AI is reshaping how organizations create, store, and use data, and that shift is amplifying existing risks while introducing new ones.

According to the research summarized in the paper:

• AI adoption is nearly universal: 95% of organizations are either implementing or developing an AI strategy. That means more data is being fed into models, shared with vendors, and exposed to new workflows.
• Data volumes are exploding: Enterprise data volume averages 2 petabytes and is growing at over 40% year over year. More data means a larger attack surface and more complexity to govern.
• AI-related incidents are rising: Organizations are seeing more data incidents attributable to AI, often because permissions, controls, and basic data hygiene haven’t kept pace.
• Governance foundations are weak: 62% of leaders say they don’t have a strong data governance structure, and only 25% have a global data quality program. That makes it hard to trust AI outputs or prove compliance.
• Data leakage is a top concern: 80% of risk leaders cite leakage of sensitive data as a primary AI concern and recognize that AI-generated data can’t be trusted without solid data quality measures.

Because of these pressures, many leaders are hesitant to fully embrace AI until they strengthen their data security posture and governance model. In response, 53% of security, risk, and data leaders are increasing budgets specifically to address AI-related regulatory and risk requirements.

In short, AI is forcing organizations to rethink how they secure, govern, and measure the quality of data across the entire estate before they can confidently scale AI initiatives.

Running many disconnected tools may feel thorough, but the research shows it often increases risk and cost instead of reducing them.

Today, organizations typically rely on 11 or more data security tools, and even more when you add governance and compliance platforms. This fragmented setup leads to several issues:

• Limited visibility across the data estate: Teams struggle to see where data lives, how it’s classified, who’s accessing it, and how it flows across systems. That blind spot is especially risky when AI tools are consuming and generating data at scale.
• More incidents, not fewer: The data shows that the more tools an organization uses, the more data security incidents they tend to experience. Overlap and gaps between tools create confusion and missed signals.
• Duplicated and inconsistent data: Fragmented tools often create duplicated data sets, inconsistent classifications, and redundant alerts. This makes investigations slower and less reliable.
• Siloed teams and workflows: Security, compliance, governance, and privacy teams frequently operate in separate systems with limited interoperability. A third of leaders say that managing data across multiple platforms is a top barrier to collaboration.
• Higher costs and ineffective governance: Multiple point solutions drive up licensing, integration, and operational costs, while still leaving gaps that can lead to regulatory non-compliance.

Leaders in the study describe incident management, data sharing, and audit trails spread across “dozens of different systems that don’t necessarily communicate with each other.” That makes it harder to track how data is used, who touched it, and where it went—exactly the visibility you need to manage AI risk.

This is why many organizations are now looking to reimagine their approach and move toward more unified, integrated platforms that give them a single, consistent view of data risk and governance.

The research points to a clear shift: organizations want to move away from fragmented tools and toward a unified platform that brings security, governance, compliance, and privacy together.

Leaders see several advantages in a unified approach:

• Centralized visibility with shared responsibility: A unified platform lets security and governance teams maintain centralized oversight, while each business unit continues to own and manage its data. This model balances control with flexibility.
• Integrated risk management: By connecting data security, privacy, compliance, and data quality in one place, teams can build more complete risk profiles—for example, linking user risk scores with all data they access across catalogs and business apps.
• Better support for AI initiatives: Clean, trusted data becomes easier to activate across multiple data sources and AI tools. It’s no longer just about locking data down; it’s about enabling safe, compliant innovation.
• Improved efficiency and lower incident costs: Leaders expect unified platforms to save time, strengthen data security posture, reduce risk exposure, and improve visibility for executives. Fewer tools and fewer silos mean fewer incidents and more efficient operations.
• Alignment with evolving roles: 87% of data security, governance, compliance, and privacy leaders already have responsibilities across multiple areas. A unified platform matches how these roles are converging in practice.

Adoption intent is strong: over 90% of data security, governance, compliance, and privacy leaders say their organization will adopt a unified solution. And despite concerns about automation, the data suggests this approach is more about enabling teams than replacing them—there are an estimated 3.5 million cybersecurity jobs open globally, a 350% increase over the past eight years.

In short, a unified platform helps organizations reimagine data security and governance for the AI era by providing:

• End-to-end visibility across the full data estate
• Consistent policies and controls across teams and tools
• Integrated capabilities like data loss prevention, investigations, privacy, and risk management
• A foundation of trusted, high-quality data to power AI responsibly

This is the direction many organizations are taking to balance AI innovation with security, compliance, and privacy expectations.