Security tools can’t protect what they can’t see. One of the biggest challenges security teams now face is that exposure risk often lives in so-called “shadow data”—forgotten copies of sensitive information scattered across endpoints, shared drives, cloud folders, SaaS tools, and generative AI prompts. That’s why visibility must be the first step in any data security strategy, and AI can help advance those efforts with automated scanning, analysis and classification.
Why shadow data exists, and why AI-driven sprawl is making it worse
According to research from Palo Alto Networks, over 80% of sensitive data lies out of sight of security teams—and the problem continues to proliferate along with increasingly distributed workloads, cloud tool sprawl and, more recently, an explosion of generative AI (GenAI) content. For the most part, IT teams know this data exists, but they lack the means to track, classify and protect it—leaving serious blind spots in security, compliance and governance.
This happens as enterprise technology stacks become more complex and multi-faceted. Ownership—or who’s responsible for which data—becomes unclear, and copies often multiply due to collaboration sprawl across drives, attachments and vendor portals. Old repositories and backups can end up staying online for years all but forgotten about, just as sensitive data can persist when accounts belonging to former employees aren’t properly offboarded.
Here’s a quick breakdown of the common sources of shadow data that tend to go unnoticed:
- Cloud storage containers, such as personal or unmanaged Dropbox or OneDrive accounts and abandoned S3 buckets from past projects.
- SaaS-to-SaaS data transfers, which happen when an employee connects an authorized app to an unauthorized third-party tool or API.
- Log files and metadata, often used for troubleshooting, may unintentionally capture sensitive data like personally identifiable information (PII) in plain text form.
- Communication and collaboration artifacts, such as recorded meetings or old email archives, which are saved locally on employee laptops rather than secure servers.
- Shadow AI data includes GenAI prompts, outputs, attachments and chat histories stored in third-party tools and personal accounts.
In most of these examples the data is unstructured, making it harder to track and govern using traditional analytics tools. Moreover, it tends to exist in rapidly growing volumes across hybrid environments, without any kind of data loss prevention (DLP) measures in place.
How AI-powered data discovery can help teams regain control
Traditional data discovery tools were built for a time when most enterprise data was structured with known patterns and kept in on-premises data centers or closely monitored cloud environments. They used manual tagging, metadata and querying to discover and classify sensitive data. And, because they relied on keyword-based searches, they were less effective for discovering and understanding unstructured data—especially non-textual content.
At today’s scale—where 64% of organizations are managing over one petabyte of data— this approach has become impractical making automated scanning and analysis essential for comprehensive data protection. AI has further advanced how organizations can tackle the shadow data problem. Modern data discovery solutions use machine learning not just to “read” text and “see” visual content, but also understand the context it represents. For example, an internal notes document discussing employee grievances, compensation decisions or disciplinary actions won’t be structured like a database or logfile, and it might not even be formally categorized, but AI-powered discovery would flag it as sensitive based on the subject matter and context.
AI makes life much easier for security teams too. By reading every file in every connected data source, and understanding context in the process, AI can create data maps to cluster and label groups in plain-language terms, while assigning sensitivity scores to each file. Also, instead of having to wade through huge repositories of data, teams can query the data discovery tool using natural language, just as they would with an AI chatbot or copilot. With full lifecycle data-tracking and human-readable summaries, security teams are also able to track the ownership and purpose of every individual file and prioritize high-risk cases without getting bogged down in false positives.
To be clear, AI-assisted data discovery isn’t about replacing human analysts, but rather about meeting the challenge of scale head-on. Human-in-the-loop trust remains as important as ever, but with the help of automation and contextual intelligence, teams can validate findings much faster and with greater accuracy to get a handle on shadow data once and for all.
To learn more about how organizations can address the shadow data challenge, read our blog post on addressing data security in the AI-powered work era. For a deeper look at the strategies security teams can use to protect sensitive information, download The CISO’s Guide to Securing Data in the AI-Ready Enterprise.
