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Picture this:
An employee needs to review a long document before a meeting. Instead of opening the company’s document management system, they upload the file to a personal AI account, ask for a summary, and paste the result into their notes. The meeting goes well, and the employee moves on, having saved a few minutes.
From a records management perspective, the employee’s actions raise several questions.
What the employee did in this scenario is known as shadow AI, and it’s becoming increasingly common. A 2026 Netskope study found that 47% of generative AI users used personal AI applications.
This guide helps organizations identify and mitigate shadow AI risks, explaining what it is, why it poses a records management challenge, and steps for shadow AI governance.
Shadow AI is when employees use AI applications, services, agents, or features without their organization’s approval or oversight.
Some shadow AI comes from tools or systems that employees adopt on their own, such as:
Shadow AI doesn’t always imply that employees have bad intentions. More often, they’re just using tools available to them to get their tasks done more efficiently.
Shadow AI isn’t primarily a problem because employees are using tools their employer hasn’t approved. It’s a problem because that use can move, transform, and outpace the information a records program already governs.
Traditional records management programs assume information stays within managed environments, such as email, document management systems, CRM systems, collaboration platforms, shared drives, and other enterprise applications. Each system has its own retention rules, access controls, and disposal schedules.
Shadow AI creates an additional layer of information outside these controlled environments. Employees can upload documents to an AI service, work with it there, and generate new work products without any of those activities ever touching the company’s normal records management process.
AI doesn’t just store information. It transforms it.
An employee uploads a contract and asks AI a question about it. The AI produces an analysis, and the employee turns that analysis into a summary. The summary then informs a business decision.
Each step in this chain can carry business information, and each one moves a little further from the original document a records schedule was written to cover.
The question here for generative AI records management isn’t whether every prompt must be retained. It’s whether the organization has any criteria for determining which AI-assisted communications or outputs qualify as records under its existing policies and legal obligations.
A company usually knows how long it needs to retain a signed contract. But it’s far less clear whether that schedule covers an AI-generated analysis of that contract, a summary used to justify a business decision, prompts explaining why a decision was made, an AI-assisted employee evaluation, or an activity log from an AI agent.
To determine whether your existing retention schedule adequately covers AI-generated information, take your current retention schedule and place the examples above into it. If that’s difficult, you need to update your schedule to account for AI.
If a shadow AI interaction contains information relevant to litigation, an investigation, or a regulatory inquiry, the company may need to determine whether that information has to be preserved and eventually produced. Shadow AI makes that determination harder, since Legal cannot reliably apply preservation obligations to information they do not know exists.
The traditional records management lifecycle consists of the following stages.
This is when a piece of business information first comes into existence and, ideally, gets noticed by the systems responsible for tracking it. Shadow AI can affect this first stage by making it impossible for the organization to know the AI-generated information exists.
Once information is created, it’s assigned a category (i.e., contract, communication, or HR record) that determines how it will be handled. AI outputs from shadow AI may never go through this step, because no process flagged them for classification.
Classified information should reside in a managed system, such as a document repository or a CRM platform, where it can be tracked and governed. AI-generated content, in contrast, often resides in an employee’s personal account or on an external AI platform.
Retention rules apply to information stored in a managed system and determine how long to keep it. For data generated by shadow AI, these rules might not apply since the information was never part of the company’s infrastructure.
When litigation, an investigation, or a regulatory inquiry arises, relevant information is typically placed under a legal hold to prevent its deletion. Legal teams may struggle to do this with AI information they can’t access or even confirm exists.
At the end of its lifecycle, information should be deleted according to a defined schedule. AI-generated information can miss that schedule in either direction.
NIST’s AI risk management guidance addresses the risks that shadow AI can introduce into the information lifecycle. The guidance notes that AI data governance must account for regulatory obligations, such as data retention requirements, and risks posed by third-party systems used without adequate governance and safeguards.
Shadow AI also affects the following.
Employees may enter personal or regulated information into shadow AI tools not approved for that purpose.
