Proofpoint, Inc. has released its second annual Data Security Landscape report, revealing that organizations are experiencing widespread data loss as they navigate the complexities of explosive data growth, the adoption of artificial intelligence (AI), and the integration of AI agents into the workplace. The report indicates that the rapid deployment of AI-driven productivity tools and autonomous agents handling sensitive data is escalating risk, particularly as many organizations lack the necessary visibility and controls to govern this evolving “agentic workspace” where human and AI systems collaborate.
The report, which compiles insights from 1,000 security professionals across 10 countries and data from Proofpoint’s platform, highlights the increasing strain on security teams due to surging data volumes. Ryan Kalember, chief strategy officer at Proofpoint, commented, “We’ve entered a new era of data security where insider threats, relentless data growth, and AI-driven change are testing the limits of traditional defenses. Fragmented tools and limited visibility leave organizations exposed. The future of data protection depends on unified, AI-powered solutions that understand content and context, adapt in real time, and secure information across both human and agent activity.”
Analysis from the report shows that human factors continue to be a primary cause of data loss incidents. Nearly six in ten (58%) organizations attribute their most significant data loss events to careless employees or third-party contractors, with 42% citing compromised users and 32% pointing to malicious insiders. Proofpoint’s telemetry further indicates a concentration of risk, with just 1% of users accounting for 76% of data loss events. Organizations reported an average of 11 data loss incidents annually, with some experiencing multiple monthly incidents that can take weeks to resolve.
Data growth and sprawl are also exacerbating these challenges, pushing visibility and control to their limits. Over the past year, more than a quarter (29%) of organizations saw their data volumes increase by 30% or more. Among enterprises with over 10,000 employees, 41% manage more than a petabyte of data. This expansion carries implications, with 46% of organizations identifying cloud and SaaS data sprawl as a top challenge, and 31% noting that redundant or obsolete data poses significant risk. Proofpoint’s platform data supports this, showing that 27% of cloud storage consists of abandoned, unused data that inflates costs and expands the attack surface.
The emergence of the agentic workspace introduces new data risks. As organizations rapidly deploy AI across enterprise workflows, it is becoming a new class of insider risk comparable to human error. Two in five organizations express concern about data loss through public or enterprise Generative AI tools, and over a third worry about sensitive data being used in AI training. AI agents, often operating with elevated privileges, present additional threats, with 32% of organizations identifying unsupervised data access by agents as a critical concern. These risks are amplified by oversight gaps, as 44% of organizations report insufficient visibility and controls over their Generative AI tools.
Fragmented security architectures continue to impede visibility, response, and remediation efforts. More than one in five organizations (21%) report that resolving a data loss incident can take between one and four weeks. The report notes that 64% of organizations rely on six or more data security vendors, contributing to tool sprawl that increases complexity and overextends security teams. In response, security leaders are increasingly seeking holistic data security and insider risk solutions to mitigate risk and streamline operations. Two-thirds of organizations (65%) have already implemented AI-enhanced data security capabilities for data classification. Half of respondents view the primary benefit of a unified data security solution as enabling the safe and productive use of AI, while 55% believe it will reduce overall data loss risk.
