How to Use Generative AI in Business Without Putting Your Data at Risk

Generative artificial intelligence can help businesses draft proposals, summarize documents, analyze information, and speed up tasks that once required hours of work. However, its usefulness does not eliminate a fundamental question: what happens to the information we share with these tools?

An employee might paste customer data into an AI assistant to prepare a report, upload a contract for summarization, or share commercial information to obtain recommendations. Although the goal may be to save time, these actions can expose confidential information if appropriate safeguards are not in place.

The solution is not necessarily to prohibit artificial intelligence. Instead, organizations need clear rules for using it responsibly. This guide explores the main risks of generative AI in business, the information that should be protected, and the measures companies can implement to benefit from AI without losing control of their data.

Why should businesses be careful when using generative AI?

Generative AI tools process the instructions, files, and context they receive to produce a response. How that information is handled depends on the tool, its configuration, the service terms, and the type of account being used.

Not all platforms offer the same privacy protections, data retention practices, access management features, or rules for using information to improve their models. Before introducing a tool into a business process, organizations should review its terms and determine whether it meets their security requirements.

The Office of the Australian Information Commissioner (OAIC) warns that entering personal information into publicly available AI tools can make that information more difficult to track and control afterward, depending on the platform and its settings. It also emphasizes that organizations must actively manage the privacy risks associated with using personal information in AI systems.

Beyond privacy, businesses should consider several other risks:

• Confidential information exposure: employees may share internal documents, customer data, or strategic information without authorization.
• Data retention: prompts, files, and conversation histories may be stored for certain periods, depending on the service terms.
• Excessive access: a tool connected to internal systems could access more information than it needs to perform its task.
• Inaccurate results: AI may produce plausible but incorrect, incomplete, or outdated answers.
• Unauthorized use: employees may turn to tools that the company has not approved, making oversight and control more difficult.

These risks do not mean that every AI tool is unsafe. They mean that organizations must evaluate the context, data sensitivity, and available safeguards before integrating AI into their operations.

What information should you never share with AI without authorization?

A practical rule is to avoid entering information that you would not share with an external provider without first reviewing the applicable security, privacy, and confidentiality requirements.

A company can establish different information categories and define which tools are authorized to process each one. As a starting point, the following types of information deserve particular scrutiny or restrictions:

• Customer or employee personal information: names linked to private information, home addresses, phone numbers, personal email addresses, identifiers, personnel records, or sensitive employment information.
• Financial information: bank statements, banking details, payment information, payroll records, tax filings, confidential budgets, or unpublished financial reports.
• Credentials and technical secrets: passwords, API keys, access tokens, private keys, recovery codes, and configuration files containing secrets.
• Confidential business documents: contracts, unpublished proposals, private price lists, profit margins, negotiation strategies, and supplier agreements.
• Intellectual property and internal development work: private source code, unpublished designs, algorithms, restricted technical documentation, and product roadmaps.
• Legal or regulated information: case files, documents covered by confidentiality agreements, or data protected by contractual or regulatory obligations.

This list is not a substitute for a risk assessment. Some information may be used with an approved enterprise tool and appropriate controls, while other information may need to remain excluded even from certain corporate environments.

What if I need to analyze a real document?

Before uploading it, verify that the tool is approved for that type of information and that its data-handling terms are compatible with company policies. If authorization is not in place, look for an alternative.

Where practical, remove identifying details, replace names with fictional labels, and retain only the information needed to complete the task. For example, instead of uploading a customer's complete file, you could create an anonymized case description that explains the issue without including the person's name, phone number, email address, or other identifying details.

Remember that removing a name is not always enough to anonymize information. A combination of job titles, dates, locations, or other details may still identify someone. Anonymization should meaningfully reduce that risk.

The main risks of generative AI in the workplace

1. Data exposure through prompts and uploaded files

The risk can begin with an apparently simple request: asking AI to summarize a contract, draft a customer response, or analyze a spreadsheet. If the content includes personal data or trade secrets, the company may expose information beyond its intended environment.

