On the train ride in this morning, I read a fascinating article that unintentionally highlighted where and when automation makes sense.
The article, This $1,500 Toaster Oven Is Everything That's Wrong With Silicon Valley Design, details a smart oven called June that uses a universe of technology to cook your food. But in attempting to cook a piece of salmon, the author finds that using automation to cook the fish just isn’t worth it.
This salmon had become more distracting to babysit than if I’d just cooked it on my own. This salmon had become a metaphor for Silicon Valley itself. Automated yet distracting. Boastful yet mediocre. Confident yet wrong. Most of all, the June is a product built less for you, the user, and more for its own ever-impending perfection as a platform. When you cook salmon wrong, you learn about cooking it right. When the June cooks salmon wrong, its findings are uploaded, aggregated, and averaged into a June database that you hope will allow all June ovens to get it right the next time. Good thing the firmware updates are installed automatically.
In the end, June – a well-intentioned piece of technology – strikes out not because of the tech, but because the problem it intends to solve isn’t really a problem that should be solved. A prerequisite to considering automation is whether the activity in question delivers incremental value each time.
When it comes to nuanced, subjective activities like cooking, playing an instrument or painting, the end result isn’t the only value produced. Mistakes are important, and the obstacles highlight the path forward.
By the same token, when completing the same task many times with the same outcome, there’s little or no value to be derived.
A great example of this is Cujo, the home firewall that connects to every Internet-connected device on your network. Cujo automatically blocks you from visiting known malicious sites and thwarts hackers from using your IoT devices in DDoS attacks. I don’t need to know the details about my dad visiting a site known to serve malware or learn about a bot in Mongolia that is trying to log in to my printer. Instead, I’m happy with automation stopping the threat.
In every exercise, there is a balance between the utility of knowing how to do a task, and the efficiency of automating the work. Cooking a salmon well may seem difficult, but at least you learn from it and become a better cook. Cooking a salmon using June just teaches you how to cook a salmon using June. On the other hand, detecting a potential cyber threat is a numbers game. With so many different threats out there, responding to each one has little incremental value. That’s where automation makes sense.
So, when considering automation, ask yourself: Will this eliminate a task that my team can learn and grow from, or will it eliminate a task that’s making them inefficient? Finding the balance is key for effective security teams.
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- Introducing Deep Learning: Boosting Cybersecurity With An Artificial Brain
- Improving Attribution & Malware Identification With Machine Learning