AI is reshaping how organizations create, store, and use data, and that shift is exposing gaps in traditional security and governance approaches.

Several factors are driving higher risk:

• Almost all organizations are moving to AI: 95% of organizations are either implementing or developing an AI strategy. That rapid adoption is outpacing many companies’ ability to put the right controls in place.
• Data volumes are surging: Enterprise data volume now averages 2 petabytes and is growing at over 40% year over year. More data, in more places, means a larger attack surface and more complexity.
• 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 up with new AI use cases.
• Weak governance foundations: 62% of leaders say they do not 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 concerns: 80% of risk leaders cite leakage of sensitive data as a top AI concern. They also recognize that AI-generated data can’t be trusted without strong data quality and governance.

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 not just another workload. It forces organizations to rethink how they classify, protect, and govern data across the entire estate before they can confidently scale AI innovation.

Relying on many disconnected tools can seem like a way to cover all bases, but in practice it often increases risk and cost—especially as AI use grows.

Key issues organizations are reporting include:

• Too many tools, not enough visibility: Today, organizations typically rely on 11 or more data security tools. When you add governance and compliance platforms, that number climbs even higher. As tool counts go up, visibility across the full data estate goes down.
• More tools, more incidents: Data shows that organizations using more tools often experience more data security incidents, not fewer. Fragmentation makes it harder to see patterns, correlate alerts, and respond quickly.
• Siloed teams and workflows: Security, compliance, data, and privacy teams frequently work in separate systems with limited interoperability. Leaders describe incident management, data sharing, and audit trails spread across “multiple standalone platforms” with no end-to-end workflow.
• Inconsistent data handling: A fragmented approach leads to duplicated data, inconsistent classification, redundant alerts, and siloed investigations. This undermines both security and governance.
• Higher costs and compliance gaps: Maintaining many tools drives up licensing, integration, and operational costs. It also creates potential gaps for privacy and regulatory compliance because no one has a complete picture.

Leaders are increasingly concluding that they need to reimagine their approach. Instead of adding more point solutions, they are looking for a unified platform that:

• Provides centralized visibility across the full data estate
• Supports shared oversight across security, governance, compliance, and privacy teams
• Reduces duplicated effort and inconsistent policies

This shift is not just about tools; it’s about enabling teams to collaborate on a single, integrated view of data risk so AI innovation can move forward with more confidence.

AI is driving a wave of new regulations, and organizations are finding it difficult to keep up using traditional, siloed approaches.

Today’s regulatory reality looks like this:

• Organizations are facing over 200 daily regulatory updates across more than 900 regulatory agencies.
• Many security leaders at organizations developing AI say they are not prepared to comply with AI regulations.
• 87% of data security, governance, compliance, and privacy leaders now have responsibilities that span multiple areas, reflecting how intertwined these functions have become.

In this environment, a unified platform helps in several practical ways:

• Centralized oversight with shared responsibility: A unified platform supports a model where each business unit owns and manages its data, while security and governance teams maintain centralized oversight and policy enforcement across the full data estate.
• Integrated view of risk and compliance: Leaders want a single solution that brings together data security, governance, privacy, and compliance signals. This includes visibility into user risk profiles, data catalogs, and business applications in one place.
• Region-specific compliance support: Unified platforms can surface gaps and risks for specific regulatory regimes and recommend actions directly in the tooling experience, making it easier to stay audit-ready.
• Better alignment across teams: By unifying tools, organizations can align security, governance, compliance, and privacy teams around common workflows and data, rather than separate systems and dashboards.
• Support for new roles and AI skills: As organizations add roles like AI data governance within security teams, a unified platform provides a consistent environment. Embedded AI copilots can help bridge skills gaps and reduce retraining time.

Organizations are signaling strong intent to move in this direction: over 90% of data security, governance, compliance, and privacy leaders say their organization will adopt a unified solution. Many also plan to implement a unified platform approach that balances centralized oversight with decentralized flexibility.

In practice, this approach helps organizations reimagine how they secure and govern data in the AI era—supporting innovation while maintaining compliance and reducing risk exposure.