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Can an employee paste a customer contract into an AI assistant to summarize it? Can an AI agent update a record in your CRM without approval? Without shared rules, employees have to answer these questions on their own.

Build a practical AI use policy for employees. Set rules for approved tools, sensitive data, human review and AI agents with a customizable company template.

AI Policy Template for Employees: A Practical Guide

An AI policy template gives your organization a starting point for setting those rules. It explains which tools employees can use, what information they may share, how to review AI-generated output and who to contact when something goes wrong. This guide includes a practical framework you can adapt to your organization.

An AI use policy is an internal document that explains how employees may use artificial intelligence for work. It can cover generative AI tools, AI features built into business software and agents connected to company data or systems. When agents can access information or take action, the policy should also address AI agent governance.

A policy should not simply ban experimentation. It should help employees answer three practical questions: Can I use this tool? Can I share this information with it? Who needs to review the result?

A policy is one part of responsible AI adoption. It does not replace tool assessments, access controls, privacy reviews or employee training.

A useful AI policy tells employees what to do in specific situations. “Use AI responsibly” is too vague to guide someone who wants to summarize a customer proposal or draft a response with an AI assistant.

List approved tools in a location employees can find. Name the person or team responsible for keeping that list current, and explain how to request approval for a new tool or an AI feature added to an existing product.

Be specific about use cases. For example, employees might be allowed to use an approved assistant to rewrite public-facing text, but need additional review before using AI to analyze a customer file. If employees are already using unapproved tools, Swiftask’s guide to Shadow AI in the workplace offers context for identifying those uses.

Classify data before setting restrictions

The data involved and the tool’s protections should shape the rule. A public announcement, an internal sales document and a confidential contract do not carry the same risks. A policy should distinguish between them rather than apply one blanket rule to every tool.

Public information: material already approved for public sharing.

Internal information: non-public information that may only be used with approved tools and for approved tasks.

Personal data: information relating to an identified or identifiable individual. It should only be processed with AI tools approved for the relevant data and purpose and in accordance with applicable privacy requirements.

Confidential information: trade secrets, credentials, privileged documents, sensitive business information or other protected data that should only be used when the tool and use case have been explicitly approved.

Restricted information: secrets or highly sensitive data that must not be entered into tools unless they have been explicitly approved for that purpose.

Ask your privacy and security teams to help define these categories. Your organization’s rules should reflect the data, systems and contractual requirements involved. Swiftask’s Data Processing Agreement details the technical and organizational measures implemented to protect personal data, including access controls, encryption, monitoring and security governance. Organizations can assess these safeguards alongside their internal AI policies.

AI-generated output should be checked before it is shared or used to make a decision. Define who checks factual accuracy, sources, tone and completeness, and what types of content require a second approval.

The level of review should match the potential consequences. A first draft of a public blog post is different from a customer response that creates a contractual commitment. For an AI agent connected to a business system, specify separately what it may read, prepare and execute. The review process should match its permissions.

Use this sample as a starting point, not as a legally approved policy. Replace the bracketed text and have the relevant teams review it for your organization, tools and jurisdictions.

Purpose and scope: “This policy sets out how [organization] employees and other covered individuals may use AI tools and agents for work. It supplements other applicable company policies.”

Approved tools and access: “The list of approved AI tools is available at [location]. Before using a tool or AI feature that is not listed, employees must request review from [team] using [process].”

Approved use cases: “Approved use cases are listed in [catalog]. For each use case, the organization identifies permitted data, authorized users and any required review before the output is shared or acted on.”

Data and information: “Employees may only share information with an AI tool when they are authorized to do so and the tool is approved for that data and purpose. If unsure, stop and contact [privacy, security or other contact] before submitting the information.”

AI-generated output and decisions: “Employees must review AI-generated content for accuracy and relevance before using it. The following actions require approval from [role or team]: [list actions relevant to your organization].”

AI agents and system connections: “Each AI agent connected to a business system must have a defined purpose and an accountable owner. Its data access and permitted actions must be documented in [register or process]. New connections or execution permissions require review.”

Training and updates: 
“[Team] is responsible for explaining this policy and maintaining the approved-tool list. The policy is reviewed when tools, data, workflows or applicable requirements change.”

The template needs to match your organization’s actual tools and processes. Approval of a tool does not automatically approve every use of that tool.

A policy works when employees can apply its rules without guessing. Publishing the document is only one step.

Inventory current use. Ask teams which tools they use, for what tasks and with what information. Treat the inventory as a way to understand work, not as a hunt for rule-breakers.

