Synthetic Data Generation

Synthetic data generation is rapidly becoming a foundational element of modern AI development, advanced analytics, and privacy-driven digital transformation. It enables organizations to create realistic, statistically accurate datasets that mirror real-world information—without exposing genuine customer or corporate data. This approach supports secure experimentation, machine learning training, and model validation while remaining compliant with established privacy regulations such as GDPR, HIPAA, and CCPA. According to a recent Gartner report, nearly half of global executives have increased their AI spending, underscoring the expanding need for responsible and secure data use. Additional guidance from the NIST AI Risk Management Framework highlights synthetic data’s role in reducing bias and supporting safer model development. Capabilities such as dynamic data masking further enhance an organization’s ability to safeguard sensitive information throughout the process.

DataSunrise positions synthetic data as a natural evolution of data protection—complementing existing methods including data masking, encryption, and database activity monitoring. This functionality empowers organizations to generate fully anonymized, production-quality datasets that retain real data’s structure, relationships, and statistical patterns. As a result, teams can conduct testing, analytics, and development in safe, controlled environments without violating privacy or regulatory requirements. Synthetic datasets support secure collaboration, accelerate innovation, and ensure compliance across every stage of the AI lifecycle.

When paired with automation and intelligent policy controls, synthetic data not only enhances data protection and regulatory compliance but also boosts scalability, operational agility, and continuity. It enables enterprises to adopt AI and analytics within secure, ethically governed ecosystems—unlocking innovation while maintaining trust and regulatory alignment.

What Is Synthetic Data?

Synthetic data refers to artificially created information that reflects the structure and statistical behavior of real datasets without retaining actual values. It maintains formats, relationships, and distributions, allowing teams to develop, test, and analyze securely. Because no genuine records are used, synthetic datasets eliminate privacy risks while remaining highly effective for AI modeling, system validation, and compliance efforts.

When to Use Synthetic Data vs. Masking

Static or dynamic masking is great when you need to retain the structure and logic of production data—but still want a reference to real values. However, masking can’t be shared externally if the source schema or metadata creates re-identification risk.

Synthetic data is better when:

• You need to simulate large datasets with no connection to real individuals
• Compliance requires zero exposure to production values
• You’re working with unstructured logs or training LLMs

DataSunrise Synthetic Data Use Cases

What Makes DataSunrise Synthetic Data Different?

While many platforms offer artificial data generation, few integrate it directly into enterprise-grade security and compliance pipelines. DataSunrise Synthetic Data tools are tightly coupled with masking, audit, and policy enforcement features—making them ideal for real-world usage in regulated environments.

• Integrated masking fallback: Seamlessly switch between masking and generation based on access context or schema type.
• Policy-aware generation: Define generation rules that align with existing compliance filters and sensitive data tags.
• Scheduled workflows: Automate synthetic dataset creation across environments, applications, and CI/CD pipelines.
• Audit logging: Track every generation task for full traceability and audit-readiness.

Whether you’re testing internal apps or training AI models, DataSunrise Synthetic Data gives teams the flexibility to simulate production-like workloads—without risking production data.

How to Configure Synthetic Data Generation in DataSunrise

Step 1: Set General Parameters

Navigate to Configuration → Periodic Tasks and create a new task. Select “Synthetic Data Generation” as the type, and name the task accordingly.

Step 2: Select Database Instance

Choose your target instance. Below, PostgreSQL is selected as the database engine.

Step 3: Define Target Tables and Columns

Select the schema and tables where synthetic data will be injected. Choose specific columns, enable “Empty Table” if needed, and configure error handling behavior.

Step 4: Use Built-in or Custom Generators

Choose from built-in value generators (names, emails, numbers, dates) or define custom logic via Configuration → Generators. This is useful for matching domain-specific patterns, like simulating patient IDs or tax codes.

Step 5: Save, Schedule, and Run

Once saved, the task appears in your job list. You can run it on demand or schedule periodic runs for continuous data refresh.

Free Tools and Libraries for Synthetic Data

DataSunrise provides comprehensive support for synthetic generation with masking, audit, and compliance controls. But developers and data scientists also benefit from free alternatives when learning or prototyping.

SDV (Synthetic Data Vault)

SDV is an open-source Python framework that uses statistical models and GANs to generate synthetic tabular datasets. It supports relational and multi-table structures.

pip install sdv

from sdv.datasets.demo import download_demo
from sdv.single_table import GaussianCopulaSynthesizer

real_data, metadata = download_demo(modality='single_table', dataset_name='fake_hotel_guests')
synthesizer = GaussianCopulaSynthesizer(metadata)
synthesizer.fit(real_data)
synthetic_data = synthesizer.sample(num_rows=500)
print(synthetic_data.head())

CTGAN

A GAN-based model tailored for tabular data, CTGAN works well with imbalanced datasets and mixed column types. See our earlier AI data generation article for sample code.