Contracts, source code, financial data, customer information, strategy documents, and proprietary research can end up on unmanaged shadow AI platforms. According to Netskope, sensitive information involved in generative-AI policy violations includes regulated information, intellectual property, and other sensitive corporate data.
Because employees use shadow AI, the organization may have limited visibility into the apps, accounts, integrations, and agents accessing business information.
Unregulated tool use can also create regulatory AI compliance risks. There’s no universal retention period for AI, and compliance requirements for retention and recordkeeping can vary widely by industry, jurisdiction, and record type.
Employees often turn to shadow AI because these tools help them brainstorm and work faster and more efficiently. A policy that simply says “Do not use AI” may not solve the problem, especially if it does not provide enough guidance about:
Ultimately, the business should aim for governed AI adoption rather than eliminating AI.
Here’s what businesses should start doing to mitigate the effects of shadow AI.
Don’t assume that the organization’s official AI tools represent all AI activity. According to Microsoft’s current enterprise guidance, companies should find unsanctioned AI applications to better understand AI usage and evaluate risk.
Make an inventory of the following:
Once you’ve created an inventory of what AI is being used for, map AI to information types by asking, “What information are employees putting into these systems?”
Examples may include:
Next, records, legal, compliance, and business teams should establish clear guidelines for when AI-generated information should be stored in an official system of record. They should avoid keeping every AI interaction forever, focusing instead on the business purpose and the information’s value.
Next, assess whether current records schedules sufficiently cover AI-generated documents, AI-assisted communications, AI-generated analyses, agent logs, AI decision-support records, and prompts or interaction histories when they hold business significance.
Legal hold processes should consider relevant AI tools and data repositories and ask whether the information can be identified, preserved, exported, searched, collected, and produced.
Provide employees with secure, well-governed options for legitimate AI use, making compliant practices simpler and more convenient than resorting to shadow AI.
For each approved AI provider, it’s important to know what information is kept, how long it’s stored, and where it’s located. You should also find out if administrators can access it, whether it can be exported, and how the deletion process works. Additionally, check if customer data is used for model training and what audit or administrative controls are in place.
Training on information governance and AI safety should cover practical scenarios. For instance, they should learn that using an approved enterprise AI tool to rewrite a generic internal announcement is safe, whereas uploading an unreleased acquisition agreement to a personal AI account for summarization is potentially risky, and be able to explain why.
Shadow AI is widespread and can pose risks to you and your data, including records management, privacy, and compliance issues.
Fortunately, businesses do not always need a completely separate records management program for enterprise AI governance. They just need to make sure AI doesn’t operate outside their existing records management program.
Ultimately, the organizations that are best prepared to limit shadow AI will be those that can answer three basic questions:
Do you know where AI-generated business information lives in your organization? To protect your business from shadow AI, start by taking an inventory of AI use, reviewing your retention policies, and determining whether your records management program can govern information created outside traditional systems.
If you’re looking for a partner that can help you store, digitize, shred, or responsibly dispose of data, our local Corodata team in California can help.
Shadow AI refers to the use of unauthorized AI tools or services within an organization without official approval, oversight, or governance from departments like IT, security, legal, or compliance.
Not always. Whether information needs to be treated as a business record depends on its content and purpose, and the organization’s legal, regulatory, and retention requirements.
Shadow AI records management risks occur when business information and AI-generated records exist outside the usual systems that handle retention, preservation, access, classification, and disposal controls.
Shadow AI can cause eDiscovery issues if important business information is stored in an AI system that Legal doesn’t know about or can’t reach. This can make preservation and collection much more challenging.
Companies don’t necessarily have to ban generative AI, since these technologies can help employees work more efficiently. Instead, they should understand how employees use AI, limit their usage to approved tools and use cases, communicate these boundaries to the team, control the movement of sensitive information, and integrate relevant AI-generated information into existing governance processes.
Learn how to smoothly transition to effective Records Information Management with our eBook! Packed with valuable tips, it guides businesses on avoiding risks, implementing a professional RIM system, and using Corodata's solutions to get your records management on track.