AI Data

In boardroom meetings, executives are discussing problems around AI hallucinations. Government regulators are examining copyright and ethical issues with AI. Corporate legal teams are developing policies on model bias and algorithmic accountability. While all these discussions have merit, the primary short-term concern for most security leaders focuses on data protection.

The CISOs see this: how widespread use of AI tools can lead to increased data exposure. According to a Proofpoint study, nearly half (44%) of organizations lack sufficient oversight of generative AI use. The biggest risk here is not AI tools malfunctioning or making poor decisions; it's the short-sighted use of these tools by humans, leading to data leaks and security issues.

For instance, employees are simply using AI to paste sensitive customer information into unauthorized chatbots to meet deadlines. Employees are using collaboration platforms where AI-assisted tools quietly access files that should never leave the organization's network. Data exposure is generally the first step leading to many AI security breaches in production environments.

The Blind Spot: AI as a Data Mover, Not Just a Model

AI tools can do a lot more than just generate text and respond to user queries. They actively ingest, transform, and redistribute organizational data in ways that traditional security controls struggle to monitor.

Think about how employees use information these days. They use AI assistants to search internal databases for customer history or financial records. They send private documents to get executive summaries before meetings. They use AI to write emails that pull information from multiple systems. They send AI-generated reports via Slack or Teams without considering where the data used to generate them came from.

These workflows often cross boundaries between the inside and the outside. An employee might ask an AI tool a question that requires it to look up information across three databases, combine it with public data, and then send the results to external partners. Each step makes something possible.

According to a co-written blog post by Proofpoint professionals, "We're now entering a new era where humans work side by side with AI assistants and AI agents. This emerging agentic workspace unlocks huge gains in productivity and efficiency. However, it also introduces new dimensions of insider threat that organizations must prepare for."

There is a real problem with visibility. Security teams can tell when a file is sent via email outside of the network. But it's harder for them to track what gets copied into a chat interface in a web browser or what context an AI agent uses to answer a question. CISOs often lack full visibility when it comes to much of their data movement, as they try to protect workflows they can't fully see.

The Behavioral Shift: Accidental Insiders at Scale

Traditionally, insider threats referred to disgruntled employees or acts of corporate espionage. The advent of GenAI has completely flipped this on its head. Today, even the most loyal employees may unintentionally create new micro-exposures through their actions..

AI tools sometimes create pathways around established security procedures. For instance, an analyst uploads financial data to generate quick charts using AI tools because Excel is ill-suited to such rapid chart creation. A sales rep uses AI to help draft responses to customer complaints at the end of their shift. These common examples circumvent existing policies to achieve efficiency and speed.

The challenge here is the sheer volume of these events. Traditional insider threat programs use anomaly-based detection to identify large-scale data transfers, unusual login locations, etc. Low-impact, high-frequency exposures aren't equipped to raise red flags. In fact, when hundreds of employees take similar actions every day, the signal may get lost in the noise. Your team may not have the necessary tools to audit each of these engagements at scale.

Collaboration Platforms as AI On-Ramps

Collaboration platforms are no longer merely communication tools. They now also serve as the initial source (the "first hop") from which data enters AI systems.

  • Before AI uses data, it goes through Slack, Teams, Google Workspace, and Gmail. People use these platforms to share files, but they don't want them to go outside the company. But when AI assistants are added to those platforms, the data breaches the original limits of collaboration. An AI tool that summarizes conversations or suggests responses might automatically index a document that's shared in a Teams channel.
  • OAuth connectors, plugins, and SaaS assistants speed up this process. Third-party AI apps often request many permissions to access data in the workspace. People click "allow" without knowing what they're giving up. An outside AI service can now read months' worth of Slack messages, shared documents, and email threads.
  • When people work together, data classification often stops working. When someone copies and pastes the contents of a confidential file from your document management system into a Slack message, the file loses its label. The metadata doesn't move. The AI assistant that reads that channel doesn't know that the information should be kept private.

Why CISOs Aren't Talking About It Yet (And Why They Will)

This risk remains under-discussed for structural reasons, not because security leaders don't see it coming.

  • Hard to quantify. Unlike the typical "event" of a ransomware attack or a phishing incident, AI-driven data breaches lack a well-defined breach event. In turn, there's no defined breach moment from which to measure the impact or budget for prevention.
  • Few benchmarks or standards exist. As such, CISOs cannot reference specific industry frameworks for the use and handling of AI data. The playbook is still being written.
  • Cross-functional responsibility creates ownership gaps. Ownership is fragmented across multiple departments (CISO, data security teams, application security teams, legal/compliance), and the lack of clear ownership means the problem may not receive adequate attention.
  • Governance frameworks remain nascent. The governance framework for data moving through AI workflows is nascent, and many organizations are still trying to figure out how to establish a basic approval process for AI tools.
  • Regulatory ambiguity delays action. Until there is regulatory clarity on what constitutes acceptable security practices for AI, organizations may be slow to implement proactive security initiatives, and CISOs may wait for regulatory pressure before taking the initiative.

That's changing. As AI adoption scales and the first high-profile data breach incidents occur, this topic will begin to gain traction at the board level.

The Strategic Implications for 2026

AI is going to create data sprawl faster than most organizations can handle. As files are duplicated, modified, and distributed through AI workloads across dozens of systems, every interaction with AI creates new versions of data that require identification and classification.

Internal risk management will no longer be an individual issue, but rather a convergent issue involving collaboration, security, AI governance, and data lifecycle management. Security teams cannot address this solely with user behavior analytics.

Compliance will continue to move away from model governance and towards data governance and digital communications governance. Regulators don't focus on the specific AI models you're using. They want to know if organizations have control over what data those models are accessing. Auditors will likely ask very detailed questions around data classification, retention, and movement through AI systems.

Similarly, cyber insurance carriers will start asking structured questions regarding how your organization uses AI workflows as part of the underwriting process. They will ask questions to determine the level of visibility your organization has into the tools and software you're using related to AI. This will include your organization's data handling policies and procedures, and incident response plans associated with AI-driven processes.

What CISOs Should Be Asking

The right questions will help identify gaps in visibility and control.

  • What data can employees input into AI systems? Set limits on sensitive categories like customer records, financial information, code, and more.
  • Which AI tools are actually in use? Map both enterprise solutions that have been approved and shadow AI tools that employees can use through their personal accounts or browsers.
  • Where does AI intersect with regulated data? Find workflows where AI interacts with PII, PHI, financial records, or other sensitive information.
  • How are collaboration surfaces monitored? Identify what information AI assistants can see when they use Slack, Teams, or email.
  • What is the policy for AI assistants and agents? When employees request new AI features, make sure they know how to get them approved, what they can and can't do with them, and how to get help if they run into problems.
  • What auditability exists for AI-driven decisions? Determine whether you can reconstruct what data an AI tool accessed and what outputs it generated.

Anticipating the Real AI Security Threat

Managing AI data risks is not just about hallucinations or biased models. It's about volumes of data being exposed through tools that workers use every day. Because traditional controls can't keep up with how information moves through these systems, CISOs are moving toward AI-aware data governance.

The conversation is moving from AI ethics to AI data safety. As compliance standards evolve and the first major breaches demand transparency, this risk will come into sharper focus. The choice for organizations is whether to act now or react later.