When organizations deploy AI tools, there’s often an assumption that these tools will honor the same file-level (or folder-level or directory-level) permissions already enforced by existing systems. In practice, that assumption deserves a closer look. (In this blog, when we say “file-level permissions,” we usually mean a combination of file-level, folder- or directory-level, but that’s a mouthful, so when you see “file-level permissions,” you’ll know.
This post explains how file-level permissions differ from permissions to access an AI-tool itself, the ways AI tools may overreach and what governance and technical controls you can adopt to keep your internal risk in check.
File permissions are the foundation of information security inside every organization, yet they were not designed with AI in mind. As machine learning systems begin to read, index and interpret internal data, those long-standing boundaries can blur in unexpected ways.
These permissions define who can view, edit, execute or share a file (or directory). It’s typically managed through more commonly-referenced mechanisms like role-based access controls and conditional access policies (e.g., via information classification labels).
Systems such as Google Drive, Microsoft SharePoint and internal file servers offer ways to set and enforce these controls. For example, in Google Drive, administrators can configure folder permissions to limit access to organizational users (users from the same domain). In SharePoint, administrators can configure permissions to edit and view files or directories by site or group. On internal servers, access often depends on network-based permissions or mapped drives. These systems maintain control as long as users interact directly through the established interface.
Introducing AI tools changes the equation. When an AI system ingests content to answer questions or summarize data, permission boundaries can shift. This means that traditional access controls designed for static databases or file systems may not apply cleanly in AI-driven environments where data is dynamically ingested, transformed and reused across contexts. Problems occur when tools index or embed content beyond their authorized scope.
What does this mean, exactly?
An embedding is a way of converting complex information (like a document, email or conversation) into a list of numbers that captures its core meaning. These representations help AI tools compare and group similar content based on meaning rather than exact wording.
For example, “contract” and “agreement” might have similar embeddings even if they don’t use the same language. This makes search and summarization powerful, but it also means the tool could unintentionally surface or connect information across permission boundaries if the embedded data wasn’t properly scoped.
Common risks of this include:
AI tools ingesting documents beyond permission limits set within the AI tool.
Shared embeddings or caches that span multiple user accounts.
Data exposure through overlapping prompts or cross-user “knowledge transfer.”
To make matters worse, the architecture varies from AI vendor to AI vendor, further compounding the problem. In a shared model instance, a document uploaded by User A might feed into a central index. When User B asks a question, the system could unknowingly use User A’s content to inform the answer. That breaks the intended boundary.
In contrast, isolated per-user instances maintain separate indexes for each account, preserving access integrity. Whether the boundary holds depends entirely on vendor design and configuration choices.
Legal definitions start to matter in unexpected ways when AI tools handle company and customer data. The distinction between a data controller and a data processor isn’t just paperwork. These roles determine who is responsible when information moves, gets exposed or is used outside its agreed-upon intended purpose.
Once an AI system gains access to files beyond its authorized scope, those responsibilities can shift, pulling the organization into sticky data protection territory it may not be prepared to navigate:
Data controller vs. data processor obligations: If an AI tool views more than it should be authorized to view, the organization may inherit responsibilities it never anticipated, exposing it to liability under privacy and regulatory laws or contracts.
Privacy law implications: In some instances, unauthorized internal access may meet the statutory definition of a reportable breach. It could even require notification, auditing or disclosure to consumers or regulators. Logs that document what the AI tool accessed, when and under whose authority are essential. Missing or incomplete logs weaken compliance claims and reduce defensibility.
Contractual and vendor risk: Many vendors pledge to respect file permissions, yet under some license agreements vendors are allowed to use data for model training (a feature widely-known as unwanted) or service improvement (which can mean almost anything).
Review vendor terms carefully and confirm that data separation and deletion policies align with internal standards. Pay attention to different licensing terms that can apply to different licensing tiers (e.g., Free, Pro, Enterprise, etc.). Dig into the contractual provisions that many users ignore, because advertised security capabilities may only apply to Enterprise or Enterprise+ licenses, meanwhile your well-meaning users have installed the freeware version of an AI tool to test it out.
Reputational and trust risk: Even without a breach by external threat actors, internal leaks or accidental access can damage client trust and employee confidence. Transparency and strong controls are critical to maintaining credibility.
How does uploading company documents to AI Tools increase risk? Learn more in our blog post: What You Need to Know Before Uploading Company Documents to AI Tools Best Practices for Access Governance with AI Tools.
Governance surrounding AI access combines legal, compliance and security considerations under one framework. As AI systems become part of daily operations, organizations need defined roles, technical safeguards and clear accountability for how data is accessed and used.
Strong governance relies on documented policies, transparent vendor relationships and regular reviews to ensure permissions maintain integrity, remain aligned with organizational intent and are relevant with every evolution of the AI tool application configuration possibilities. We recommend an approach that includes:
Organizations using AI should establish a clear framework for oversight and response in conjunction with a staggered AI deployment effort.
Begin by defining the people responsible for each stage of governance, including AI Use Case Owners, Data Stewards and Legal Reviewers, to ensure accountability remains clear. Make sure those people are sufficiently trained to fulfill their responsibilities.
Develop a permission matrix that specifies which data types may be accessed by AI tools based on role or project, and schedule regular audits to confirm that permissions remain accurate.
Update and maintain a strong data loss prevention strategy backed by technical control requirements is in place where AI is a focal point and not an afterthought.
Finally, update an escalation plan to guide investigations and responses if the AI system accesses information beyond its intended scope.
Technical safeguards play a central role in controlling how AI tools interact with organizational data.
Use isolated environments or individual AI instances to prevent shared indexing, and apply sandboxing so the system can only view explicitly-approved content.
Limit access through least-privilege settings and classify information (e.g., with tags or labels or careful training to a small group of users) to restrict what the AI can ingest.
Configure specific AI platform controls to align with the company's security policies and practices, including for data loss prevention.
Align data retention configurations across AI to avoid conflict with existing policies
When uploading files, use vetted data masking techniques or strong redaction to remove sensitive details. (Don’t just put a black rectangle over words in a file. Use the redaction feature available in PDF applications.)
Maintain audit logs that capture prompt history and file access, and require vendors to provide transparency for access, subprocessors, logging and data deletion.
Input and output are important considerations for AI-generated content. Learn more about AI data ownership in our blog post.
This simple list of questions will help you with the due diligence necessary to protect your organization when considering potential AI tool vendors:
Does the AI tool truly enforce file-level permissions per user or account?
How are shared embeddings handled across users or clients?
What controls allow administrators to restrict cross-user visibility?
Do logs record which prompt accessed which file?
Can cross-user indexing or knowledge transfer be disabled?
What is the vendor’s deletion and data retention policy?
Are there contractual guarantees that data will not train external models?
Does the vendor disclose details of its retrieval architecture?
What safeguards prevent prompt-based data leakage between users?
AI tools promise efficiency, but they also test the limits of access governance. File permissions in your existing systems do not automatically extend to AI environments. If an AI tool overreaches, the result can be legal exposure, privacy violations or reputational damage. Treat these risks as operational realities and plan accordingly. Governance frameworks, technical isolation and thorough vendor review are the foundation of responsible AI use.
Curious about how ZeroDay Law can strengthen your AI access governance and legal readiness? Contact us to schedule a consultation.