How to prevent data leaks in AI: security strategies, LGPD (Brazilian General Data Protection Law), and governance

To prevent data leaks in Artificial Intelligence tools, companies must implement a rigorous data governance policy, mask or anonymize sensitive information before sending it, and use enterprise APIs with contractual clauses that guarantee that the inputs will not be used to train public models.

The challenge of data security in the age of artificial intelligence

The popularization of generative AI tools has brought immeasurable productivity gains, but it has also opened up a critical vulnerability: the inadvertent transfer of intellectual property, trade secrets, and personal data (LGPD) to third-party servers. When an employee enters a billing spreadsheet or the source code of proprietary software into a public chatbot, this data can be incorporated into the model's global knowledge, becoming accessible to competitors in future searches.

The key to mitigating this risk is not to ban the technology, but to create a layer of isolation and control between your technology infrastructure and AI tools.

If we use private AI, are we 100% safe?

The myth of perfect isolation: many managers believe that simply hiring a corporate version of an AI provider is enough to shield the operation. The reality is that cloud and AI security operates under a shared responsibility. The provider guarantees that the data will not train the public model, but if your company does not control internal access levels (Identity and Access Management – ​​IAM), a malicious employee or a leaked credential could still expose confidential data through corporate prompts.

Strategic pillars to protect your operation

To ensure cybersecurity and regulatory compliance when using AI, your organization's IT architecture should follow three fundamental guidelines:

• Automatic anonymization via iPaaS: utilize an integration platform (iPaaS) such as Skyone Studio to create workflows that intercept data collected from internal systems (ERPs, CRMs), cleaning any sensitive information (such as CPF numbers, bank details, and names) before sending the payload to the AI ​​API.
• Secure and certified environments: centralize the use of tools on validated platforms. The use of virtualization and controlled cloud solutions, such as Autosky, ensures that AI interactions occur within a secure network perimeter, with traffic monitoring and data loss prevention (DLP).
• Enterprise contracts and training opt-outs: block the use of free interfaces aimed at end consumers. All corporate interaction must be done via dedicated API connections that have explicit terms prohibiting the use of data for public fine-tuning.

What is the difference between public AI and enterprise AI in the cloud?

Public AI stores and processes prompts to optimize its global algorithms, meaning your data could become someone else's output. Enterprise AI, on the other hand, integrated into secure cloud environments, ensures the logical isolation of requests, retaining information exclusively within your company's subscription and preventing the model from learning from your business data.

How does the LGPD (Brazilian General Data Protection Law) apply to the use of artificial intelligence in companies?

The LGPD (Brazilian General Data Protection Law) requires that all processing of personal data have a defined legal basis and guarantee information security. Sending customer data to AI (artificial intelligence) companies without anonymization techniques or without safeguard agreements with the provider constitutes a serious infraction, subject to fines and reputational sanctions due to unauthorized sharing with third parties.

Could I lose proprietary data or intellectual property using AI?

Yes. If collaborators use public tools without governance, programming codes, strategic plans, and patents embedded in the prompts become part of the AI ​​provider's database, which invalidates trade secrets and allows this information to be exposed in responses generated for external users.

FAQ 

1. What are DLP filters and how do they help with AI security?

DLP (Data Loss Prevention) are security tools that monitor and block the transfer of confidential data based on predefined rules (such as credit card patterns or strategic keywords), preventing this information from leaving the corporate network and reaching external locations.

2. Does data masking affect the quality of AI responses?

No, as long as it's done semantically. Replacing a real name with a generic tag like "Client_A" allows the AI ​​to perfectly understand the business context and provide the requested analysis without needing to access the individual's real identity.

3. How do we monitor what employees are entering into AI tools?

Through monitoring logs and implementing CASB (Computer-Aided Business Edge) security solutions that audit the use of cloud applications within the enterprise, identifying unauthorized AI URL access.

4. Does cloud infrastructure affect the security of AI models?

Absolutely. Hosting or consuming AI models on consolidated public or private cloud infrastructures ensures compliance with key international cybersecurity certifications (such as ISO 27001 and SOC 2), shielding the infrastructure layer.

5. What should we do if we discover that critical data has been leaked into a public model?

The incident committee and the DPO should be notified immediately, the tool provider should be contacted to request the manual deletion of historical logs (if applicable), and the compliance impact should be assessed in accordance with the requirements of the LGPD (Brazilian General Data Protection Law).

Technical Glossary

• iPaaS (Integration Platform as a Service): a cloud-based platform used to connect systems, applications, and data sources in an automated and secure way (Example: Skyone Studio).
• Cloud Computing: cloud computing; delivery of computing services (servers, storage, databases, networks, software) over the internet on demand.
• Fine-tuning: the process of taking an already trained AI model and adjusting it with a specific dataset to make it more specialized in a particular task or business niche.
• Prompt: a text, image, or code instruction provided by the user to a generative AI model to guide the desired response.
• Anonymization: a technical process by which data loses the possibility of direct or indirect association with an individual, making it immune to the privacy rules of the LGPD (Brazilian General Data Protection Law).

Written by Skyone