AI adoption is accelerating, and it is putting new pressure on how organizations secure and govern data. According to the research cited in the paper: - 95% of organizations are either implementing or developing an AI strategy. - Enterprise data volumes are already at an average of 2 petabytes and are growing more than 40% year over year. This rapid growth in both AI usage and data volume is driving several challenges: 1. **More data incidents linked to AI**: As AI tools access more data, incidents such as data leakage and oversharing are increasing. Many organizations do not yet have sufficient permissions, controls, or data hygiene to manage this safely. 2. **Weak data governance foundations**: 62% of leaders admit they do not have a strong data governance structure, and only 25% have a global data quality program. Without governance and quality, data used and generated by AI cannot be fully trusted. 3. **Unprepared leaders and teams**: A large share of security, risk, and data leaders say they are not prepared to manage AI-related risks. Many are hesitant to fully embrace AI until they strengthen their data security posture and governance. In response, 53% of security, risk, and data leaders are increasing budgets to address regulatory requirements and data risks associated with AI. The core issue is that AI is reshaping how data is used, shared, and stored, and most existing security and governance approaches were not designed for this level of scale, speed, and interconnectedness.

Many organizations have responded to new risks by adding more tools, but this has created its own set of problems. The research shows that organizations typically: - Use 11 or more data security tools, and even more when governance and compliance platforms are included. This fragmented tooling leads to several issues: 1. **Limited visibility across the data estate**: With data spread across many systems, leaders struggle to see where sensitive data lives, who is accessing it, and how it is being used. This makes it harder to understand the true data risk posture. 2. **Higher incident volume and cost**: Data suggests that the more tools an organization uses, the more incidents they experience. Fragmentation can increase exposure to risk and drive up operational costs. 3. **Siloed teams and workflows**: Security, compliance, governance, and privacy teams often work in separate systems with limited interoperability. This creates duplicated data, inconsistent classification, redundant alerts, and disconnected investigations. 4. **Ineffective governance and compliance gaps**: When tools and teams do not connect, it becomes harder to maintain consistent policies, prove compliance, and manage audit trails efficiently. Leaders across industries are asking for a unified platform that: - Provides centralized visibility into data and associated risks. - Integrates data security, governance, compliance, and privacy capabilities. - Supports end-to-end workflows for incident management, data sharing, and audit reporting. Most organizations now believe that an integrated, comprehensive approach is more effective than stitching together multiple best-of-breed point solutions. They see unification as a way to reduce complexity, improve collaboration, and better support AI innovation without increasing risk.

AI is reshaping the regulatory landscape and expanding the responsibilities of security and compliance leaders. Organizations are currently facing: - Over 200 daily regulatory updates across more than 900 regulatory agencies. - A situation where many security leaders at organizations developing AI feel unprepared to comply with AI-related regulations. At the same time, roles are converging: - 87% of data security, governance, compliance, and privacy leaders now have responsibilities across multiple areas. A unified platform approach helps address this in several ways: 1. **Centralized oversight with shared responsibility**: A unified platform gives security, governance, compliance, and privacy teams a common view of the full data estate. Each business unit can own and manage its data, while central teams maintain oversight and policy adherence. 2. **Integrated risk, security, and compliance workflows**: By bringing together data security, data loss prevention, privacy, risk management, investigations, and data quality in one place, organizations can take a more holistic approach to managing AI-related risks. 3. **Better support for regulatory requirements**: Unified visibility and audit trails make it easier to identify gaps, align with region-specific regulations, and demonstrate compliance. Leaders specifically want tools that can surface risks and recommend actions directly in the platform. 4. **Enabling trusted AI innovation**: Clean, well-governed, and well-protected data is essential for reliable AI. A unified platform helps ensure that only the right people and systems access the right data, and that AI tools are fed with quality data. 5. **Efficiency and scalability, not job reduction**: Despite concerns, the data does not suggest that unified platforms eliminate jobs. In fact, there are 3.5 million cybersecurity jobs open globally, a 350% increase over the past eight years. Unified platforms, often with AI copilots embedded, are expected to help teams do more with limited time and skills, not replace them. As a result, more than 90% of data security, governance, compliance, and privacy leaders say their organization will adopt a unified solution. They expect this approach to save time, improve their data security posture, reduce risk exposure, and give leadership clearer visibility into how AI and data are being managed.