How long does a piece of string need to be?
It depends on what you need to do with it.
How high do you need your sensitivity and specificity to be?
It depends on what you need to do with your model. You need to do a cost benefit analysis based on your planned deployment environment to get a definitive answer.
It really is application specific. If there are application relevant standards to compare against then do so. If they have carried out a benefit risk analysis then you may be able to dovetail into that. If you perform better in both sensitivity and specificity then it is win win. More common is that one will be higher and one lower, then you really need to get into the meat of how the model will be used.
What are the costs associated with positive predictions?
What are the risks associated with actions undertaken (or inaction) in response positive predictions?
What are the costs associated with negative predictions?
What are the risks associated with actions undertaken (or inaction) in response to negative predictions?
What context will it be used?
Will it be completely randomly sampled from all possible targets?
Will there be some form of prefiltering that correlates with your predictive model? (see Is sensitivity or specificity a function of prevalence? for a relevant discussion)
Will there be follow up confirmatory testing or will affirmative action result directly from it?
Note on Gold Standard
The term 'gold standard' has a very specific meaning in classification/discrimination. It is what you used to define ground truth for the classification. The context of your use of the term suggests you mean an ideal target value for sensitivity and specificity.