While genetics is concerned with the observation of specific sections of DNA, genomics is about studying the entire genome of an organism, something that has only become practically possible in recent years. In forensic genetics, which is the technology behind the large national DNA databases being built in several countries including notably UK and USA (Wallace’s outstanding article lucidly exposes many significant issues), investigators compare scene-of-crime samples with database samples by checking if they match, but only on a very small number of specific locations in the genome (e.g. 13 locations according to the CODIS rules). In our paper we explore what might change when forensic analysis moves from genetics to genomics over the next few decades. This is a problem that can only be meaningfully approached from a multi-disciplinary viewpoint and indeed our combined backgrounds cover computer security, bioinformatics and law.

(Image from Wikimedia commons, in turn from NIST.)

Sequencing the first human genome (2003) cost 2.7 billion dollars and took 13 years. The US’s National Human Genome Research Institute has offered over 20 M$ worth of grants towards the goal of driving the cost of whole-genome sequencing down to a thousand dollars. This will enable personalized genomic medicine (e.g. predicting genetic risk of contracting specific diseases) but will also open up a number of ethical and privacy-related problems. Eugenetic abortions, genomic pre-screening as precondition for healthcare (or even just dating…), (mis)use of genomic data for purposes other than that for which it was collected and so forth. In various jurisdictions there exists legislation (such as the recent GINA in the US) that attempts to protect citizens from some of the possible abuses; but how strongly is it enforced? And is it enough? In the forensic context, is the DNA analysis procedure as infallible as we are led to believe? There are many subtleties associated with the interpretation of statistical results; when even professional statisticians disagree, how are the poor jurors expected to reach a fair verdict? Another subtle issue is kin privacy: if the scene-of-crime sample, compared with everyone in the database, partially matches Alice, this may be used as a hint to investigate all her relatives, who aren’t even in the database; indeed, some 1980s murders were recently solved in this way. “This raises compelling policy questions about the balance between collective security and individual privacy” [Bieber, Brenner, Lazer, 2006]. Should a democracy allow such a “driftnet” approach of suspecting and investigating all the innocents in order to catch the guilty?

This is a paper of questions rather than one of solutions. We believe an informed public debate is needed before the expected transition from genetics to genomics takes place. We want to stimulate discussion and therefore we invite you to read the paper, make up your mind and support what you believe are the right answers.

The problem is that (Chris’ analysis is) completely impractical. I’ll take a recent, and fairly typical situation as an example. I was taking issue with the manner in which remote access was being provisioned for a third party vendor to connect to a system hosted by one of our European business units. To cut a long story short, it was not only a breach of policy but highly insecure. I wanted the access to be disconnected, the business unit director wanted my risk assessment. And he didn’t want to wait for it.

To quote Chris Hayes, spending time on working out the expected effectiveness of controls, over a given timeframe, as measured against a baseline level of force was not going to pacify an angry Italian fearful that my decision was going to cost him money. He wanted my explanation of the risk and more importantly, what I was going to offer as a solution to keep his business functioning

As Chris is someone who actually does this for a living in a large company, and this is typical of his actual day job, I really find Stuart’s “impractical” comment to be, um, misinformed.

Also, I think Stuart mistakes the purpose of a risk analysis.  The purpose of the risk analysis is not to force someone to be “secure”, but to provide knowledge for decision making.  Using it as a “hammer” to knock in the nail of your personal risk tolerance impairs efficiency and in the long run retards “security” as it creates political resentment.  Seriously, who cares if something might violate policy or not in a pre-implementation discussion?  Policies are not stone tablets handed down from on high, they are state-in-time codification of the data owners risk tolerance.  This risk tolerance changes sometimes, and that’s OK.

To that extent, I appreciate (and I’m sure Chris does, as well) that risk analysis does not create rationality in the data owner.  Someone who sees you as a speedbump on the route to progress they may not be ready to appreciate your point of view even if it is stated in the most rational manner possible.   But it’s worth noting (and Stuart’s example is indicative of this point) that risk analysis does not create rationality in the analyst, either.  If one is being so “security minded” as to ignore the risk tolerance of the business owner - we’re bound to get a reaction similar to that Stuart encountered.  In fact, a practical risk analysis like Chris performed on his blog, done in 30 minutes, should identify the critical point of disagreement between Stuart and the data owner (again, Stuart doesn’t own the data, the agitated Italian does).

But let’s stay rational and open to alternatives to what Chris offers.  Stuart states his approach to risk analysis as:

When I need to document a risk assessment I use a very simple form: list the threats, state the level of vulnerability, list the associated operational costs and potential revenue hits. High, medium, or low risk? Describe the controls and options. Write up who needs to do what, and how much of their time it’s going to take. Job done.