Review higher-risk use cases. Involve the teams responsible for IT, security, privacy, HR and legal review as appropriate.

Test the policy against real tasks. Try examples such as summarizing a meeting, reviewing a proposal or drafting a customer response. Employees should be able to identify the right tool and approval path.

Train employees. Explain the limits of AI-generated answers, the data rules and how to report a concern. If your organization is subject to the EU AI Act, Article 4 requires providers and deployers of AI systems to take measures to support the development of AI literacy among staff and other persons dealing with those systems on their behalf.

Align technical access with the policy. If only certain teams may use an agent or model, check that the permissions reflect that decision. Swiftask’s AI governance page describes workspace controls such as roles, permissions and usage monitoring.

Review and update. Revisit the policy when employees raise recurring questions, an incident occurs or the organization changes its tools and workflows.

In short: inventory, decide, explain, configure and review. If employees cannot tell what a rule means in practice, clarify it.

Is an AI policy required for employees?

What is the difference between an AI policy and an acceptable use policy?

Should a company ban unapproved AI tools?

Can a company use this AI policy template as written?

FAQ about AI policies

An effective AI policy gives employees clear answers about which tools they can use, what information they can share and what needs human review. It becomes more useful when training and technical permissions support the same rules.

If your team is defining those rules for AI agents and business tools, explore Swiftask’s AI governance controls.

How to Create an AI Use Policy for Employees?

Your developers are already using AI coding tools. The question is no longer whether to adopt them, but who decides which model can access your source code.

Learn how AI coding works, where it helps, and how to keep source code, model access, costs, and developer control in check.

AI coding: the enterprise guide to coding with AI

AI coding has moved beyond experimentation and into daily engineering work—often before IT has formally approved a tool. This guide explains how AI-assisted coding works, where it delivers value, which risks it introduces, and how to choose a solution that works for an enterprise.

AI coding is the use of large language models to write, complete, fix, explain, or refactor software code. Modern tools can go beyond suggesting the next line: they can read a project, propose changes across several files, and run commands.

Three main AI coding use cases exist today:

Code completion: AI extends what a developer is typing, line by line, inside the editor.

Developer chat: A developer describes a need and receives code, tests, explanations, or debugging guidance.

AI coding agents: An agent receives an objective, plans steps, edits files, and checks its work.

The third category changes the interaction. Instead of asking, “Write this function,” a developer can ask: “Fix this session bug, add tests, and explain what changed.”

Swiftask applies this agent-based approach to enterprise AI: an agent, approved tools, controlled data access, and an auditable record of actions.

Key takeaway: AI coding includes code completion, technical chat, and autonomous coding agents. As autonomy increases, review and governance become more important.

An AI coding assistant combines a language model, project context, and execution tools. Each component affects whether the output is useful, secure, and maintainable.

The model produces suggestions, explanations, and edits. No model is consistently best for every software task. A model that performs well for refactoring may be less effective for system-level debugging or a specific language ecosystem.

A multi-model strategy lets teams select an appropriate model for each task rather than locking all developers into one vendor. Explore Swiftask’s AI model hub to centralize model access under a shared governance layer.

An AI coding assistant needs relevant context to generate useful output. Without access to project structure, open files, dependencies, and explicit instructions, it will produce generic code.

Modern AI coding tools enable developers to specify the files, folders, and terminal output an agent should consider. That control matters: more context can improve relevance, but it also requires clear rules for sensitive repositories.

Execution tools distinguish an AI coding agent from a standard chatbot. They can include:

Reviewing a proposed change set before it is applied

Swiftask code brings these capabilities into Visual Studio Code while keeping sensitive actions subject to developer approval.

The fourth level can remove friction from routine work, but it also carries the highest risk. A simple operating rule helps: destructive actions should never run without human approval.

Where AI-assisted coding helps engineering teams

AI-assisted coding creates its strongest value in execution and comprehension work, not in architecture decisions. It can reduce repetitive effort while leaving technical ownership with the engineering team.

Swiftask supports this model by giving developers an agent inside their editor and giving IT teams visibility into model usage, access, and spend.

Understanding an unfamiliar codebase. A developer joining a legacy project can ask focused questions about flows, dependencies, and conventions before escalating to a senior engineer.

Reducing repetitive work. Unit tests, syntax migrations, documentation updates, boilerplate, and one-off scripts are common candidates.

Performing a first-pass review. AI can flag obvious omissions before a human reviewer evaluates design choices, business logic, and architecture.

Translating code between languages. Teams can use AI to help move a Python script to TypeScript or interpret a legacy COBOL routine.

