Preventing Data Leaks When Using AI: How to Protect Sensitive Data Before It Is Transmitted

When using AI, sensitive data rarely leaks due to malicious intent. Often, there is simply no controlled path from the prompt to the model. If a request contains contract clauses, personal data, customer numbers, source code, or internal reports – and is sent to an external AI service without being reviewed – it leaves the organization’s sphere of control.

Appeals to employees and policies alone will not solve this problem. Anyone who wants to prevent the leakage of sensitive data must control the technical handoff point – that is, the moment when a prompt leaves the organization and is handed over to an external model.

In many organizations, there is no standardized approach to AI models. Personal accounts, browser extensions, individual departmental licenses, and unauthorized tools create parallel data paths.

Such uncontrolled data flows have already been a problem in the past. Generative AI, however, exacerbates the issue: The immediate benefit is high, the barrier to entry is low, and prompts often contain entire documents, internal analyses, code snippets, or contract excerpts. This increases the likelihood that sensitive information will be transmitted on a large scale to unauthorized services. For IT, security, and data protection managers, this makes it even more difficult to track which information is being sent, when, and where.

A centralized AI access point creates a defined control point here. Every request passes through a designated endpoint where security, data protection, and routing rules are uniformly applied and technically enforced – before a prompt reaches an external or internal model. This transforms many unguarded exit points into a controlled data path.

The protection of sensitive data begins even before it is transmitted to the model. If users enter a prompt containing personal or confidential information or upload a document containing such information, several verification steps should be triggered automatically.

• Detection: Detectors identify sensitive content such as names, email addresses, phone numbers, customer numbers, contract data, login credentials, or other defined data classes. In addition to the prompt itself, the check should also include inserted passages, uploaded documents, code fragments, and other contextual data.

• Masking and tokenization: Personal or confidential information is replaced before transmission. Ideally, a preview directly within the input flow shows which content is being masked. This allows users to verify before submission whether sensitive information has been correctly identified and to adjust their input if necessary. The model receives only a sanitized text containing placeholders. The plaintext values remain within a controlled environment and are managed locally, separate from the model’s access.

• Back-translation: When the response is returned to authorized users, the placeholders can be replaced with the original values without the model having processed those values.

• Policy checking: Additional guardrails check prompts against defined rules, such as regular expressions (Regex), keywords, language restrictions, data classifications, or jailbreak detection.

• Output checking: Model responses can also be checked before display – for example, for unwanted content, sensitive data disclosures, or violations of internal policies.

This ensures that only sanitized inputs leave the organization. Depending on the policy, the content in prompts and context data is masked, blocked, redirected, or released only to specific models. This prevents the leakage of sensitive data right at the source.

Masking and tokenization are not the best solution for every use case. Sometimes data must be processed in plain text without masking, for example, when you want to analyze contracts, customer transactions, or internal documents that contain specific names, numbers, and references.

In such cases, it is crucial to determine where and by whom this sensitive data is processed. Depending on the level of protection required, plaintext data may not be allowed to leave the organization or the EU legal jurisdiction, or – for regulatory reasons – may only be transferred to contractually bound processors within the EU.

This is where model routing comes into play: Sensitive queries are routed only to approved target systems, such as open-weight models on local infrastructure or models operated within Europe. Less critical queries can still be sent to other external models.

The routing thus determines which model is allowed to see which data. The data path remains configured, traceable, and verifiable.

For regulatory authorities, auditors, and internal security teams, the protection of sensitive data must be verifiable in an audit-compliant manner.

A centralized AI access point can capture the audit information required for this purpose, such as:

• Time of the request

• User, role, or tenant reference

• Model used

• Policy applied

• Routing decision

• Identified sensitive data classes

• Blocked or sanitized content

• Policy violations and approval decisions

This provides evidence for data protection, security, and audit documentation – such as for technical and organizational measures, internal controls, risk assessments, and the documentation of processing activities. If the events are also forwarded to existing SIEM, monitoring, or reporting systems, the AI data path remains visible even in real-world operations, allowing proof of regulatory compliance.

However, controlled access to AI offers little protection if numerous uncontrolled access points remain open. Safe AI use is therefore not just a matter of technical control, but also of acceptance.

The approved path must be practical for employees: easily accessible, efficient, and aligned with the workflows they are already familiar with. This includes, for example, a central chat interface, collaborative projects, team features, and clear approval processes for different use cases.

At the same time, governance is needed behind the scenes: single sign-on (SSO), role-based access control (RBAC), tenant management, model approvals, policies for unauthorized AI services, and clear lines of responsibility. Only when security and usability go hand in hand will the controlled channel become the first choice, and unofficial channels will lose their appeal.

A suitable solution for secure AI usage should combine several requirements:

• Centralized Access: All AI requests pass through a defined technical control point.

• PII Detection and Masking: Sensitive data is detected, masked, and managed in a controlled manner before being sent to the model.

• Configurable Guardrails: Policies can be enforced based on data type, role, model, region, and use case.

• Routing by model and region: Requests are specifically routed to appropriate models and operating locations.

• Auditability: Information such as data paths, routing decisions, and policy violations is documented in an audit-proof manner.

• Integration: Events can be integrated into existing security, monitoring, and compliance processes.

• Usability: The solution is easy for all employees to use and can be centrally managed by the organization.

• Trustworthy operation: Providers, infrastructure, certifications, and legal frameworks align with the organization’s own compliance requirements.

It is only the interplay of these factors that makes AI usage a controlled, auditable, and widely applicable process.

Myra AI Workspace implements this approach as a centralized, controlled gateway to AI models. It combines PII detection and reversible tokenization, configurable guardrails, model routing, role-based access control, tenant management, and audit information into a unified data path.

This allows sensitive content to be masked before being transmitted to a curated selection of leading AI models, queries to be routed specifically to approved models, and AI usage to be made traceable for security, data protection, and compliance teams. Events can be integrated with existing operational and security systems so that AI activities do not operate in isolation from the rest of the IT landscape.

Myra operates AI Workspace on its own EU infrastructure – as a German company with a 100% EU shareholder structure, without a U.S. parent company and without CLOUD Act exposure. Myra is BSI-qualified for critical infrastructure operators and certified to ISO 27001 based on BSI IT-Grundschutz, BSI C5 Type 2, PCI DSS, and other standards. Organizations that rely on Myra’s infrastructure include, among others, Deutsche Kreditbank (DKB), 1&1 Versatel, Aleph Alpha, the Munich Security Conference (MSC), and Edeka.

Sign up for a demo and learn how PII detection, masking, model routing, and audit information work together within a controlled AI data path to protect your sensitive data.