Mockaroo

Mockaroo is a web tool for generating mock datasets in CSV, JSON, SQL, and other formats. It’s ideal for quick prototypes and supports custom field schemas. Free usage is limited to 1,000 rows per session.

from scipy.stats import ks_2samp

# Compare real vs synthetic column distributions
ks_stat, p_value = ks_2samp(real_data["age"], synthetic_data["age"])
if p_value > 0.05:
    print("Synthetic 'age' distribution matches real data")
else:
    print("Significant difference detected")
  

import pandas as pd

real_corr = real_data.corr(numeric_only=True)
synth_corr = synthetic_data.corr(numeric_only=True)
diff = (real_corr - synth_corr).abs()
print(diff.head())
  

Best Practices for Generated Data

1. Match data formats to downstream expectations.
   Ensure synthetic values follow the same patterns—data types, ranges, formats, and constraints—so that applications, pipelines, and analytics tools work without modification.
2. Preserve table relationships where needed.
   Maintain key dependencies such as primary/foreign keys, hierarchies, and lookup tables to keep workflows, joins, and business logic functioning correctly.
3. Document generation rules for reproducibility.
   Track the logic, seed values, and transformation rules used to create the dataset to support consistent regeneration, auditing, and troubleshooting.
4. Run sanity checks to validate logic.
   Verify that distributions, ranges, and behavioral patterns look realistic—catching anomalies like out-of-range values, empty fields, or broken relationships early.
5. Use masking or exclusions to avoid any overlap with real data.
   Confirm that synthetic values cannot be traced back to actual customer information, reducing re-identification risk and strengthening compliance.

Quick Comparison

When Synthetic Data Isn’t Enough: Considerations and Controls

While synthetically generated data offers strong privacy guarantees and flexibility, it’s not a universal replacement for real data or enterprise masking workflows. Certain scenarios—such as referential integrity testing, deterministic joins, or longitudinal analysis—may still require controlled access to masked or pseudonymized datasets instead.

To ensure generated data serves your goals effectively, consider these guardrails:

• Use case alignment: For model validation, use fully synthetic data. For integration or UI testing, masked production clones may be more accurate.
• Governance documentation: Track which fields were synthetically generated, which were preserved, and which tools or logic were used.
• Sampling vs. simulation: Don’t confuse random sampling of real data with synthetic generation. Only the latter breaks linkage to identifiable subjects.
• Audit readiness: Maintain logs of generation tasks, retention timelines, and access controls—especially if synthetic data enters test pipelines shared with vendors or contractors.

DataSunrise helps bridge these decisions with automation, masking fallback options, and full visibility across data types and environments. The result is safer, smarter, and faster data workflows—without compliance trade-offs.

Key Takeaways for Using Synthetic Data Effectively

• Choose synthetic data when compliance requires zero exposure to real records, or when sharing datasets externally.
• Combine synthetic generation with masking for hybrid scenarios—keeping relational integrity where needed while replacing high-risk fields entirely.
• Document generation rules, retention policies, and access controls to maintain governance and audit readiness.
• Test synthetic datasets against real-world workflows to confirm they meet performance, accuracy, and compatibility requirements.
• Automate generation tasks through scheduling and integration with CI/CD pipelines for consistent, repeatable results.

The Future of Synthetic Data Generation

Synthetic data is rapidly evolving from a testing utility into a core component of enterprise data strategy. As organizations strive to innovate responsibly, next-generation synthetic data platforms will combine AI-driven generation, data quality validation, and automated compliance controls to create realistic yet fully anonymized datasets at scale. These systems will not only reproduce statistical accuracy and structural integrity but will also dynamically adapt to changing data models, privacy requirements, and evolving regulatory landscapes.

Future solutions will offer seamless integration with complementary technologies such as data masking, database activity monitoring, and sensitive data discovery. This interoperability will allow organizations to fluidly transition between real, masked, and synthetic datasets based on context—enabling safe analytics, model training, and external collaboration without exposing regulated information. Over time, this adaptive data ecosystem will make privacy-preserving innovation a default, not an exception. Capabilities like compliance automation will further ensure that synthetic datasets consistently meet regulatory and organizational requirements.

For highly regulated sectors such as finance, healthcare, and government, synthetic data will redefine the balance between compliance and innovation. Enterprises will be able to accelerate AI adoption, collaborate securely with third parties, and maintain verifiable proof that no authentic customer data ever left controlled environments. Ultimately, the future of synthetic data generation lies in intelligent automation and continuous compliance, turning privacy into an enabler of progress rather than a constraint.

Conclusion

Synthetic data has become a key component of modern privacy-first data management strategies, providing a secure and regulation-friendly alternative to using real production datasets for development, testing, analytics, and machine learning. By mirroring the structure, statistical characteristics, and relationships of real-world data—without retaining any personally identifiable or proprietary information—it enables organizations to innovate, collaborate, and analyze safely. This privacy-by-design approach minimizes compliance risks, reduces legal liabilities, and supports ethical AI deployment across sectors such as finance, healthcare, telecommunications, and the public domain.

DataSunrise incorporates synthetic data generation within its comprehensive data security and governance platform. Through automated policy enforcement, advanced masking logic, and detailed audit trails, the platform ensures that synthetic datasets comply with both internal governance frameworks and external standards such as GDPR, HIPAA, SOX, and PCI DSS. This allows enterprises to produce realistic yet fully anonymized data suitable for AI training, software testing, and third-party collaboration—without risking exposure of sensitive or confidential content.

When integrated with solutions like Database Activity Monitoring (DAM), data discovery, and dynamic masking, synthetic data becomes a powerful enabler of secure digital transformation. It allows organizations to accelerate innovation while maintaining transparency and compliance, supporting responsible experimentation and continuous improvement. As privacy regulations evolve and AI technologies advance, synthetic data will remain a foundational element of secure, ethical, and scalable data-driven innovation.