Generative AI at Work: How to Protect Your Company's Confidential Information

Generative artificial intelligence has become a useful tool for businesses. It can help draft documents, summarize information, analyze data, prepare proposals, and accelerate tasks that once required several hours of work. However, these benefits also raise an important question: how can an organization take advantage of AI without putting its confidential information at risk?

The risk is not limited to cyberattacks. It can also arise when an employee enters customer information into an unauthorized tool, uploads an internal document to generate a summary, or uses a platform without understanding its privacy and data-retention practices.

This does not mean businesses should avoid artificial intelligence. It means they need clear rules, appropriate tools, and security processes that support responsible use. This guide explains the main risks, preventive measures, and practical steps organizations can take to begin protecting their information.

What happens to the information you share with AI?

When someone uses a generative AI tool, they typically submit an instruction or question known as a prompt. It may contain text, documents, images, code snippets, or other information required to complete a task. The service processes that input and generates a response.

What happens to that data afterward depends on the provider, the specific product, account settings, contractual terms, and enabled features. Some platforms may store conversations to maintain chat history, deliver the service, investigate misuse, or allow users to resume a task. Others offer specific controls for data retention and enterprise administration.

It is therefore inaccurate to claim that every AI tool automatically uses every conversation to train its models. It is equally inaccurate to assume that no service retains information or that all products offer the same privacy protections.

For example, OpenAI states that data from its business offerings, including ChatGPT Business, ChatGPT Enterprise, and its API platform, is not used to train its models by default unless the customer explicitly opts in to sharing it. Microsoft also describes enterprise data protection commitments for Microsoft 365 Copilot and Copilot Chat under their applicable terms.

These differences make it essential to review the documentation for the specific service before using it with company information. See the official OpenAI business data privacy and security information and Microsoft Copilot enterprise data protection terms.

The right question is not simply whether a tool uses data to train its models. Businesses should also understand how long information may be retained, who can access it, which integrations are enabled, how files are handled, and what controls are available to delete or restrict data.

What confidential information should you avoid sharing without authorization?

Before entering business information into an AI tool, identify the type of data involved and determine whether you are authorized to use it with that service. The following categories provide a starting point for developing internal rules.

1. Personal information about customers and employees

Avoid entering full names associated with financial information, home addresses, personal phone numbers, government identification numbers, employment records, performance reviews, or private customer information unless the processing is authorized and the environment has been approved for that purpose.

If you need to analyze customer feedback or summarize survey responses, remove identifying details when they are not necessary. You can replace names with generic labels and remove references that could identify an individual. Remember that removing a name does not always anonymize a dataset: a combination of other details may still identify someone.

2. Contracts, legal documents, and commercial agreements

Contracts may contain confidentiality clauses, negotiated prices, commercial terms, obligations between parties, or information protected by agreements with third parties. Sharing these documents with an unapproved service could violate company policies or contractual commitments.

When you need help summarizing a contract, use an authorized tool and determine which information is genuinely necessary for the task. If you do not have an approved environment, work with a fictional example or an authorized, limited extract instead.

3. Financial and strategic information

Unpublished budgets, sales forecasts, profit margins, expansion plans, negotiations, acquisitions, and internal financial results can reveal commercially sensitive information. Do not share these details simply because a tool promises to save time.

In many cases, you can ask for help building an analytical framework without providing actual figures. For example, request a cost-evaluation template and replace sensitive information with fictional values.

4. Credentials and access information

Passwords, API keys, access tokens, recovery codes, and other technical secrets should never be included in a prompt. If you need help troubleshooting code, replace these values with placeholders such as [API_KEY] or [PASSWORD] and share only the code required to understand the problem.

If a credential has already been shared by mistake, deleting the message is not enough. Assess the incident and, when appropriate, revoke or rotate the credential, review access logs, and follow your organization's security procedures.

5. Intellectual property and technical information

Private source code, unpublished designs, formulas, proprietary processes, prototypes, product plans, and research documents may have competitive value. Before using them with an AI tool, confirm that the service and its configuration are approved for that type of information.

The general rule is straightforward: share only the data needed for the task, using the appropriate tool under conditions authorized by your organization.

The main risks of using generative AI in a business

Accidental information exposure

An employee may copy an entire internal email, database, or report when only a small section is needed to complete a request. The more unnecessary information that is shared, the greater the potential exposure.

Use of unauthorized tools

When a company does not provide clear alternatives, employees may turn to personal accounts or applications that have not been reviewed by the responsible team. This can leave the organization without appropriate access, administration, retention, or oversight controls.

Integrations and excessive permissions

Some AI tools can connect to files, business applications, and other information sources. If an integration receives overly broad permissions, it may be able to access data that the user does not need for the task. The risk depends on the permissions granted, the configuration, and the tool's specific capabilities.

Errors, incorrect answers, and poor decisions

AI can produce plausible but incorrect answers. If it is used to analyze financial information, prepare legal documents, or respond to customers, an unverified answer may create operational or reputational consequences. Confidentiality is only one part of the problem: accuracy and the use of generated results also require attention.

Unclear responsibilities

If nobody knows who approves a tool, who reviews its permissions, or how to report an incident, security measures may remain general recommendations rather than working procedures. Effective security requires designated owners and processes that employees can follow.

The NIST Generative Artificial Intelligence Risk Management Framework Profile provides a reference for identifying, assessing, and managing risks throughout the use of these technologies. Organizations can adapt its principles to their size, industry, and level of exposure.

How can businesses protect their information when using AI?

1. Establish a list of approved tools

Define which services each team may use and for which types of tasks. The evaluation should consider privacy terms, administrative controls, retention options, available integrations, and the company's security requirements.

