Every week, employees paste sensitive data into public AI tools without realizing the risk. A Samsung engineer did exactly that, sharing proprietary source code with ChatGPT to speed up debugging. The code entered OpenAI's training pipeline, and there was no way to retrieve it.
Samsung employees did exactly that. In three separate incidents within 20 days in early 2023, engineers submitted proprietary semiconductor source code and a confidential meeting transcript to ChatGPT. Under OpenAI's default terms at the time, those submissions were eligible for use in model training, and Samsung acknowledged it could not retrieve or delete the data from OpenAI's servers. Samsung temporarily restricted generative AI tools on company devices shortly after while developing in-house alternatives. None of the employees were trying to break policy. There was simply no policy to follow. An AI acceptable use policy (AI AUP) closes that exposure before it becomes a headline.
An AI acceptable use policy is a formal set of rules that governs how employees and contractors interact with artificial intelligence tools across an organization. It defines permitted uses, prohibited activities, approved tools, data handling requirements, and the consequences for violations. It is distinct from a general IT policy because it addresses risks specific to AI systems, including AI-generated content, model outputs, prompt-based data exposure, and the use of public AI tools that may train on submitted data.
The policy applies to all staff who use AI tools in any business context, from drafting internal emails to running customer-facing automations. Contractors, vendors, and third-party platforms that process organizational data using AI are also within scope. Personal devices used for work purposes fall under the policy if they access company systems or handle company data.
Before writing specific rules, a policy needs clear definitions. Artificial intelligence, for policy purposes, refers to systems that use machine learning, large language models, or generative algorithms to produce outputs based on user inputs. AI tools include any application, platform, or API that incorporates these capabilities, whether an approved enterprise tool or a consumer product accessed through a browser.
AI-generated content is any text, image, code, or other output produced by an AI system in response to a prompt. This matters because attribution, accuracy, and intellectual property ownership all depend on how clearly generated content is labeled and reviewed. Organizations also need to define risk tiers for AI tool classifications, separating approved enterprise tools with data processing agreements from unapproved public AI tools with no such protections.
The core of any AI AUP is a clear list of what employees can and cannot do. Permitted activities typically include drafting internal documents, summarizing meeting notes, generating ideas, writing code with human review, and using approved AI platforms for customer service workflows. Prohibited activities include uploading sensitive company data, customer data, source code, or personally identifiable information to public AI tools, using AI to make final decisions on employment, legal, or compliance matters without human oversight, and generating content designed to deceive or mislead.
Edge cases require an approval process rather than a flat prohibition. An employee who needs to use an AI tool not yet on the approved list should have a clear path to request a security review rather than defaulting to unapproved use. This keeps the policy workable without creating blind spots.
Publishing a list of approved AI tools is not a one-time task. New tools require a security review before approval, including vendor contracts that address data protection, model training practices, and retention limits. Approved tools should be revalidated on a defined schedule, at a minimum annually, to account for changes in vendor terms or security posture.
For enterprise or regulated teams, the security posture of AI platforms matters as much as their features. For organizations in regulated industries, particularly healthcare and financial services, certifications like SOC 2 Type 2 and compliance with HIPAA are expected requirements when routing customer data through AI tools. Enterprise buyers increasingly treat SOC 2 Type 2 as a procurement prerequisite regardless of industry.
Read AI's governance posture addresses the structural cause of incidents like the Samsung leak, not just the policy gap that followed. It does not train on customer data by default, applies opt-out recording consent at the meeting level, and surfaces data from integrated services only inside each employee's own knowledge base until they choose to share more broadly. Certifications matter, but architecture is what keeps sensitive data out of a training pipeline in the first place.
Every AI acceptable use policy needs a data classification framework. Before any employee uses an AI tool for a given task, they need to know whether the data involved is public, internal, confidential, or restricted. Confidential data and restricted data, including personally identifiable information, protected health information, source code, payment cardholder data protected under PCI DSS, and financial records subject to regulations such as GLBA or SOX, cannot be submitted to public AI tools under any circumstances.
Outputs from AI systems also require controls. Encryption for stored model outputs, defined retention limits for AI-generated content, and logging of prompts and responses are all components of a complete AI security posture. These controls support compliance audits and give organizations a record of how AI tools were used, which matters when an incident occurs. The stronger architectural move is permission-based by default. Read AI scopes integrated data to each user's own knowledge base, with sharing happening piece by piece rather than through blanket access grants, and runs roughly half a billion permission checks daily to enforce it. That keeps the worst-case exposure surface small even before the policy controls layer on top.