AI coding tools should not be treated as technical decision-makers. They cannot reliably resolve:

Organizational history behind past engineering decisions

An agent may understand the code it can access. It does not inherently know why your organization rejected microservices three years ago.

In summary: AI coding accelerates known work. It does not replace engineering judgment when requirements are uncertain or trade-offs are strategic.

The main enterprise risk is not that developers use AI. It is that they use it without a shared operating model. Shadow AI use can expose source code, fragment spending, and create undocumented technical debt.

A developer installs a free AI coding tool. No one knows which model receives requests, where data is processed, or what contract governs the service.

Proprietary source code can leave the organization without a clear record of its path or use.

Prompts often contain more than code. They can include API keys, database schemas, customer identifiers, security configuration, and confidential business logic.

Teams need to understand where models process data and whether a provider may retain or use submitted content. Swiftask AI governance centralizes permissions, usage visibility, and approved model access.

Code that works but is not understood by the team becomes harder to maintain. The risk rises when generated changes are merged without review, tests, documentation, or ownership.

Treat AI output like any external contribution: inspect it, test it, and make sure someone can explain it.

Model usage can become difficult to track when developers subscribe to separate tools or use personal accounts. Without budgets and reporting, technology leaders may only discover the total cost after the billing period closes.

A single-model or single-vendor workflow can limit your options when pricing, performance, terms, or availability changes. Enterprise teams benefit from separating their AI workflow from a specific model provider.

Key takeaway: Source code protection, access control, cost oversight, and vendor flexibility all point to the same solution: route model use through one governed enterprise layer.

How to choose an AI coding assistant: six criteria that matter

The right AI coding assistant must support developers without removing IT’s ability to manage security, cost, and access. Code quality alone is not enough for an enterprise deployment.

A tool that writes excellent code but fails on most of these criteria is not a viable enterprise platform.

Swiftask code is a Visual Studio Code extension that provides an AI coding agent inside the editor, backed by Swiftask governance. It is designed for teams that need practical developer assistance without unmanaged AI use on sensitive codebases.

Within a developer’s workspace, the agent can:

Use explicit file, folder, and terminal references as context

Ask structured questions when a request is ambiguous

Let developers keep or revert changes from the editor toolbar

This workflow keeps the developer in control of the workspace and the final result.

Swiftask gives administrators a centralized view of AI coding activity:

Usage and costs by user in the Swiftask workspace

Lists of approved models that can be adjusted by project

A model gateway that applies the same governance rules used across enterprise AI activities

The ability to route sensitive workloads to models hosted in France or Europe, where applicable

Developer work sessions remain stored locally on the developer’s machine. Model calls pass through the Swiftask gateway under your workspace policies.

The same governance layer can also support custom AI agents connected to business tools, data, and workflows beyond software development.

Install the Swiftask code extension from the Visual Studio Code Marketplace. It requires VS Code 1.85 or later.

Open the Swiftask panel from the Activity Bar.

Sign in with your Swiftask account. Available models reflect your workspace rules.

Give the agent a first task: fix a bug, write a test, explain a function, or propose a refactoring plan.

What is the best AI coding assistant?

Is there a free AI coding assistant?

Is code sent to AI confidential?

Will AI replace developers?

Can AI coding tools work with Python, JavaScript, and PHP?

Can an AI coding assistant use European-hosted models?

Take control of AI coding across your engineering teams

Teams that define this framework early can support developer productivity without discovering later that their source code has been shared across unmanaged services. Install Swiftask code in VS Code and give your developers an AI coding assistant that IT can actually govern.

Shadow AI is the use of AI tools, models, or features without formal approval, visibility, or oversight from an organization’s IT and security teams. It can involve an employee using a personal ChatGPT account for work, an AI meeting notetaker, a browser extension, or an AI feature added to an approved SaaS application.

Learn what Shadow AI is, how to detect unapproved AI use, and how to reduce data, security, and compliance risks without slowing teams down.

Shadow AI: Risks, Detection and Prevention

A single prompt can expose customer information, source code, financial forecasts, or internal strategy outside your security perimeter. Yet banning AI rarely works. Employees use unapproved tools because they help them work faster.

This guide explains what Shadow AI is, why it spreads, the risks it creates, and how to bring AI use back under control without blocking productivity.

Shadow AI is the unsanctioned use of artificial intelligence tools, applications, models, or AI-powered features in the workplace. These uses happen outside the approval process and governance framework set by IT, security, legal, or compliance teams.

