The Internet of Robotic Things: Secure, harmless helpers or vulnerable, vicious foes?

1 2 Page 2
Page 2 of 2

The industry has not adequately identified this threat from the data plane, from the data coming in from the Internet and Internet-connected sensors, in order to verify that the sources are trustworthy.

It should not, however, be difficult to achieve verification. Realizing that there are several temperature sensors on a plant floor, for example, an enterprise could compare sensor readings in order to address these risks. “If one sensor records a drastically different temperature than the other sensors do, or if that one sensor is supposed to be in the US, and all of a sudden its DNS registry is in Romania, attackers may be spoofing it,” says Cooper.

Internet of Robotic Things security challenges enterprises

The awareness and intelligence from environmental sensor data that Internet-connected robots from different vendors will increasingly share between them in this ecosystem is a big security challenge for the enterprise, whether it is producing robotics, AI, and related data or simply consuming them, says Cooper.

The smart home is a great example. It’s really just a set of single-point robots like the Roomba and smart connected devices making individual decisions. “In 10 years—and we have some customers who are working on this—your smart home will actually become aware,” says Cooper.

The smart home will apply base services and presence, knowing where family members are and what they are doing, and use that information to tell the Roomba to leave the room where they are hosting a party, to tell the assisted living service to move objects that an elderly patient could bump into, and to tell a service robot to bring a family member their sneakers.

“That kind of predictive element requires those base services to be available and shared as sort of an awareness, a consciousness,” says Cooper. There will be that kind of awareness and service availability in industry as well. But it opens up the potential for a proliferation of security threats and faults between systems of multiple vendors.

This demands a sophisticated system of data provenance that knows where data came from, what happened to it before it arrived, and what decisions systems have already made in order to address those security threats and faults, says Cooper. This could help prevent false data from spoofed sensors from having the effect the attacker intended.

Will enterprises meet those challenges?

Pew Internet data says that AI and Robotics will be in nearly every aspect of human life by 2025, just 10 years from now. Will enterprises meet these security challenges by then? Perhaps, with the right preparation and tools they will.

Cloud-based data processing is one possibility. A distributed intelligence model would enable a subset of local decision-making on a drill head on the factory floor, for example. The cloud could take that drill head data output, perform some additional intelligence analysis on it, and provide that back to the cloud and down to the drill head to capture and provide provenance about data.

This data provenance could control and secure that data at the same distributed points where that intelligence is generated. Together with that data provenance, the cloud system would capture and provide information about how that data should be secured, who can see it, and how they can use it.

Copyright © 2015 IDG Communications, Inc.

1 2 Page 2
Page 2 of 2
The 10 most powerful cybersecurity companies