When it comes to developing and fine-tuning artificial intelligence (AI) and machine learning (ML) models, reliable results don't happen by chance – they're built on a solid foundation of data. AI/ML models and customer use cases are only as good as the data they’re trained on.
However, using sensitive or personally identifiable information can carry serious privacy, security and compliance risks. With DataMasque, you can mask and generate statistically equivalent data that is synthetically identical to your customers’ data, preserving privacy while maintaining data utility for AI/ML model training, testing and development.
DataMasque enables enterprises to fine-tune AI models using de-identified customer data. Our data masking technology creates synthetically identical customer data that preserves integrity while ensuring compliance.
Unlike traditional masking or fully synthetic datasets that often lack the full variability and edge cases present in real-world data, our approach maintains realistic detail without compromising sensitive information.
Whether you're training a proprietary model or tweaking an off-the-shelf one, DataMasque consistently masks your data, enabling you to leverage all of your data sources.
DataMasque integrates seamlessly with your BYOM strategy, providing consistently de-identified training data ready for fine-tuning.
Don't let regulatory compliance slow down your AI initiatives. DataMasque helps you adhere to HIPAA, GDPR and PCI DSS privacy regulations, allowing you to focus on innovation.
DataMasque’s masking software provides robust data protection for enterprises by removing sensitive PII, PHI, and PCI data, meeting all necessary compliance requirements.
By de-identifying sensitive data while maintaining the utility of that data before it enters an AI or ML workflow.
DataMasque replaces personal and sensitive information with ‘synthetically identical’ equivalents while preserving structure, relationships and behavior.
This allows teams to work with production-like datasets for AI training, experimentation and fine-tuning, without using real customer data.
DataMasque de-identifies sensitive information before it is used for AI.
DataMasque can discover and de-identify sensitive data in structured, semi-structured and unstructured datasets, while ensuring that the structure and context required for analytics and AI workloads remain intact.
DataMasque safeguards sensitive information while keeping your data functional.
AI models derive insights from patterns in the data, including relationships, distributions and edge cases.
If training data does not reflect real-world behavior, models may perform poorly in production.
DataMasque maintains this important data integrity and realism, while masking sensitive values, helping teams train and test models on realistic datasets.
DataMasque applies consistent masking across data stored in databases, files and cloud or on-prem environments.
By preserving referential integrity through primary and unique key support, masked datasets remain suitable for downstream analytics and AI workflows.
Yes.
DataMasque uses deterministic masking to ensure the same sensitive value is replaced consistently across databases, tables, files and unstructured data.
This preserves referential integrity and context across datasets, which is important for AI and ML workflows involving customer histories, event sequences, logs and agent workflows.
Yes.
DataMasque irreversibly masks sensitive values before data enters any AI pipeline, preventing the risk of embedding real PII in model weights, or inadvertently exposing it through model outputs.
Synthetic data can be useful in some scenarios, but it may not capture all the relationships, nuances and edge cases found in real production data.
DataMasque masks real datasets instead, preserving these characteristics so AI and ML models can be trained and evaluated on data that reflects actual behavior while keeping sensitive information protected.
Yes.
DataMasque supports masking sensitive data in unstructured text such as logs, documents and text fields.
