

Source: MarketsAndMarkets

Thirty-nine US robotics companies raised $519 million in the first quarter of 2017, according to a Crunchbase News analysis. This compares to $553 million raised in all of 2014 by 134 companies, implying that deal sizes are getting larger.

Whether because of government regulation or good old-fashioned self-interest, we can expect increased investment in IoT security technologies. In its recently-released TechRadar report for security and risk professionals, Forrester Research discusses the outlook for the 13 most relevant and important IoT security technologies, warning that “there is no single, magic security bullet that can easily fix all IoT security issues.”
Based on Forrester’s analysis, here’s my list of the 6 hottest technologies for IoT security:
Note that Forrester did not identify any technologies in the “creation” stage. It says: “The continued evolution of IoT-specific security threats will undoubtedly drive innovation in this space, so expect more new IoT-specific security technologies to appear in the creation phase in the near future, many of which may align around vertical- and industry-specific use cases such as connected medical devices or industrial applications.”
Forrester lists the following challenges to achieving a secure IoT: Many IoT devices lack basic security requirements; There is a plethora of IoT standards and protocols, which creates security blind spots; The scale and scope of IoT deployments hinder visibility into security incidents; There is a lack of clarity of responsibility regarding privacy and security.
“The Dyn attack was likely just the start,” says Forrester, “so [we] can expect further attacks that leverage insecure IoT devices in the coming months and years.” Indeed, Gartner placed security at the top of its list of top 10 IoT technologies for 2017 and 2018, saying “IoT security will be complicated by the fact that many ‘things’ use simple processors and operating systems that may not support sophisticated security approaches.”
It’s complicated when simple things connect to become a vast network that reaches everywhere. Forrester makes the following observations and recommendations: IoT security requires an end-to-end approach; Encryption is an absolute must; IoT security scenarios place a premium on scalability (dealing with the sheer number of devices); Security analytics will play a significant role in IoT security solutions; IoT standards are important catalysts but still need time to mature.
Concludes Forrester: “It’s imperative for today’s digital businesses to balance the business benefits that IoT-connected products can deliver with the recognition that these same devices have become an attractive attack plane for hackers and cybercriminals seeking to cause disruption and exfiltrate sensitive data.”
Originally published on Forbes.com

CDC:

Robotic Industries Association:
North American robotics companies posted the strongest ever first-quarter results, according to the Robotic Industries Association (RIA), the industry’s trade group. Both robot orders and shipments achieved record levels.
An all-time high total of 9,773 robots valued at approximately $516 million were ordered from North American robotics companies during the first quarter of 2017. This represents growth of 32 percent in units over the same period in 2016, which held the previous record. Order revenue grew 28 percent over the first quarter of last year. Robot shipments also reached new heights, with 8,824 robots valued at $494 million shipped to North American customers in the opening quarter of the year. This represents growth of 24 percent in units and five percent in dollars over the same period in 2016.

Data are to this century what oil was to the last one: a driver of growth and change. Flows of data have created new infrastructure, new businesses, new monopolies, new politics and—crucially—new economics. Digital information is unlike any previous resource; it is extracted, refined, valued, bought and sold in different ways. It changes the rules for markets and it demands new approaches from regulators. Many a battle will be fought over who should own, and benefit from, data…
The problem [with personal data] is the opposite to that with corporate data: people give personal data away too readily in return for “free” services. The terms of trade have become the norm almost by accident, says Glen Weyl, an economist at Microsoft Research. After the dotcom bubble burst in the early 2000s, firms badly needed a way to make money. Gathering data for targeted advertising was the quickest fix. Only recently have they realised that data could be turned into any number of AI services.
Whether this makes the trade of data for free services an unfair exchange largely depends on the source of the value of the these services: the data or the algorithms that crunch them? Data, argues Hal Varian, Google’s chief economist, exhibit “decreasing returns to scale”, meaning that each additional piece of data is somewhat less valuable and at some point collecting more does not add anything. What matters more, he says, is the quality of the algorithms that crunch the data and the talent a firm has hired to develop them. Google’s success “is about recipes, not ingredients.”
That may have been true in the early days of online search but seems wrong in the brave new world of AI. Algorithms are increasingly self-teaching—the more and the fresher data they are fed, the better. And marginal returns from data may actually go up as applications multiply, says Mr Weyl.
See also:
Data is Eating the World: 163 Trillion Gigabytes Will Be Created in 2025
Data Is Eating the World: Enterprise Edition