To reduce this risk, establish clear limits on permitted data, avoid uploading entire documents when unnecessary, and use approved tools for tasks involving sensitive information.

2. Unauthorized AI tools

When employees lack an approved alternative or do not understand the internal rules, they may create personal accounts across different AI services. This practice, known as shadow AI, reduces the organization's visibility into which tools are being used and what information is being shared.

The response should not be limited to blocking platforms. Organizations should also provide suitable tools, explain the rules, and establish a process for requesting new solutions.

3. Incorrect or biased responses

Generative AI can invent references, misinterpret a document, or produce a summary that leaves out important details. A well-written answer is not necessarily a correct one.

In finance, legal work, human resources, or customer service, an inaccurate output can have significant consequences. Important decisions should therefore be checked against reliable sources and reviewed by people with the appropriate expertise and authority.

4. Excessive access to systems and documents

When an AI tool connects to repositories, email accounts, databases, or business applications, the risk is not limited to what a person types into a prompt. It also depends on the integration's access permissions.

Apply the principle of least privilege: each user, assistant, or integration should access only the information necessary for its role. Review permissions regularly and remove access that is no longer needed.

5. Unclear data retention and usage practices

Services may differ in how they retain conversations, grant provider access, use data to improve models, and support deletion. Do not assume that a tool immediately deletes everything you share or that every version of a product offers the same protections.

Before approving a tool, review its current documentation, privacy settings, administrative controls, and contractual terms. When its use involves personal or regulated information, involve the teams responsible for privacy, security, or legal review.

7 measures for using generative AI more safely

1. Classify information before using it

Define clear information categories that employees can understand, such as public, internal, confidential, and restricted. For each category, specify which tools may be used and what type of processing is permitted.

For example, text already published on a company website may be suitable for a public tool. An unpublished contract or customer database, however, should be handled under stricter rules.

2. Approve tools and review their terms

Do not evaluate a platform solely on price or ease of use. Check what data it processes, how long it retains information, who can access it, what administrative options it provides, and which terms apply to business use.

Pay particular attention to retention policies, the use of data for model training, user management, information deletion, available integrations, and the provider's contractual commitments. Features and terms can change, so this review should be kept up to date.

3. Minimize and anonymize data

Share only the information required to obtain the desired result. If a task can be completed using aggregated data, synthetic examples, or anonymized records, avoid sending the original data.

Before relying on anonymization, check that the remaining information does not make it easy to identify individuals or reconstruct confidential details. If the task requires identifiable data, it should be handled with proper authorization and in an appropriate environment.

4. Configure access and technical safeguards

Use access controls, multifactor authentication where available, role-based permissions, and procedures for removing unnecessary accounts or access rights. For integrations, limit the data and actions that AI can retrieve or execute.

Depending on the level of risk, activity logging, alerts, sensitive-data detection, and review mechanisms before sharing a response or executing an action can also be useful.

5. Maintain human oversight

Define which outputs can be used as drafts and which require approval. Decisions affecting rights, money, employment, contracts, or sensitive information should have safeguards proportionate to their potential impact.

Verify figures, quotations, sources, calculations, and conclusions before distributing content. If AI contributes to an important decision, record who reviewed the output and which criteria were applied.

6. Train employees and establish clear rules

A policy alone does not guarantee safe use. Employees need to understand what they can do with AI, which information is prohibited, how to verify outputs, and whom to contact when they have questions or identify an incident.

Include examples related to each team's actual responsibilities. Sales, human resources, operations, and technology teams may need different instructions because they work with different information and processes.

7. Monitor usage and prepare an incident response process

Establish a mechanism for identifying the tools in use, reviewing risks, and reporting potential data exposure. The goal is not indiscriminate employee surveillance, but proportionate and transparent controls that align with the organization's obligations.

If someone shares information by mistake, they should know how to report it promptly. The procedure should explain whom to notify, what information to document, how to contain the incident, and when to involve security, privacy, legal, or compliance teams.

