The objective of this thesis is to study methods for the flexible and secure storage of sensitive data in an unaltered cloud. While current cloud storage providers make guarantees on the availability and security of data once it enters their domain, clients are not given any options for customization. All availability and security measures, along with any resulting performance hits, are applied to all requests, regardless of the data's sensitivity or client's wishes. In addition, once a client's data enters the cloud, it becomes vulnerable to different types of attacks. Other cloud users may access or disrupt the availability of their peers' data, and cloud providers cannot protect from themselves in the event of a malicious administrator or government directive. Current solutions use combinations of known encoding schemes and encryption techniques to provide confidentiality from peers and sometimes the cloud service provider, but its an all-or-nothing model. A client either uses the security methods of their system, or does not, regardless of whether the client's data needs more or less protection and availability. Our approach, referred to as the Data Storage Facilitating Service (DSFS), involves providing a basic set of proven protection schemes with configurable parameters that encode input data into a number of fragments and intelligently scatters them across the target cloud. A client may choose the encoding scheme most appropriate for the sensitivity of their data. If none of the supported schemes are sufficient for the client's needs or the client has their own custom encoding, DSFS can accept already encoded fragments and perform secure placement. Evaluation of our prototype service demonstrates clear trade-offs in performance between the different levels of security encoding provides, allowing clients to choose how much the importance of their data is worth. This amount of flexibility is unique to DSFS and turns it into more of a secure storage facilitator that can help clients as much or as little as required. We also see a significant effect on overhead from the service's location relative to its cloud when we compare performances of our own setup with a commercial cloud service.