Every digital process needs to consume some data in order to work properly. It is very common for applications to use some external data in their processes, getting them by sources such as external APIs. Therefore, trusting the received data becomes crucial in such scenarios, considering that if the data are not self-produced by the consumer, the trust in the external data source, or in the data that the source produces, can not always be taken for granted. The most used approach to generate trust in the external source is based on authenticated data structures, that are able to authenticate the source when queried through the generation of proofs. Such proofs are useful to assess authenticity or integrity, however, an external user could also be interested in verifying the data history and its consistency. This problem seems to be unaddressed by current literature, which proposes some approaches aimed at executing audits by internal actors with prior knowledge about the data structures. In this paper, we address the scenario of an external auditor with no data knowledge that wants to verify the data history consistency. We analyze the terminology and the current state of the art of the auditable data structures, then we will propose a general framework to support external audits from both internal and external users.