Controls become pervasive
Future of risk series: Trend two
Smart devices (also known as the Internet of Things) equipped with a variety of sensors, communications, and computing capabilities serve as risk monitoring and enforcement points. This presents an opportunity for organizations to detect risk events, derive crucial risk insights, and even take immediate actions in the environment. The result? Real-time, pervasive, dynamic risk management.
- What forces are driving this trend?
- What are the opportunities?
- What are potential threats and pitfalls?
- Case studies: Where is this trend already in play?
- Meet the authors
What forces are driving this trend?
|Declining cost, decreasing size, and increasing connectivity of sensors|
|Increasing investments in the Internet of Things|
|Growing adoption of workplace wearables|
|Advancements in sensor technology|
|Advancements in analytics|
|Businesses operating as networked ecosystems|
What are the opportunities?
- Enhance operations and improve risk-related decision-making by integrating pervasive risk controls in areas such as internal audit, supply chain management, finance, cybersecurity, and controls testing
- Reduce cyber security and fraud risk by using sensor-enabled devices to implement context-aware identity access capabilities
- Improve traceability across the supply chain, especially in security-sensitive industries such as food production and pharmaceuticals
- Automate compliance monitoring and reporting by embedding risk controls into business technologies
- Manage risks introduced by customers by analyzing customer behavior through real-time data feeds
What are potential threats and pitfalls?
- Heightened exposure to cyber risks as business processes rely more heavily on the Internet of Things
- Greater availability of data revealing risks in areas that were formerly considered safe, resulting in new obligations to manage those risks or increased liability
- Rising privacy concerns from employees, customers, and business partners because of pervasive monitoring
- Increased difficulty of filtering relevant information from the noise, given the vast amount of data generated
Case studies: Where is this trend already in play?
Saia, a US-based freight company, has worked with Intel to deploy sensors into its truck fleet to track maintenance needs, driver safety, fuel usage, and other metrics in real-time. Through real-time process intelligence, this initiative has led to a 6 percent increase in fuel efficiency, which translated to $15 million in savings for Saia. In addition to achieving cost savings, Saia has been able to track maintenance needs, driver safety, and fuel usage, as well as other metrics, in real time.1
Fujitsu has developed wearable tags that can detect whether users have changed location or posture, have fallen down, or are experiencing high heat. With the help of these tags, employers can—in real time—monitor employees’ working conditions or detect if they are carrying a heavy load or standing in a place where they might fall. The aim is to reduce the risks of injury at the workplace.2
Singapore-based TrustSphere, whose clients include financial services firms, specializes in trying to uncover the relationships that an employee has through digital interactions—attempting to reduce the risks of illegal collusion and internal fraud.3
1 Erica E. Philips, “Internet of Things reaches into the trucking business,” The Wall Street Journal, April 29, 2015, http://www.wsj.com/articles/internet-of-things-reaches-into-the-trucking-business-1430342965; “How IoT is transforming the logistics industry,” Mubaloo Innovation Lab, May 15, 2015, http://innovation.mubaloo.com/news/iot-logistics/.
2 Tim Hornyak, “Fujitsu pushes wearable IoT tags that detect falls, heat stress,” PC World, May 13, 2015, http://www.pcworld.com/article/2921972/fujitsu-pushes-wearable-iot-tags-thatdetect-falls-heat-stress.html.
3 Vidya Ranganathan, “Banks chase trading cheats with ‘fuzzy’ surveillance,” Reuters, November 18, 2014, http://www.reuters.com/article/markets-surveillance-idUSL3N0T23N220141118.