Tag Archives: Data Science

One of my favorite infosec thinkers, Andrew Hay, had a pair of recent posts that have given me lots to chew on.

First, he asked:

If you heard the term 'big data security', who would you group into that category?—
Andrew Hay (@andrewsmhay) January 30, 2012

This provoked a wide-ranging conversation about what that means. We’ll find tremendous value in applying big data techniques to security data. (Actually, I think data analysis will change the world, but that’s a bit larger scope than this post can comfortable handle.) We can then start to bring in additional data feeds past what traditional SIEMs handle. Think along the lines of more OSINT, network flows, and possibly even business data. At that point, you can really start to grasp the qualitative and quantitative improvements to data protection.

The next day, he wrote an article in which he asked an oft-heard data analysis question: Where’s my ‘Minority Report’ dashboard?. We have to unpack that a little, though, because the data analysis scenes involved a few different useful things.

First, and perhaps most memorably, Cruise’s character used a gesture-based interface to work with the data he had available. As Hay notes, this tech has started to push down into consumer electronics like game consoles, but not generally into business applications like SIEM. While this might seem natural, we will have to move beyond the standard desktop metaphor and start to think of data as objects. It certainly won’t happen completely intuitively, but the long existence of similar ideas in various cultures (think mudras and sign language) and scientific research into the connection between words and gestures seems to indicate that we still have a lot of potential here.

Second, note how many disparate data feeds he had available. Apart from the fictional visualizations from the “precogs” (for which we can use surveillance video as a stand-in), he had social profiles, financial records, and more. While most of the entities we need to visualize aren’t always so human, we can assume some of the analogues I mentioned above for deploying “big data” tech. Data mining and machine learning will help here, particularly in knowledge discovery to hypothesize and test for correlations among the various data.

Third, the system latency seemed absurdly low. Try running a DB query on unstructured, near-realtime data, and tell me if it happens that immediately. While we’ve seen significant leaps in these areas, we need lots more advancement. Much of the tech today has started to move back towards a batch processing model rather than direct interaction and exploration, for example. Don’t think of this as just an engineering problem, because latency greatly matters when talking about trying to analyze data at anything remotely resembling the speed of thought.

Finally, the analyst clearly had excellent spatial reasoning skills. As younger generations continue to move into adulthood, we’ll likely see more applications of spatial reasoning. This means more research into data dimensionality: human brains don’t really visualize high-dimensional spaces very well, so we need to improve our models and analysts. It might turn out, for example, that we need to conceive of data as a hypercube as we drill down into specific nodes. Analysts already need to understand the foundations of graph theory when working in a lot of knowledge domains.

The future of data analysis excites me, and I really geek out over the possibilities. This has fractal-type potential: no matter whether we’re looking at data science from the MBA-typical “thirty-thousand foot view” or ångström altitude, we can find ways to change the world. (And if you’re working on this stuff and want some cross-domain thinking, let’s talk.)