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Friday, June 10, 2011


When we look at protecting data, irrespective of whether that protection is achieved on a desktop, on a network drive, on a remote laptop, or in a cloud, we need to remember certain things about data and human beings. Data are most often information that needs to be used; it may be unfinished and require to be passed through several hands for collaboration for completion, or it could be a finished document needing to be sent onto many organizations and then passed through multiple users to inform. It may also be part of an elaborate workflow, across multiple document management systems, working on platforms that cross the desktop and cloud domain. Ultimately, that information may end up in storage in a data center on a third-party server within the cloud, but even then it is likely to be re-used from time to time.

This means that the idea of “static” data is not entirely true and it is much better (certainly in terms of securing that data) to think of it as highly fluid, but intermittently static.

What are the implications of this? If we think of data as being an “entity” that is not restricted by network barriers and that is opened by multiple users in a distributed manner, then we should start to envision that a successful protection model will be based on that protection policy being an intrinsic part of that entity. If the protection becomes inherent in the data object, in much the same way that perhaps a font type is inherent in a document (although in the case of security in a much more persistent manner), then it is much less important where that data resides. However, how this is achieved programmatically is a little trickier, particularly in terms of interoperability across hybrid cloud systems.

One of the other aspects of data security we need to assess before embarking on creating a security model for data in the cloud is the levels of need; that is, how secure do you want that data to be? The levels of security of any data object should be thought of as concentric layers of increasingly pervasive security, which I have broken down here into their component parts to show the increasing granularity of this pervasiveness:

Level 1: Transmission of the file using encryption protocols
Level 2: Access control to the file itself, but without encryption of the content
Level 3: Access control (including encryption of the content of a data object)
Level 4: Access control (including encryption of the content of a data object) also including rights management options (for example, no copying content, no printing content, date restrictions, etc.)

Other options that can be included in securing data could also include watermarking or red-acting of content, but these would come under level 4 above as additional options. You can see from the increasing granularity laid out here that security, especially within highly distributed environments like cloud computing, is not an on/off scenario. This way of thinking about security is crucial to the successful creation of cloud security models. Content level application of data security gives you the opportunity to ensure that all four levels can be met by a single architecture, instead of multiple models of operation which can cause interoperability issues and, as previously mentioned, can add additional elements of human error, leading to loss of security. The current state of cloud computing provides us with a number of cloud deployment models, namely, public (cloud infrastructure that is open for public use, for example, Google App engine is deployed in a public cloud), private (privately available clouds on a private network used by an individual company; for example, IBM provides private clouds to customers, particularly concerned by the security issues surrounding public cloud deployments), managed (clouds offered by a third-party hosting company who look after the implementation and operational aspects of cloud computing for an organization), and hybrid (a mix of both public and private cloud implementations). It is highly likely, especially in the early years of cloud computing, that organizations will use a mixture of several, if not all, of these different models. With this in mind, to allow an organization to deal with securing data within any of these types of systems means that the issues of interoperability, cross-cloud support, minimisation of human error, and persistence of security are crucial. The fluid
movement of data through and between these clouds is an integral part of the cloud philosophy, and any data security added into this mix must not adversely encumber this movement. This requires that you look at that data as a separate entity with respect to the underlying system that it moves through and resides within. If you do not view the data as a free-moving object, you will build a data security model that is not built to suit the data, but instead is built for the specific system surrounding that data. In a cloud-type system, the end result is likely to be only suitable for static data (something that we have already described as not truly existing) which will not be able to transcend that original system without potentially having to be re-engineered to do so, or at the very least having additional features and functions tagged onto the original specification. This type of software engineering results in interoperability issues and an increased chance of bugs occurring, because of feature adjuncts being added as an after thought, as opposed to being built into the original working architecture of the software. In addition, what can occur with security software development, which uses a non-extensible approach to software design, is that security holes end up being inadvertently built into the software, which may be very difficult to test for as the software feature bloat increases. With this in mind, the way forward in creating data security software models for a cloud computing environment must be done from scratch. We must leave the previous world of encrypted containers behind us and open up a new paradigm of fluidic protection mechanisms based on content-centric ideologies. Only through this approach will we hope to achieve transcendence of security across the varying types of cloud architectures.


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