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Best Practices for Using Customer Data Securely in Non-Production Environments

The divide between production and non-production environments is critical for ensuring the integrity, security, and compliance of sensitive data. However, there are situations where production data is used in non-production environments to facilitate testing, development, or troubleshooting. This practice carries distinct risks but can also be managed effectively with the right strategies.  

Maya Data Privacy is a GDPR & NIS Compliant company, certified under ISO 27001:2022, which sets the Gold Standard for secure, scalable, and privacy-first data management — making it the Ideal partner for enterprises handling sensitive data in ERP systems, AI projects, and test environments. 

Different Uses of Production Data in Non-Production Environments 

1. Testing and Debugging Complex Systems 

In development workflows, production data may be required to identify and resolve bugs or errors that cannot be replicated using synthetic or mock data. By leveraging real-world data, developers can simulate actual scenarios and ensure the robustness of the system under realistic conditions. 

2. Performance and Stress Testing 

Production data is often used in non-production environments to understand system performance under realistic workloads. This allows organisations to predict how applications will handle traffic spikes, prevent downtime, and ensure scalability. 

3. AI and Machine Learning Model Development 

Machine learning models improve significantly when trained on production data containing real-world patterns. Non-production environments are used to iterate and refine algorithms before deploying them live. Using actual data ensures predictive accuracy while avoiding skewed insights from synthetic data. 

4. ERP System Upgrades and Configuration Validation 

Enterprise Resource Planning (ERP) systems can be highly complex, and upgrades often demand testing with authentic data structures to validate configurations and confirm integration paths. Production data in sandbox environments helps avoid errors during deployment. 

5. User Acceptance Testing (UAT) 

Production data in non-production environments may be requested by stakeholders for User Acceptance Testing. This ensures final validation of workflows and interfaces in a controlled environment before applying changes in production. 

6. Troubleshooting Issues Across Platforms 

Production data is occasionally mirrored into testing environments to identify the root cause of errors or issues in existing workflows or systems. This practice provides a detailed view of live system challenges without compromising production stability. 

Risks of Using Production Data in Non-Production Environments 

Using production data in non-production environments can introduce severe risks if not appropriately managed: 

• Exposure to Data Breaches: Non-production environments typically lack the security controls of production environments. If production data is replicated or accessed without safeguards, it exposes sensitive customer or business data to potential breaches.

According to a Gartner survey, "41% of organisations experienced an Al 

privacy breach or security incident. Of those reported breaches or incidents, 

60% faced data compromises by an internal party". 

• Privacy Violations and Compliance Failures: Regulatory frameworks like GDPR, SOC 2, and ISO 27001:2022 demand strict controls over sensitive data. Using live data in environments outside production without proper anonymization or safeguards could result in non-compliance and legal fines. 

• Insider Threats: Development or testing teams often have broad access to non-production environments. Without strict access controls, production data in these settings could be misused or mishandled. 

 A global survey by One Identity (via Dimensional Research) found that 92% of IT security professionals reported employees attempting to access information irrelevant to their jobs; additionally, 66% of respondents admitted they themselves had accessed such data unnecessarily.

GlobeNewswire 

• Data Integrity Risks: The use of production data in testing environments may lead to unexpected corruption or inadvertent changes that ripple back into the production dataset. 

Mitigating Risks of Using Customer Data in Non-Production Systems with Maya Data Privacy’s Product Suite 

To address the challenges of protecting customer data in non-production environments, Maya Data Privacy’s product suite offers specialized tools—AppSafe, FileSafe, and AISafe—to mitigate risks, ensure compliance, and enable secure data usage without compromising productivity or innovation. These solutions provide anonymization and protection capabilities for various data use cases, enabling organizations to adhere to privacy regulations like GDPR, CCPA, SOC 2, and HIPAA. Below is an overview of how Maya's suite can effectively mitigate risks. 

 AppSafe 

AppSafe is designed to protect privacy by anonymizing production data copies. It enables organizations to utilize sensitive customer data for non-production purposes without exposing the original data, thereby reducing risks of data breaches, privacy violations, and regulatory non-compliance. 

Key Features and Benefits 

• Data Anonymization: AppSafe ensures production data copies are consistently anonymized across systems (including both SAP and non-SAP environments). This removes identifiers, such as Names, Org Names, Material, Addresses, Telephone numbers, Email address, IBAN, Account numbers, URL’s, IP Addresses, SSN’s, Passport Numbers, Tax ID’s etc.  while retaining useful anonymised data for testing, development, and training. 

  
  

european-data-governance-act.com 

“The principles of data protection should not apply to anonymous information, namely information which does not relate to an identified or identifiable natural person…” 

• Safe Data Usage: Enables teams to use anonymised production data for scenarios where personal information is prohibited or unnecessary—for example, test data migration and AI model training. 

• Centralized Test Data Management: AppSafe helps manage and refresh test data effectively, ensuring the anonymization process is consistent and scalable across environments. 

• Data Security: Protects against data breaches by ensuring sensitive customer information is anonymized before use in less secure environments. 

• Ensures compliance with regulations by anonymizing personal data. 

• Prevents insider threats by providing anonymized data copies accessible to broader teams while ensuring sensitive details remain protected. 

FileSafe 

FileSafe extends data protection to non-production environments for unstructured data such as Tabular files (.xlsx, .csv), Images (.tif, .png, .jpeg), and documents (.docx, .srt, .log, .pdf). This is critical for protecting sensitive customer information across diverse formats used in testing and analysis. 

Key Features and Benefits: 

• Anonymization: Automatically anonymizes personal and sensitive data across various file types, including spreadsheets, scanned documents, forms, and images. 

• Secure Document Sharing: Ensures sensitive data is not accidentally exposed during file-sharing or collaboration in non-production systems. 

  
  

 AISafe 

AISafe provides advanced anonymization controls for enterprise environments by integrating real-time data protection during AI interactions. With the proliferation of AI tools and large language models (LLMs), AISafe offers a unique solution to ensure customer privacy when leveraging AI-driven insights. 

Key Features and Benefits: 

• Real-Time Anonymization: Functions as an AI chatbot that automatically detects sensitive data during user interactions and replaces it with anonymized placeholders before reaching external AI tools. 

• Enterprise-Level Protection: Facilitates the secure use of AI LLMs while ensuring compliance with privacy regulations and removing sensitive data from AI workflows. 

• AI Training Data Compliance: Ensures training data used for AI models is anonymized, preventing regulatory violations or inadvertent data breaches when employing AI tools. 

• Seamless User Experience: Enables organizations to leverage the power of AI while embedding privacy protection seamlessly into workflows. 

Realising Enterprise Success with Maya 

Managing production data in non-production environments is an increasingly common requirement, but without proper safeguards, it can introduce risks to security, compliance, and privacy. Maya Data Privacy Limited is the trusted partner for enterprises looking to innovate in ERP systems, AI projects, or test environments while adhering to stringent global standards like ISO 27001:2022, GDPR and NIS. 

With Maya’s secure, scalable, and privacy-first approach, organizations gain the confidence to tackle complex problems without compromising what matters most — their sensitive data and compliance reputation.  

“At Maya Data Privacy Limited we turn personal and sensitive data into strategic advantage.” 

Learn More: https://www.mayadataprivacy.com 

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