, how it is protected, and who has access to it. Access governance further strengthens compliance by ensuring that only authorized identities can interact with regulated data.

Together, data classification and access controls reshape compliance for the modern, AI-enabled era.