AI-generated content requires human review before it goes anywhere external. This includes customer-facing communications, published articles, regulatory submissions, and any output that represents the organization's position. The policy should require clear labeling of AI-generated content in internal workflows and provenance metadata for outputs that carry legal or compliance significance.
Attribution rules also matter when AI tools generate content that incorporates third-party material. Intellectual property protections can apply to AI-generated outputs that closely resemble existing copyrighted works, though the legal framework is still evolving. Courts have reached different conclusions on whether AI training constitutes fair use, and the U.S. Copyright Office acknowledged in its May 2025 report that AI outputs could infringe in certain circumstances. Both AI providers and users may face liability, which is why clear attribution rules belong in any AUP.
Every AI AUP needs an executive owner, a cross-functional governance team, and defined user responsibilities. Without named accountability, policies drift. The governance team should include representatives from legal, IT security, compliance, and the business units most likely to use AI tools actively.
Training must be mandatory, not optional. Employees need to understand what the policy requires, what tools are approved, and how to report potential violations. Awareness campaigns, quick-reference cards, and approved prompt playbooks all reduce the rate at which employees inadvertently violate policy through ignorance rather than intent. Monitoring AI tool usage for policy violations, retaining usage logs per the organization's retention schedule, and conducting compliance audits at defined intervals complete the enforcement loop.
When a data exposure occurs through an AI tool, organizations need a documented response workflow. This includes immediate reporting procedures, escalation paths for severe incidents, and remediation timelines. Vendors who process organizational data using AI must provide security attestations, sign data processing agreements, and validate their training data sources. Third-party AI use carries the same risk as internal use when the data involved belongs to the organization or its customers.
AI tools and regulations change faster than most policy cycles. Scheduling a full policy review at least annually, with a documented change history published alongside each revision, keeps the policy accurate. Exceptions and waivers need a formal approval process and a central record. Exceptions granted without documentation create compliance exposure that can be difficult to defend during an audit.
An AI acceptable use policy is not a document that slows down AI adoption. It is what makes AI adoption sustainable. Organizations that skip formal guidance end up with inconsistent tool use, data exposure, and reactive bans that cost more than the policies would have. A clear AI AUP, built around defined data rules, approved tools, and real enforcement, lets employees use AI tools safely and gives leadership visibility into how AI is being used across the organization. The goal is not to restrict. It is to keep AI use expanding without the exposure that triggers reactive bans, vendor freezes, or board-level incident reviews. That gets easier when the tools sitting inside the policy are already aligned with it: no training on customer data by default, opt-out consent, and access scoped to the individual employee rather than the whole organization. Read AI was built around that model, which is why it tends to land inside an AUP cleanly rather than against it.
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An AI AUP should cover its scope and applicability, definitions for AI tools and AI-generated content, permitted and prohibited uses, a list of approved AI platforms, data protection requirements, governance roles, training requirements, monitoring and logging procedures, incident response workflows, vendor risk management, and enforcement consequences. Policies that omit any of these areas leave employees without clear guidance in exactly the situations where it matters most.
The policy applies to all employees, contractors, and vendors who use AI tools in connection with organizational systems or data. This includes remote workers, part-time staff, and third parties whose platforms incorporate AI capabilities. Personal devices fall under the policy when they are used for work purposes and access company data or systems.
At minimum, annually. AI regulations, vendor terms, and available tools change quickly enough that a policy written two years ago may no longer reflect current risks or available options. Organizations in regulated industries, including healthcare, financial services, and government, should review more frequently and align their review cycles with relevant compliance frameworks such as HIPAA, PCI DSS, GDPR, and applicable AI-specific regulations.
Approved AI tools are platforms that have cleared a security review, operate under a vendor contract with explicit data protection commitments, and have been authorized for specific use cases within the organization. Public AI tools are consumer-facing applications accessible through a browser or API without organizational controls. Public AI tools typically train on submitted data by default, which is why policies prohibit uploading sensitive data to them without explicit safeguards in place. The cleanest test for an enterprise-grade tool is whether no-training-on-customer-data is the default state rather than a setting buried in admin controls. Read AI applies that default across meetings, emails, messages, and connected platforms, which is what lets it sit inside an AUP rather than outside it.
This article is for informational purposes only and does not constitute legal advice. Consult a qualified attorney or compliance professional before drafting or implementing an AI acceptable use policy for your organization.
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