Thank you, internets, for all the feedback I’ve gotten on BoomTime: Risk As Economics. Of course my slides are nigh indecipherable without my voiceover, and my notes didn’t make it to the slideshare, so here are some notes to fill in (some) of the blanks until the video hits YouTube (SiRA members will get early access to SiRAcon15 videos via the SiRA Discourse forum, BTW). (You will want to look at the notes and the slides side by side, probably, as one doesn’t make sense w/o the other.)
A list of typical themes one hears when discussing information security & economics: within businesses we are requested to talk about exposures and threats in terms of financial impact, or consider the financial (money) drivers. Also the theme of information asymmetries (Market for Lemons) is a big theme of information economics and of software markets in general: when information about quality of a product is difficult to find, that lack of transparency drives down prices, and we get less incentives to improve quality. (Ask me questions about market signals as a mechanism for correcting information asymmetries.) “Make it more expensive for the attacker” or “don’t outrun the bear, outrun the guy next to you” is also an idea that gets raised. Game theory, concepts of quantifying “risk” (exposure, tolerance), markets for exploits & vulns is a hot topic at the moment, as is behavioral economics and all things related to incentive design – gamification being the most buzzwordy example, perhaps, but framing as a method for improving consumers’ ability to make good choices related to privacy preferences also something that has come up a bit lately in security economics research. Anyway, these are some themes that tend to be repeated in recent research literature.
Last week I stopped into SOURCE Dublin to give a follow-up to my recent talk in Boston, another foray into game theory (Games We Play: Payoffs & Chaos Monkeys) — this time w/some more advanced mathiness and references back into behavioral economics. Anyway, I still owe some explanatory blog posts to support some of the materials I had to rush through (to get everything into 45 minutes), but first thing I wanted to share is my working reading list. I’m finishing up reading some other books which I’ll post later but this is a good overview and will get folks interested in the topics headed in the right direction.
Over the last year I’ve started reviewing game theory in more depth, looking for some models I can use to understand system management (vis a vis risk) better. Game theory is one of the more interesting branches of economics for me, but I don’t actually have a great intuition for it yet (I really have to work at absorbing the material). Since it doesn’t come super-naturally to me, I’m particularly proud of the presentation I gave at SOURCE Boston last year: Games We Play: Defenses and Disincentives (description here). Luckily, there is a good video of the presentation, because when I wanted to expand out the presentation a few months later, my notes were totally undecipherable. 🙂
Yes, that is a Pringles can sharing the podium with me. Photo credit (and Pringles credit) go to @attritionorg.
Since I am still a proponent of applied risk analytics (as in my talk at Brucon this year: A Million Mousetraps: Using Big Data and Little Loops to Build Better Defenses (description here), I’ll never be able to escape behaviorally-driven defenses, but even with the power of big data behind us it feels like we defenders often find ourselves playing the wrong game. I don’t disagree the deck might be stacked against us, but we might be able to at least take control of the game board a little better.
Essentially — I am interested in we how might be able to adjust incentives in order to improve both risk reduction, whether from a fraud, security, or general operational dynamics perspective. Fraud reduction typically considers incentives and system design rather vaguely (not in a systematic way, except maybe in the case of authentication), and instead relies almost exclusively on behavioralist approaches (as typified by the complex predictive models launched looking for patterns in real time. I have been wondering for a while if we can “change the game” and get improved results.