The term is related to Shadow IT, which describes software used without IT approval. Shadow AI raises a different level of concern because AI tools do more than store information. They can process prompts, summarize documents, generate outputs, call connected tools, and sometimes retain data under terms the organization has not reviewed.

For example, an employee might paste a customer contract into a public chatbot to prepare a summary. The contract has now left the company’s controlled environment. The organization may not know where the data is stored, who can access it, or what terms apply to the interaction.

Key takeaway: Shadow AI is not limited to public chatbots. It includes any AI capability used without approved controls, including embedded SaaS features, browser extensions, code assistants, and personal AI agents.

ChatGPT is not inherently Shadow AI. It becomes Shadow AI when an employee uses it for work without the organization’s approval, security review, data-handling rules, or visibility.

The same principle applies to Claude, Gemini, Microsoft Copilot, AI transcription tools, AI design platforms, coding assistants, and AI-enabled browser extensions. The risk depends on the tool’s configuration, the data entered, its contractual terms, and the controls your organization has in place.

Shadow AI vs. Shadow IT: what is the difference?

Shadow IT is the use of unapproved software, devices, or cloud services. Shadow AI is a specific form of Shadow IT that involves artificial intelligence.

The difference matters because AI can rapidly transform and redistribute sensitive information. An employee does not need to install software, request a license, or open an IT ticket. A browser tab and a personal account may be enough.

For CIOs and CISOs, the practical lesson is simple: Shadow AI cannot be handled only as a procurement issue. It requires a coordinated approach across security, data governance, legal, operations, and employee enablement.

Shadow AI often appears through useful, everyday shortcuts rather than deliberate policy violations. The following examples are common across business functions.

An employee uses a personal ChatGPT, Claude, Gemini, or similar account to rewrite a customer email, analyze a spreadsheet, summarize meeting notes, or draft a proposal.

The risk increases when prompts include personally identifiable information, confidential documents, pricing, source code, or regulated data.

A meeting transcription bot joins a Zoom, Microsoft Teams, or Google Meet call without formal approval. It records conversations, creates transcripts, and may distribute summaries to external services or participants.

Meeting assistants can expose confidential strategy discussions, HR matters, customer negotiations, or technical details.

Writing assistants, translators, summarizers, and research extensions can access content displayed in the browser. That may include CRM records, email threads, support tickets, dashboards, or internal web applications.

4. AI coding assistants connected to internal repositories

A developer uses an unapproved coding assistant with access to source code, API keys, architecture files, or private repositories.

Without clear controls, the company may lack visibility into what code or secrets were shared, which model processed them, and how generated code entered production workflows.

Employees upload planning decks, revenue figures, product roadmaps, or market strategy documents to free AI tools to generate slides, images, or visual assets.

The content may be sensitive even when it does not contain direct personal data.

6. AI features inside approved SaaS tools

A SaaS platform may already be approved, but a new generative AI feature can change how data is processed. The feature may send data to a different provider, introduce new retention terms, or expand access to third parties.

An approved application does not automatically make every AI feature within it approved.

Employees can now build personal automations that connect email, cloud storage, calendars, CRM systems, and public AI models. These workflows may access business data without a security review, logging, or defined ownership.

Why do employees use unapproved AI tools?

Shadow AI is rarely a malicious act. More often, it is a practical response to friction, unmet needs, or pressure to deliver more with less time.

The tool saves time immediately. Drafting, summarizing, translating, researching, and analyzing can take minutes instead of hours.

No approved alternative exists. Teams fill the gap with the tools they already know.

The approval process is too slow. If a review takes months, consumer tools become the default path.

Policies are unclear. Employees cannot follow rules they have never received or understood.

The approved tool is less useful. A restricted or poorly integrated option will be bypassed.

Productivity pressure is high. When peers work faster with AI, adoption spreads informally.

What to remember: Shadow AI is often a symptom, not the root problem. The root problem is the absence of an approved, useful, and easy-to-access AI environment.

Shadow AI creates risk across data protection, security, compliance, intellectual property, operational quality, and cost management.

Every prompt, upload, and connected integration can create a potential data exposure. Customer data, contracts, source code, financial plans, employee information, and trade secrets can leave the company environment in seconds.

A company cannot manage data risk when it does not know which AI tools receive its information.

Unapproved AI use can undermine privacy obligations and contractual commitments. The relevant requirements depend on your industry and location, but common concerns include personal data handling, vendor due diligence, data processing agreements, data residency, retention, deletion rights, and cross-border transfers.

For regulated organizations, unsanctioned AI use can also complicate HIPAA, financial services requirements, public-sector obligations, or customer security commitments.

