Regulations and societal expectations have recently emphasized the need to mediate access to valuable databases, even by insiders. At one end of a spectrum is the approach of restricting access to information; at the other is information accountability. The focus of this work is on effecting information accountability of data stored in relational databases. One way to ensure appropriate use and thus end-to-end accountability of such information is through continuous assurance technology, via tamper detection in databases built upon cryptographic hashing. We show how to achieve information accountability by developing and refining the necessary approaches and ideas to support accountability in high-performance databases. These concepts include the design of a reference architecture for information accountability and several of its variants, the development of a sequence of successively more sophisticated forensic analysis algorithms and their forensic cost model, and a systematic formulation of forensic analysis for determining when the tampering occurred and what data were tampered with. We derive a lower bound for the forensic cost and prove that some of the algorithms are optimal under certain circumstances. We introduce a comprehensive taxonomy of the types of possible corruption events, along with an associated forensic analysis protocol that consolidates all extant forensic algorithms and the corresponding type(s) of corruption events they detect. Finally, we show how our information accountability solution can be used for databases residing in the cloud. In order to evaluate our ideas we design and implement an integrated tamper detection and forensic analysis system named DRAGOON. This work shows that information accountability is a viable alternative to information restriction for ensuring the correct storage, use, and maintenance of high-performance relational databases.