How to create an internal AI usage policy

An AI policy should turn general principles into instructions that employees can apply to their daily work. It does not need to be lengthy to get started, but it should define responsibilities, limits, and practical procedures.

At a minimum, include the following elements:

• Purpose and scope: explain which tools, teams, and activities are covered.
• Approved tools: identify authorized services and the process for evaluating new options.
• Data classification: define what information may be entered into each tool and what information is prohibited.
• Privacy and security: establish requirements for access, configuration, data retention, and deletion.
• Human review: specify which outputs must be verified and approved before use.
• Responsibilities: identify who approves tools, answers questions, and manages incidents.
• Training and updates: establish how changes will be communicated and when the rules will be reviewed.

The policy should reflect the company's size, the industries in which it operates, and the sensitivity of the information it processes. If the organization handles personal, financial, health-related, or other regulated information, the assessment should account for applicable legal and contractual obligations.

For a reference on structuring AI risk management, the NIST Generative AI Profile provides a voluntary framework to help organizations identify and manage risks throughout the lifecycle of these systems. It does not replace laws or internal policies, but it can serve as a starting point for organizing responsibilities and safeguards.

How to choose an AI tool for your business

The right tool is not necessarily the most popular one or the one with the most features. It is the tool that addresses a real need and meets the organization's security, privacy, operational, and cost requirements.

Before purchasing or enabling a service, ask the following questions:

• What specific task will it perform, and what information does it need?
• What data is sent to the provider, and how long is it retained?
• Are inputs or outputs used to train or improve models? What control options are available?
• Can the organization manage users, permissions, histories, and settings?
• Can access to internal documents and systems be restricted?
• What security controls, activity logs, and deletion options are available?
• Which contractual terms, confidentiality obligations, and legal requirements apply?
• How can data be exported or the service terminated if the company changes providers?

Verify the answers using current documentation and, where necessary, the contract or direct confirmation from the provider. Do not assume that an enterprise subscription automatically eliminates every risk or that all versions of a product offer the same protections.

For more sensitive processes, it may be worth evaluating options with stronger administrative controls, private environments, or specific deployment models. The decision should be based on a technical and risk assessment rather than a generic security promise.

Checklist before using AI in a business process

Before introducing a tool into a real task, review the following points:

• The task and expected benefit are clearly defined.
• The tool has been approved for the intended use.
• The information to be shared has been classified.
• No personal data, credentials, or confidential information will be shared without authorization.
• Privacy, retention, and data usage terms have been reviewed.
• Access permissions are limited to what is necessary.
• A person is responsible for reviewing the output.
• The team understands the rules and incident-reporting process.
• There is a plan for measuring results and reviewing risks.

If any of these points remains unresolved, pause the use of sensitive data until the conditions are clear. In many cases, it is possible to begin with public information, synthetic data, or a low-risk pilot.

Conclusion: innovate with AI without losing control of your information

Generative AI can improve productivity, support analysis, and accelerate business tasks, but adopting it requires more than choosing a tool and starting to use it. Organizations need to understand their data, define boundaries, evaluate providers, and maintain safeguards throughout the process.

A practical strategy combines a clear policy, approved tools, restricted access, employee training, and human review. These measures do not eliminate every risk, but they help reduce exposure and provide a more structured response when problems arise.

Start with a specific use case, identify the information it requires, and test the solution with low-risk data. Then evaluate the results, adjust the safeguards, and expand its use gradually.

The question is not only what artificial intelligence can do for your business, but how you can use it responsibly. A well-planned implementation makes it possible to explore its benefits without treating privacy and security as secondary concerns.

Further reading

• NIST — Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile.
• OAIC — GenAI tools in the workplace: balancing protection of personal information and business efficiency.
• OAIC — Guidance on privacy and the use of commercially available AI products.
• IBM — The silent risk layer in enterprise AI: Governing data exposure in the age of generative AI.
• Google Cloud — Generative AI security best practices.