I have n dimensional data and I want to check integrity, can I downgrade to 2 dimensional feature space via PCA and do so?
By good I assume you're trying to see if the features are different enough? It depends on your data set actually. As smallchess has pointed out, PCA is commonly used and may work with your dataset. Try it out and see if the different classes are distinct? If not, you can try using higher dimensional visualisation tools. 

Besides, with PCA you're reducing the dimensions to new features that may or may not be intuitive in meaning. That makes interpretation more complicated. If you're looking to visualise data, why not go with higher dimension visualisations? In case you're not aware of the different available tools..
Here is a question with great answers on Quora about higher dimension visualisation

Of the tools recommended, Andrews plot and parallel coordinates plots are very popular. I would suggest using these tools instead of using PCA and subsequently plotting for the purpose you're describing. If you have no need to visualise the data, sometimes you can just get a statistical summary/description of the datasets (mean, variance, min, max, etc). Below is an example of an Andrews Plot. You can plot the various classes into different colours to see if there's any discernible differences between the classes. 


If that's not what you're looking for, to visualise the data to gauge the representation, just proceed to try out and build a model with the features and see if you get a good score?

Edit: Here are some shortcomings of PCA..if there's information that's misrepresented, do let me know!
One thing to note is that with PCA, its transformation is orthogonal. Sometimes your data requires non-orthogonal representation. This is very dependent on how your data looks like. To give an example, take a look at the diagram below.


In the above example, PCA fails to find PCs that maximises the variance due to orthogonality. Independent Factor Analysis may work better. Also, if you're planning to use the transformed data to build your model upon, there may be limitations with the linearly transformed data. Sometimes we need non-linear PCs. See the below example from the scikit webpage.



In such a case, a kernel PCA (utilizing a kernel trick similar in SVMs) may work better by getting a projection to get better linear distinction.