Consequences of using XGBoost regressor for small dataset(< 500 rows)
The one other problem you have because of this is you are stuck with just this solution and this may not be the best one. Yes, you can use regularization but still, what if some other better solution exists than this. Since, in boosted trees, you aim is to  reduce your errors, each iteration of boosting tries to overfit on the examples which the last iteration predicted wrong.
But there is a way to confirm if this is the best solution. Do cross-validation. Or try LightGBM and CatBoost also, since, they are also good libraries for the same purpose. And finally, you combine all three of them to come with an ensemble that will you the best of the three worlds.