Not every organization needs the same solution. A company that uses AI only to generate general ideas may have different requirements from one that analyzes internal documents or integrates models into operational systems.

2. Classify information before sharing it

Establish categories that are easy to understand, such as public, internal, confidential, and restricted. Define which tools and approvals apply to each category. Restricted information may require additional controls or be excluded from certain tools entirely.

An effective classification system should be clear, concise, and practical for everyday work. If the rules are too complicated, teams may find it difficult to follow them consistently.

3. Share only the information that is necessary

Reduce documents to the sections relevant to the task. Remove names, identifiers, financial information, and other unnecessary details. Whenever possible, use fictional examples or public information.

This practice also applies when the company uses an approved tool. Authorization to use a service does not mean every type of data can be shared without limits.

4. Review privacy and retention terms

Before approving a service, determine what data it processes, how long information may be retained, who can access it, and what options exist to control storage. Also review the terms that apply to files, conversation history, integrations, and additional features.

Document these decisions and review the terms periodically, since products, settings, and policies can change.

5. Limit access permissions

Apply the principle of least privilege: every user and integration should have only the permissions required to perform its role. Avoid connecting tools to entire repositories when they only need access to one folder or a limited set of documents.

When a solution uses AI agents capable of taking actions, also review which operations they can perform, which systems they can modify, and when human approval is required. The level of autonomy should match the risk associated with the task.

6. Train employees and establish human review

Training should explain which tools are allowed, what data may be used, and how to recognize a risky situation. Include practical work examples, straightforward instructions, and a channel for questions.

Also determine which outputs require human review before they are used. High-impact decisions, sensitive documents, and external communications may need specific checks depending on the process.

7. Prepare an incident response procedure

Employees should know what to do if they accidentally share information, discover a suspicious integration, or receive a response containing data that should not be accessible. Establish a reporting channel, response owners, and criteria for assessing the scope of an incident.

The response may include revoking access, changing credentials, reviewing logs, contacting the provider, or activating the organization's relevant procedures. The appropriate measures depend on the information involved and the circumstances of the incident.

Why your company needs an internal AI use policy

An AI use policy turns security recommendations into operational rules. It does not need to begin as a lengthy document, but it should answer practical questions and assign responsibilities.

• Approved tools: which platforms are authorized and who can approve new solutions.
• Permitted information: which data categories may be used with each tool.
• Responsibilities: who manages access, reviews settings, and answers questions.
• Use of outputs: which responses must be verified and which decisions require human approval.
• Integrations: how connections to files, applications, and internal systems are approved.
• Training: how employees are informed and how good practices are updated.
• Incidents: how data exposure is reported and who coordinates the response.

The policy should reflect the organization's actual needs. A small business might begin with a list of approved tools, a data classification system, and an incident-reporting procedure. A company handling highly sensitive information may need additional technical controls, formal reviews, and specialized advice.

It is also important to avoid broad prohibitions that fail to explain which alternatives are available. When employees need to complete a task but cannot identify a safe option, they may turn to unapproved tools. Communication should explain both the limits and the permitted ways to work with AI.

How to encourage responsible AI use among employees

Security does not depend on technology alone. It also depends on whether people understand the risks and can make appropriate decisions while working.

To encourage responsible adoption, businesses can provide short training sessions, share examples of safe prompts, offer a channel for questions, and publish a quick-reference guide to permitted and restricted data. Leaders should follow the same rules they expect their teams to follow.

It is also useful to assess adoption through indicators such as the proportion of employees trained, the use of approved tools, the number of reviewed integrations, and incident response times. These indicators can reveal opportunities for improvement, but they do not replace a comprehensive risk assessment.

The CISA guidance on staying safe when using AI recommends avoiding the sharing of sensitive or confidential information and maintaining basic cybersecurity practices. Businesses can use these recommendations as a starting point for internal materials adapted to their teams.

Checklist: 7 measures for using AI safely in your business

Before expanding the use of artificial intelligence, check whether your organization can answer yes to the following questions:

1. Do we have a list of approved AI tools and designated owners responsible for approving new solutions?
2. Do employees know which information is public, internal, confidential, or restricted?
3. Have we reviewed the privacy, storage, and retention terms of the services we use?
4. Do we limit the information we share and remove data that is not necessary?
5. Do accounts, integrations, and agents have appropriate and limited permissions?
6. Do we provide training and human review for tasks that require them?
7. Is there a clear procedure for reporting and handling AI-related incidents?

If any answer is no, identify the responsible person and define a concrete action to address the gap. You do not need to resolve every risk at once: start with the tools and processes that handle the most sensitive information.

Conclusion: innovation also means protecting information

Generative artificial intelligence can improve productivity, support analysis, and simplify many business tasks. However, these benefits should be accompanied by measures that protect information, limit access, and establish clear responsibilities.

The strategy is neither to prohibit every AI tool nor to trust security promises without review. It is to understand how each service works, classify data, select appropriate tools, and train the people who use them.

A clear policy, limited permissions, privacy controls, and incident response procedures provide a practical foundation for moving forward. As an organization introduces new capabilities, it should review its controls and adapt its processes to the risks involved.

At Kreativeco, we understand digital transformation as a process that combines technology, people, and operations. Responsible AI adoption requires this integrated perspective: identifying opportunities, designing appropriate processes, and creating the conditions for innovation to deliver value without neglecting the information that keeps a business running.

References:

• OpenAI: Business data privacy, security, and compliance.
• Microsoft: Enterprise data protection in Copilot.
• NIST: Generative Artificial Intelligence Risk Management Framework Profile.
• CISA: Security recommendations for using AI.