At first glance, I don’t think what Chris has done is any less efficient, and it provides greater insight (using Frequency and Capability instead of just ‘listing the threats’).  But what is key here is that Chris’ approach is consistent and defensible.  Less generous risk geeks and CSO’s I know would have no little difficulty with Stuart’s approach.  But to particularly answer Stuart’s main objection (impracticality) I would offer that with practice, Chris’ work is probably quicker and easier than Stuart’s described process as it eliminates much of the ambiguity an immature risk analysis creates - reducing the need for further discussion and arguments with data owners (regardless of disposition or nationality).

Finally the irony of Stuart’s post is that the reason he had this confrontation may in fact be because he was incapable of bringing a salient model for risk to the table, one that identified the factors that create risk and developed a defensible belief statement concerning risk.   We’ll never know if one would have helped him in this isolated instance, but I can tell you that in organizations like Chris’, good risk models and strong risk anlayses create operational efficiencies, reduce costs, and streamlines intra-departmental communications.

After meetings and calls, I was finally able to slip into a conference session – just in time to catch uber-smart analysts Rachel Chalmers (The 451 Group) and Dan Golding (Tier1 Research) engage in a lively and not-so-mock debate on “Hosting Meets the Cloud”.

Now this doesn’t cover the entire debate – and part II is coming tomorrow. But what it does cover is the most interesting questions (to me) and paraphrase the points made by the analysts. I thought they both had very interesting points and more similarities than differences in the end; the real difference is how they thought about the issues and through what lens – for Rachel it was the enterprise and for Dan it was managed hosting providers. (image from inmagine)

Question: What is a cloud and why?

Dan: Shared infrastructure leveraged/run by third parties for the benefit of enterprises, developers, etc. This is not a new idea – just recently “rebranded.” Given all the discussion and disagreement over this now, what will the cloud end up looking like?

Rachel: The cloud is “IT infrastructure as a service” down to the level of a server operating system. Take the example of Amazon web services – in this case it’s not just the infrastructure but also the internal processes built around service delivery, e.g., provisioning, that are being exposed as a commodity to external customers.

Dan’s Question for Rachel: In your opinion, how much is the cloud a fad versus CIOs really trying to solve a problem?

Rachel: For the practical, roll-up-your-sleeves types of CIOs – those coming up from the engineering ranks – that I talk to, the cloud is real, as opposed to SOA and middleware.

What about “internal” cloud computing – built and maintained by an enterprise versus a third-party provider?

Dan: Cloud computing is done by providers for customers. Certainly there are enterprises that have made internal computing investments, e.g., for publishing, large-scale phone systems, etc - but they were stupid ideas made by companies that have too much money. A better question here is does it make any sense for an enterprise to create their own cloud? While an enterprise can play at it, they can’t do it cost-effectively, not in a way that a third party provider can do it.

Rachel: Many CIOs have “managed-hoster” envy – for things like chargeback and billing that hosters understand a do better. Of course there has been a rise in automation and virtualization tools in the enterprise which may not be as efficient and built for scalability as a hoster can achieve, but what is important is that they are customized/specialized for that business.

Dan: Can you give a specific example of optimization to make it worthwhile for enterprises to do it themselves?

Rachel: One example is sovereignty. The privacy laws around financial and healthcare information are not the same everywhere. Clouds and their geographically-dispersed data centers don’t necessarily have “national” borders. This is definitely a concern for the CIO that has to comply with regulations in their industry around privacy protection, for instance. Another example is security. Dow Chemical does a lot of work via joint ventures and has a need to provide but lock down desktops given to contractors as corporate workspaces. For their level of security, they need to “own” their computing resources.

Dan: But why can’t someone like SunGard provide that as they do for many other large companies?

Rachel: It comes down to a question of trust.

Do people trust their hosting providers?

Dan: Yes. Whether it’s for a content delivery network or collocation, hosting the customers of hosting providers are some of the largest companies in the world in industries like energy and financial services. Give me a case when there was a major security issue with a hosting company. In fact, managed hosting providers usually provide better security than enterprises are capable of.

And a question provided by an attendee from EMC: A few years ago, this would have been a grid discussion. How is the cloud different?

Rachel: Grid computing ended up being applicable only for niches – which I predicted. The real opportunity for everyone else with the cloud only comes up when you combine the kinds of automation tools (originally developed for grid computing) with x86 virtualization.

Dan: I agree. Grid was a niche play. There were very few orgs that needed it and that the economics worked for. There were very few enterprises for whom it made sense to build their own for. The cloud is shared/leveraged versus grid computing. It economically makes sense in a way grid never did.