Gartner has predicted that worldwide end-user spending on public cloud services will reach $723.4 billion in 2025. That growth will be fueled in part by the continued expansion of cloud collaboration tools like Google Workspace, though Sid Nag, Vice President Analyst at Gartner, also attributes the ramp-up to delivering AI-enabled applications at scale.
Today, as AI expands and reliance on cloud collaboration tools grows, new security risks could compromise critical data, requiring organizations to further strengthen security across their Google, Microsoft, and other cloud environments.
The real challenge for IT leaders isn’t just securing sprawling cloud environments. It’s doing it without losing the productivity gains generative AI makes possible. Read on to explore how organizations can balance AI innovation with strong cloud security without putting sensitive data or agility at risk.
Growing Cloud Environments, Increasing Data Security Complexity
According to Flexera’s 2024 State of the Cloud Report, 89% of organizations now operate in a multi-cloud environment. While this approach offers flexibility and resilience, it also introduces significant complexity and risk. Microsoft’s 2024 State of Multicloud Security Report reveals that only 2% of the 51,000 permissions granted to cloud identities are actually used—yet nearly half are considered high-risk. Organizations using platforms like Google Workspace and Microsoft must manage these challenges proactively to avoid security gaps.
However, the multi-cloud environment is far from the only emerging risk factor in today’s age of AI. As more data flows into the cloud – both Gen AI-related and not – securing that data becomes a much more complicated and high-stakes proposition, since more data equals greater risk and more complexity. The growth of the cloud and the emergence of AI have also led to new regulations from governments all around the world, which makes compliance significantly more challenging.
In addition, the broad adoption of AI tools at the enterprise level has created its own novel risks. AI increases the risk of accidentally oversharing confidential information, especially when data isn’t properly categorized, tagged, or secured. To reduce that risk, organizations should optimize their cloud data before implementing AI. This task becomes even more imperative and complicated for multi-cloud organizations, who then have to accomplish this across multiple environments.
The emergence of AI agents has created another set of challenges. With agents now automating workflows, governance of agents themselves has become more critical and high-risk. This is a trend that we can expect to accelerate further in 2025.
As AI advances and its adoption spreads, data security will become even more complex and consequential. To stay ahead, organizations need to have effective short- and long-term strategies to manage these risks. Without them, they may soon have to face the consequences of inaction, whether regulatory or otherwise.
How to Secure Data Without Sacrificing Productivity
Many assume that enhanced security measures necessarily result in lower productivity and agility, but this isn’t the case. Instead of viewing data security as a barrier to productivity, organizations should explore new approaches. With AI, ensuring security while increasing agility and productivity is a very real possibility.
To accomplish this, organizations should follow a three-step process to reinforce security and agility with AI
1. Prepare a framework to manage and secure data.
Organizations should start by creating or reinforcing an information management (IM) strategy. Many organizations claim to have one, though the 2024 AI and Information Management Report found that most lack basic components of a comprehensive IM approach.
2. Secure the data.
Organizations need to implement industry-standard practices like secure backup, ransomware protection and threat detection. Furthermore, effective data security requires clear visibility into where sensitive information resides, who can access it, and whether those permissions are appropriate. A robust data management platform can automate the identification and clean-up of outdated content and inactive workspaces. It can also enforce consistent security policies across multi-cloud environments, reducing manual effort and minimizing risk while supporting secure collaboration.
3. Optimize the data.
Once data is secured, it should be optimized. This means documents should be properly tagged and classified and superfluous or irrelevant data quarantined or removed. Automation can streamline this process. This helps organizations achieve the ultimate goal: being a secure, lean, and highly organized cloud environment.
These steps – pursued during and before the Microsoft 365 Copilot rollout for instance – map generally onto Gartner’s digital maturity grading scale. This scale measures an organization’s readiness to securely and efficiently adopt new technology like Google Gemini, Microsoft Copilot, and other tools. By following these steps, organizations can reach a level of readiness that allows them to safely and efficiently use AI while securing all related data.
Security is the Enabler of Agility
AI is reshaping how organizations collaborate, innovate, and compete. Without the right data foundation, however, even the most powerful tools can introduce more risk than reward. Balancing productivity and protection is no longer optional; it’s the new baseline for digital leadership.
By taking a proactive approach to data management, cloud security, and AI readiness, organizations can build a resilient and adaptive workplace. For instance, locking down AI safely and simplifying the multi‑cloud strategy are ways to future‑proof the business.
The technology to do so is here. Now’s the time to align your strategy to match it.
