Generative AI Workplace Policy
For Informational Purposes Only
An enterprise-grade internal policy governing how employees and contractors may use generative AI tools at work — covering approved tools, use-case tiers, data classification, IP ownership, high-impact employment decisions, vendor procurement, security controls, incident response, and NIST AI Risk Management Framework alignment.
What This Document Does
A generative AI workplace policy establishes the rules and guardrails for how an organization’s employees, contractors, and agents may use AI tools in their work. It is not a technology policy in the traditional sense — it is a governance framework that addresses the legal, ethical, and operational risks specific to AI systems that generate text, code, images, and other outputs based on training data and user prompts.
This template defines which AI tools are approved, classifies use cases into risk tiers (unrestricted, supervised, restricted, and prohibited), establishes data-handling rules for what can and cannot be input to AI systems, addresses intellectual property ownership of AI-generated outputs, and creates governance structures for ongoing oversight. It is designed to be a living document that evolves as AI capabilities, regulations, and organizational experience develop.
Why Startups Need This
Every company with employees is already using generative AI — the question is whether it is happening with governance or without it. Employees are using ChatGPT, Copilot, Claude, Midjourney, and dozens of other tools to draft emails, write code, create presentations, analyze data, and generate marketing content. Without a policy, they are making individual decisions about what company data to input, what outputs to rely on, and what disclosures to make — or not make.
The risks are concrete and immediate. An employee inputs confidential customer data into a consumer AI tool with no data processing agreement. A developer ships AI-generated code without license review. A recruiter uses an AI screening tool that creates disparate impact against a protected class. A marketing team publishes AI-generated content that infringes a third party’s copyright. A sales team uses AI-generated testimonials without disclosure. Each of these scenarios has already generated real litigation, regulatory action, or reputational harm at other companies.
Key Provisions Explained
Four-Tier Use Classification
The policy classifies AI use cases into four tiers based on risk. Unrestricted uses (brainstorming, research summaries, formatting) require no special approval. Supervised uses (customer-facing content drafts, code generation, data analysis) require human review before use. Restricted uses (legal analysis, financial projections, HR decisions, regulatory filings) require department-head approval and documented review. Prohibited uses (inputting restricted data, autonomous customer communications, generating misleading content, circumventing security controls) are banned outright. Exhibit B maps common use cases to their tiers.
Data Classification and Input Rules
The most immediate risk with AI tools is data leakage — employees inputting confidential information, trade secrets, personal data, or client materials into AI systems that may use that data for training, store it indefinitely, or expose it to other users. This policy establishes clear rules by data classification: public data may be freely used, internal data may be used with approved enterprise tools, confidential data requires specific approval and enterprise-grade data processing agreements, and restricted data (PII, health data, financial records, legal privileged materials) may never be input to any AI tool without written authorization from the data owner and legal review.
High-Impact Employment Decisions
Using AI tools in hiring, performance evaluation, promotion, compensation, or termination decisions creates specific legal exposure under federal and state employment law. The EEOC has made clear that existing anti-discrimination and accommodation requirements apply when AI is used in employment decisions, including the obligation to identify and remediate disparate impact and to provide reasonable accommodations for disability-related limitations. This template requires that any AI tool used in employment decisions undergo a documented impact assessment, that results be reviewed by qualified humans, and that the organization maintain records sufficient to demonstrate compliance with Title VII, the ADA, and emerging state AI employment laws.
NIST AI Risk Management Framework Alignment
The governance structure aligns with the NIST AI Risk Management Framework’s four functions: Govern (establishing roles, policies, and accountability), Map (identifying and classifying AI use cases and their contexts), Measure (assessing and monitoring risks through the use-case registration process), and Manage (implementing controls, responding to incidents, and continuously improving). While NIST AI RMF compliance is voluntary, it provides a defensible framework for demonstrating responsible AI governance to regulators, customers, and investors.
Vendor Procurement and Tool Approval
Not all AI tools are created equal from a legal and security perspective. Consumer versions of AI tools typically have different data-handling terms than enterprise versions — consumer tools may use inputs for training, lack data processing agreements, and provide weaker security guarantees. This policy requires that AI tools go through a procurement and approval process that evaluates data handling, security certifications, IP ownership terms, training-data provenance, model transparency, and contractual protections before the tool is added to the approved list. Exhibit A maintains the approved tools register with these details.
Emerging Provisions (2025–2026)
State AI Employment Laws
Multiple states have enacted or proposed legislation specifically targeting AI use in employment decisions. Illinois requires notice and consent when AI is used in video interview analysis. Colorado’s AI Act imposes risk assessment and disclosure obligations for high-risk AI systems used in consequential decisions including employment. New York City Local Law 144 requires bias audits for automated employment decision tools. This template flags these obligations and requires the organization to monitor emerging state requirements — the compliance landscape is changing rapidly, and a policy written without attention to state-specific rules may be insufficient within months.
Agentic AI Governance
The next generation of AI tools goes beyond generating text in response to prompts — agentic AI systems can browse the web, execute code, send emails, make API calls, and take multi-step actions with limited human oversight. These tools create qualitatively different risks: an agent that sends an email has committed the organization to a communication, an agent that executes code has potentially modified production systems, and an agent that accesses customer data has potentially created a privacy incident. This template includes a tiered permission model for agentic AI (read-only, draft-only, limited action, elevated action) with escalating approval and monitoring requirements.
IP Ownership and Training Data Provenance
The copyright status of AI-generated content remains unsettled. U.S. Copyright Office guidance indicates that works generated entirely by AI without sufficient human authorship may not be copyrightable. This policy requires employees to document their creative contributions to AI-assisted works, maintain records of prompts and modifications, and disclose AI involvement where required by policy or law. It also requires vendor due diligence on training-data provenance — whether the AI tool was trained on properly licensed data — to reduce the organization’s exposure to copyright infringement claims based on the training data.
How to Use This Template
1. Inventory current AI use. Before implementing the policy, survey the organization to understand what AI tools employees are already using, what data they are inputting, and what outputs they are relying on. The policy should govern reality, not an idealized version of it.
2. Populate the approved tools register. Exhibit A should list every AI tool approved for use, with data-handling terms, security certifications, approved use cases, and the responsible tool owner. Unapproved tools should be explicitly prohibited.
3. Classify your use cases. Work with each department to map their AI use cases to the four-tier classification in Exhibit B. This is an ongoing process — new use cases will emerge as AI capabilities evolve.
4. Train before enforcing. Roll out training before enforcement. Employees need to understand not just the rules but the reasons behind them — why certain data cannot be input, why human review is required, and how to document AI involvement in their work.
5. Schedule regular reviews. AI capabilities, regulations, and organizational experience change rapidly. The policy should be reviewed at least quarterly for the first year and at least annually thereafter, with interim updates when material regulatory changes occur.
Related Forms
This template is provided by Montague Law for informational and educational purposes. It does not constitute legal advice and does not create an attorney-client relationship. AI governance involves employment law, intellectual property, data privacy, consumer protection, securities regulation, and rapidly evolving state and federal legislation. Organizations should consult qualified legal counsel to adapt this template to their specific industry, jurisdiction, workforce, and AI use cases. Montague Law is a Florida-based law firm focused on corporate, M&A, venture capital, and technology transactions.