AI is reshaping how organizations create, store, and use data, and that shift is driving new types of risk.

Several trends stand out:

• AI adoption is now mainstream: 95% of organizations are either implementing or developing an AI strategy. As AI use grows, so does concern about data leakage, oversharing, and misuse.
• Data volumes are surging: Enterprise data volume averages 2 petabytes and is growing at over 40% year over year. More data, in more places, means a larger attack surface and more governance 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 how quickly AI tools are being deployed.

Leaders are aware of the gap:

• 62% say they do not have a strong data governance structure.
• Only 25% have a global data quality program in place.
• 80% of risk leaders cite leakage of sensitive data as a top AI concern and recognize that AI-generated data can’t be trusted without solid data quality measures.

As a result, many organizations 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 trust their data before they can confidently scale AI innovation.

Many organizations have responded to new risks by buying more tools. On average, they rely on 11 or more data security tools, and even more when you include governance and compliance platforms. This fragmented approach creates several issues:

• Limited visibility: Teams struggle to see data and risk across the full data estate. It becomes difficult to answer basic questions like who is using which data, where it lives, and how it’s being shared.
• Higher incident volume and cost: Data shows that the more tools an organization uses, the more incidents they tend to experience. Fragmentation increases exposure and drives up operational costs.
• Operational friction: Siloed teams, disparate workflows, and legacy tools make it hard to manage incident response, data sharing, and audit trails end to end.
• Inconsistent controls: Multiple systems often mean duplicated data, inconsistent classification, redundant alerts, and disconnected investigations. This weakens governance and makes regulatory compliance harder.

Leaders are feeling this pain:

• The majority of users are dissatisfied with their disparate platforms, and three-quarters are uncomfortable with their current experience.
• About a third say that managing data across multiple platforms is a top barrier to collaboration across security, governance, compliance, and privacy teams.

Because of this, organizations are reimagining their approach and moving toward unified platforms that:

• Provide centralized visibility into data, users, and risk.
• Align data security, governance, compliance, and privacy in one integrated experience.
• Reduce duplicated effort, streamline investigations, and improve auditability.

The majority of organizations now believe that a comprehensive, integrated solution is more effective than stitching together multiple best-of-breed tools.

AI is accelerating regulatory change and expanding the scope of security and compliance work.

On the regulatory side:

• Organizations are tracking over 200 daily updates across more than 900 regulatory agencies.
• Many security leaders at organizations developing AI say they are not prepared to comply with emerging AI regulations.
• Compliance is increasingly seen as binary: you are either compliant or not, and you must be able to demonstrate this consistently through audit trails and reporting.

This is reshaping roles and responsibilities:

• AI strategy often sits under the CEO or CIO, but the associated risks flow directly to the CISO and security teams.
• 87% of data security, governance, compliance, and privacy leaders now have responsibilities across multiple areas, reflecting how intertwined these domains have become.
• Organizations are creating new roles, such as AI data governance positions within security teams, to keep pace with AI adoption.

In response, leaders are rethinking their operating model and tooling. They are looking for a unified platform that can:

• Provide centralized oversight of the full data estate while allowing business units to manage their own data.
• Connect data security, governance, privacy, and compliance so teams can manage risk holistically.
• Offer centralized visibility into user risk profiles, data catalogs, and region-specific regulatory requirements, with recommended actions built into the tools.

Adoption intent is strong:

• Over 90% of data security, governance, compliance, and privacy leaders say their organization will adopt a unified solution.
• Organizations expect a unified platform to save time, improve their data security posture, reduce risk exposure, and give leadership clearer visibility.
• Rather than replacing jobs, unified platforms are expected to help teams execute better. This is important in a market with 3.5 million open cybersecurity jobs globally, a 350% increase over the past eight years.

Overall, organizations are reimagining data security and governance as a shared responsibility, enabled by integrated platforms that support both innovation and compliance in the era of AI.