Artificial Intelligence and Sensitive Data: Risks, Policies, and Best Practices for Companies

Generative artificial intelligence (AI) tools like ChatGPT, Gemini, and others are transforming how professionals work in companies across all sectors. However, this convenience brings a hidden danger: the exposure of sensitive corporate data without proper control.

The Real Problem: Corporate Data Vulnerability

It takes just one employee to copy and paste a commercial spreadsheet, internal report, or customer data into a public AI tool to open a risky door. Many of these platforms store conversations, use the data to train future models, or may be vulnerable to attacks that cause data leaks. Additionally, the lack of transparency on where and how data is processed complicates audits or investigations.​

Main risks include:

• Leakage of confidential company, client, supplier, or strategic project information.​
• Non-compliance with data protection laws like GDPR, LGPD, and others, which require explicit consent and proper data lifecycle management.​
• Disclosure of intellectual property or trade secrets.
• Lack of traceability: it’s often impossible to determine where the data has been shared or stored.
• Risk of disciplinary action for employees and damage to company reputation.​

Real Cases and Common Mistakes

Typical errors involve:

• Uploading contracts to AI tools for rewriting.
• Pasting sensitive data into chats to get quick summaries.
• Submitting internal spreadsheets for fast analysis.

Though these behaviors may seem to solve problems, they expose companies to enormous risks, especially when no clear policies are in place regarding AI tool use.​

Corporate Policies: Balancing Productivity and Risk Exposure

Modern companies need clear internal rules about:

• What types of data can be used with AI models and under what conditions.
• Who may access and use public or external AI tools.
• Continuous monitoring and auditing of interactions with generative AI systems.​
• Constantly updating guidelines as new threats and regulations emerge.​

Rules should define:

• Use of private, controlled, and audited AI environments.
• Periodic training on data privacy, compliance, and responsible digital use.
• Formal processes for anonymizing or masking data before submitting it to AI.
• Clear consent from data owners for any AI applications involving personal information.​

Best Practices for Secure AI Use

• Prefer internal or private AI models hosted on company infrastructure or in private cloud environments to reduce risks.​
• Use data anonymization and masking techniques before any interaction with external AI tools.​
• Limit access and AI functionality according to user authorization levels (principle of least privilege).​
• Implement automated logging and auditing to track information sharing and AI usage.​
• Review contracts, terms of service, and privacy policies of AI tools adopted by the company.​
• Promote regular training so every employee understands the risks and acts consciously.​
• Use automatic alerts and blocks to prevent sending sensitive data to open models accidentally.​

Conclusion

AI is a powerful ally in improving productivity and operational efficiency but can cause serious issues if used without caution. Companies must do more than promote innovation; they must protect the integrity, confidentiality, and traceability of corporate data, safeguarding employees and the business from legal and reputational risks.

It is everyone’s responsibility—from interns to C-level executives—to understand that information security and best practices for AI use are essential to building a reliable and sustainable digital operation.
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FAQ - Risks of Using Artificial Intelligence and Sensitive Data in Companies

1. What are the main risks of using generative AI with sensitive corporate data?

The main risks include leakage of confidential information, non-compliance with data protection laws (such as LGPD and GDPR), exposure of intellectual property and trade secrets, and lack of traceability regarding where data is processed or stored. These risks can cause legal, financial, and reputational damage.

2. Why is it unsafe to copy and paste sensitive information into public AI tools?

Public AI tools often store and use submitted data to improve their models, potentially exposing your information to third parties. Additionally, limited transparency about data destinations and usage increases the risk of leaks, especially without clear corporate policies and access controls.

3. What can happen to the company and employee who misuse AI?

The company may face fines for regulatory non-compliance, loss of trust from clients and suppliers, and reputational damage. Employees can suffer formal warnings or even dismissal for exposing sensitive data without authorization, compromising internal security.

4. What policies should companies implement to mitigate these risks?

Corporate policies should define which data can be used with AI, restrict access with authorization, require data anonymization whenever possible, keep interaction logs, and provide employee awareness training. It's also important to review supplier contracts to ensure security and privacy.

5. Is it possible to use AI safely in the corporate environment?

Yes. Safe use involves preferring private or internal models, ensuring data anonymization, controlling access based on user roles, continuously monitoring activities, providing ongoing training, and implementing alerts to prevent inadvertent submission of sensitive data to public models.

6. What is "Shadow AI" and why is it a problem?

Shadow AI refers to the use of AI tools by employees without company approval or oversight, often involving sensitive data. It poses risks of confidential information exposure and compliance violations, making it difficult to control and govern internal processes.

7. How to ensure AI respects LGPD and other laws?

It's essential to apply anonymization or pseudonymization processes, have clear consent from data subjects before using AI, maintain transparency about how models make decisions, and conduct audits with records of automated data processing to ensure compliance.

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