Synthetic Data vs Data Masking: Benefits & Challenges in 2024

Data breaches are rampant today, with the average cost of a breach now over $4 million according to IBM. As cyberattacks and data regulations proliferate, effectively protecting sensitive information is imperative for organizations.

If you‘re evaluating data protection strategies, you‘re likely exploring synthetic data and data masking. Both are viable options, but have important nuances. In this comprehensive guide, I‘ll demystify these technologies so you can determine the best approach for your needs.

Diving Deeper on Synthetic Data Generation

Synthetic data provides an appealing approach for reducing privacy risks – since no actual data is used, it avoids many legal and ethical pitfalls. But how is synthetic data actually created?

Popular methods include using generative adversarial networks (GANs), a deep learning technique where two neural networks compete to generate increasingly realistic synthetic data. Variational autoencoders (VAEs) are another option, generating data by learning the statistical distributions of real data.

Once trained, these AI models can rapidly generate as much synthetic data as needed. However, the big challenge is properly evaluating how closely the synthetic data mimics the nuances and complexity of real-world data. Using metrics like statistical distributions or specific utility metrics relevant to your use case is advised.

I‘ll explore synthetic data quality further in the challenges section. First, let‘s look at how data masking works.

Demystifying Data Masking Techniques

Data masking involves transforming a copy of real data to conceal sensitive information. Some common masking techniques include:

• Substitution – Replacing data with fake values, like names or IDs.
• Shuffling – Scrambling parts of data, like swapping age and ZIP code fields.
• Number variance – Alter numbers within a specific range, like adding 10% to salaries.
• Encryption – Encoding data so only authorized parties can access the original values.
• Aggregation – Combining or grouping data fields, like generalizing date of birth to only show the year.

The appropriate technique depends on your specific data protection needs. For example, encryption ensures higher security but makes data less usable.

Key Benefits of Synthetic Data Generation

• No real data exposure – eliminates many privacy and compliance risks.
• Customizable – tailor synthetic data properties as needed.
• Can improve model accuracy with robust, diverse training data.
• Scalable – generate large datasets on demand.
• Future-proof – avoid rework as new PII fields emerge.

According to Gartner, 60% of CDOs will use AI-generated synthetic data by 2024. Top use cases include AI training, testing data pipelines, and de-identifying data for customer service teams.

Top Benefits of Data Masking

• Retains original data relationships and utility – crucial for many analytics use cases.
• Reversible methods allow recovering source data.
• Familiar formats integrate smoothly across systems.
• Complies with data protection regulations like GDPR while enabling analysis.
• Controlled, targeted protection for specific high-risk fields.

Per IBM, over 80% of organizations use data masking to reduce breach impacts and comply with privacy rules. Top applications include masking test data and protecting PII for customer service teams.

Key Challenges and Limitations to Consider

Synthetic Data Challenges

• Assessing quality – statistical similarity doesn‘t guarantee suitability or usability. Need to validate with metrics relevant to your specific use case.
• May not mimic all nuances and intricacies of complex real-world data.
• Integration with existing analytics pipelines may require additional work.

According to Gartner, through 2022 over 85% of AI projects that leverage synthetic data will fail due to deficiencies in the synthetic data used for training those models. Rigorous evaluation is key.

Data Masking Challenges

• Irreversible methods like encryption permanently obscure data.
• Heavy masking can reduce data utility significantly.
• Doesn‘t future-proof data – new sensitive fields may emerge.
• Susceptible to re-identification attacks – reconstruction of masked data using correlation.

Approaches like differential privacy help mask data while limiting utility loss. Proper implementation is important – over 50% of organizations have experienced data security incidents due to misconfigured data masking per Gartner.

Navigating the Synthetic vs Masked Data Decision

So when should you opt for synthetic data or data masking? Here are some guidelines:

Consider synthetic data when:

• You need to share or distribute data without disclosing real PII.
• Training machine learning models with robust, balanced data.
• Your analytics rely heavily on mimicking patterns and distributions of real data.

Data masking is preferable for:

• Use cases where retaining exact data relationships and utility is critical.
• Compliance with data protection regulations.
• Limiting exposure for specific high-risk fields, while maintaining overall usability.
• Reversible access to real data is occasionally needed.

Neither approach is bulletproof – both come with challenges around proper implementation and evaluation. Think critically about your specific risks, objectives and analytics needs when deciding between synthetic or masked data.

Tips for Success With Synthetic and Masked Data

Here are best practices to ensure you get the most value from synthetic and masked data:

• Validate quality using statistical analysis, utility metrics, audits, and spot checks on masked data.
• Isolate sensitive data first before applying masking across entire datasets when possible.
• Implement role-based access controls and auditing to ensure proper use of any available real data.
• Evaluate new data types frequently – sensitive fields can emerge over time as attacks evolve.
• Use hybrid approaches combining masking and synthetic data tailored to your case.
• Work with experienced vendors who understand both the nuances of data protection and your analytics objectives.

The Bottom Line

Protecting sensitive data while enabling business insights requires careful evaluation of risks, regulations and analytics requirements.

Both synthetic data and data masking offer benefits, but also have pitfalls around proper implementation and evaluation. Used judiciously, they help balance privacy and usability for critical analytics applications.

Understanding the core distinctions allows you to craft a tailored data protection strategy aligned to your organization‘s specific needs and priorities. With the right solution, you can uphold data privacy while unleashing the full value of data for your business.