The synthetic vs masked sandbox data question comes down to what you are testing and how much realism it needs. Masked data starts as real production records with sensitive fields scrambled, so it keeps the relationships, volume, and messiness of your actual business. Synthetic data is built from scratch to look like production without ever having been production, which makes it faster to generate but weaker on edge cases you did not think to script.
Most Salesforce teams pick one and stick with it, usually by accident rather than design. That is the mistake. The two approaches solve different problems, and treating them as interchangeable is how test cycles end up either too slow or too shallow to catch what matters.
What Masked Data Actually Gives You
Masked production data is your org's real history with the personally identifiable fields swapped out. Names, emails, phone numbers, and financial details get replaced with realistic fakes, but the record count, the field distributions, and the object relationships stay exactly as they were in production. That is the entire value proposition.
If your Service Cloud team needs to test a new case assignment rule, masked data shows you every weird case type, every mislabeled priority, every account with three duplicate contacts that somehow never got merged. Synthetic data will not surprise you like that, because someone had to imagine it into existence, and imagination has limits.
The tradeoff is speed and scope. A full sandbox refresh with masking, using something like MaskEzee, still requires a full copy sandbox to begin with, and that copy carries every record whether you need it or not. For a 40GB org, that is a lot of data to mask, store, and query against just to test one flow.
Where Synthetic Data Wins Outright
Synthetic data earns its keep in three scenarios: new features with no production history, load testing at volumes production has not reached yet, and any environment where legal or compliance flatly refuses to let real customer data leave its region, masked or not.
Take a new Agentforce use case built around a product line launching next quarter. There is no production data for it, masked or otherwise, because nothing has been sold yet. Synthetic generation, the approach behind SproutEzee, builds records that match the shape of what production will eventually look like, using field-level rules rather than copied history.
It is also faster to scale. Need 500,000 opportunity records to stress-test a trigger before Black Friday volume hits? Generating that synthetically takes minutes. Sourcing it from a masked full copy means either you already have that volume in production, which is rare, or you are stuck extrapolating from what you have.
The Realism Gap Nobody Talks About
Here is the part vendors skip in their pitch decks. Synthetic data is only as good as the rules behind it, and most teams underinvest in those rules. A generator that produces evenly distributed, well-formed records will pass every test you write and still miss the malformed address, the null-but-required field from a 2019 integration bug, or the account hierarchy that is four levels deep because someone merged two orgs in 2021.
Masked data does not have this problem, because it did not need rules to invent the mess. It already has the mess. This is the single biggest argument for keeping at least one masked full copy sandbox in rotation, even if your day-to-day testing runs on synthetic data everywhere else.
I would treat any claim that synthetic data alone can replace masked production data with suspicion. It can replace it for volume and speed. It cannot replace it for the accumulated weirdness that only comes from years of real users doing unexpected things in your org.
Running Both Without Doubling Your Overhead
The teams that get this right do not choose one approach, they sequence them by sandbox purpose. A partial or full copy sandbox, refreshed on a slower cadence and masked before anyone touches it, becomes the source of truth for UAT and regression testing where realism matters most.
Developer and scratch orgs, refreshed constantly and torn down just as fast, run on synthetic data generated to match current schema. Nobody needs real customer records in an environment that gets deleted in three days. Speed matters more than realism there, and synthetic data delivers speed without the compliance overhead of masking anything.
This is also where cost stops being abstract. Masking a full copy sandbox takes processing time and storage, both of which scale with data volume. Generating synthetic data for a scratch org takes a fraction of that, because you are building exactly the records the test needs, no more.
A Decision Framework That Actually Holds Up
Instead of picking based on habit, match the sandbox purpose to the data source. The table below is the version I would hand to an IT Director trying to standardize this across a 2,000-user Salesforce instance.
| Sandbox Purpose | Recommended Data | Why |
|---|---|---|
| UAT / regression testing | Masked full or partial copy | Needs real relationships and edge cases |
| New feature with no production history | Synthetic | No masked data exists to draw from |
| Load and performance testing | Synthetic at scale | Faster to generate high volume than to source it |
| Developer / scratch orgs | Synthetic, refreshed often | Speed matters more than fidelity here |
| Compliance-restricted regions | Synthetic | No real customer data allowed to leave region |
| Agentforce training and testing | Both, layered | Synthetic for coverage gaps, masked for real conversation patterns |
Agentforce is worth calling out separately, because it is the one case where getting this wrong is expensive in a way regression bugs are not. An agent trained or tested only against synthetic conversation data will handle textbook cases fine and stumble on the phrasing, sentiment, and edge-case requests that only show up in real, masked customer interactions.
What This Means for Your Refresh Cycle
Once you separate data strategy from refresh cadence, the two stop fighting each other. Your masked full copy can refresh monthly or quarterly, since it exists for stability and realism, not speed. Your synthetic-data scratch orgs can refresh daily without anyone flinching, because rebuilding synthetic data costs almost nothing compared to running a masking job against a multi-gigabyte copy.
The practical result is fewer stalled test cycles and fewer late-stage surprises in UAT, because each environment is doing the job it is actually suited for. Teams that run everything through one sandbox type, whether that is masked-only or synthetic-only, tend to discover the gap during a release weekend rather than during planning. That is the expensive way to learn this lesson.
Frequently Asked Questions
Is synthetic data safe for compliance without masking?
Yes, as long as the synthetic data was never derived from real customer records. It carries no personal information because it was generated from rules, not copied from production. This makes it the preferred option for regions or industries with strict data residency requirements.
Can synthetic data fully replace a masked full copy sandbox?
Not for UAT and regression testing, because synthetic data only reflects the edge cases someone thought to build into the generation rules. Masked data retains the real relationships and irregularities that accumulate in production over years. Most teams need both, applied to different sandbox types.
How much faster is synthetic data generation compared to masking a full copy?
Generating synthetic records for a scratch org typically takes minutes regardless of volume, since the tool builds exactly the records requested. Masking a full copy sandbox depends on how much production data exists, and can take hours for larger orgs because every sensitive field across every object needs processing.
Does synthetic data work for testing Agentforce and AI features?
Synthetic data helps cover volume and edge-case scenarios that do not yet exist in production, which is useful for new AI use cases. It falls short on capturing real conversational patterns and customer phrasing, so pairing it with masked production data gives more reliable agent testing.
What is the biggest risk of relying only on synthetic data?
The biggest risk is a realism gap: synthetic generators only produce the patterns their rules account for, missing the malformed records and unusual account structures that build up naturally in production. Tests can pass consistently against synthetic data while still failing against real-world conditions in UAT or after release.