Intellectual property and trade secret exposure

A product roadmap, patent strategy, pricing model, formula, or unreleased source code can lose protection if it is shared through tools outside the company’s approved controls.

Trade secret protection depends in part on an organization taking reasonable steps to safeguard confidential information. Shadow AI can weaken those safeguards through one copy-and-paste action.

Inaccurate outputs and unreviewed decisions

Generative AI can produce plausible but incorrect content. Without review standards, AI-generated errors can reach customers, executives, legal teams, or operational workflows.

This is especially risky for legal guidance, financial analysis, compliance work, medical information, technical documentation, and customer-facing communications.

After an incident, leaders need to answer basic questions: What data was shared? Which tool processed it? Who used it? When did it happen? What was the resulting output?

Shadow AI makes these questions difficult or impossible to answer. That affects internal investigations, incident response, insurance claims, audits, and regulatory inquiries.

Individual subscriptions, duplicate tools, unmanaged API expenses, and disconnected workflows make AI spending difficult to control. The organization may pay several times for overlapping capabilities while still lacking a reliable view of adoption and business value.

How to detect Shadow AI in your organization?

No platform can identify every unapproved AI use on its own. Detection begins with trust, process, and cross-functional visibility—not surveillance.

Use this six-step approach to identify Shadow AI responsibly.

Start with a short, non-punitive survey: Which AI tools do you use? For what tasks? What data do you enter? What results do you get?

Employees are more likely to share real practices when the purpose is to create a safe alternative rather than punish them.

Check corporate card transactions, expense reports, and software reimbursement requests for individual AI subscriptions, AI APIs, transcription services, browser tools, and design platforms.

This identifies paid tools that may not appear in the official software inventory.

Review AI-enabled browser extensions on managed devices. Prioritize extensions that can read page content, access emails, interact with CRMs, or capture data from internal applications.

4. Assess network and SaaS activity responsibly

Security teams can assess traffic to known AI services and review SaaS logs where appropriate. Any monitoring must be transparent, proportionate, and consistent with employment law, privacy requirements, and internal policy.

The goal is to understand organizational exposure—not to create intrusive employee surveillance.

5. Reassess AI features in existing SaaS tools

Review major SaaS vendors regularly. AI capabilities often change after the original procurement review, and a newly activated feature may affect data handling, permissions, or third-party processing.

6. Prioritize discovered use cases by risk

Classify each use case using two criteria:

Start with the highest-risk quadrant: sensitive data used in critical business processes. This is where Shadow AI can create the most serious operational and compliance exposure.

How to prevent Shadow AI without blocking innovation?

A total ban pushes AI use onto personal devices and personal accounts, where it becomes less visible. A hands-off approach allows risk, duplication, and costs to grow until an incident forces action.

The durable approach is to provide a better, governed alternative.

Give employees access to approved AI models: Employees use public AI tools because they are capable and convenient. Your approved environment should provide strong models for common work, with clear controls for how data is handled. A multi-model AI platform helps teams use the right model for the task without creating separate accounts, unmanaged subscriptions, or vendor lock-in.

Build AI agents for real business workflows: A generic chatbot is useful, but it is rarely enough for operations at scale. Teams need AI agents designed around specific tasks: responding to RFPs, reviewing documents, supporting customer service, preparing meeting follow-ups, or analyzing internal knowledge. Swiftask enables organizations to build no-code AI agents connected to approved data and business tools. When the official option is more useful than a personal workaround, Shadow AI becomes less attractive.

Protect data by design: Employees should not have to become privacy experts before each prompt. Define data classifications, approved use cases, access rules, and technical safeguards that protect sensitive information by default. Controls may include encryption, approved data sources, access segmentation, vendor reviews, and restrictions on certain models or actions.

Make governance easy for employees and complete for IT: Effective AI governance should reduce friction rather than add it. Employees need a clear, reliable way to use AI. IT and security teams need visibility into adoption, permissions, usage, models, and costs. Swiftask centralizes enterprise AI access with role-based permissions, SSO/SAML, audit logs, and usage visibility by user, team, and model. This supports control without forcing employees to manage a patchwork of separate AI accounts.

Create a fast exception process: An employee who needs to test a new AI tool should receive a response in days, not months. A lightweight review process gives innovation a legitimate route and reduces incentives to bypass governance.

Publish a clear AI acceptable use policy: A policy should explain what employees can do—not only what they cannot do. 

Data that must never be entered into public tools

Rules for personal data, confidential information, source code, and customer content

Human review requirements for high-impact outputs

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