The Data Economy is a video podcast series about leaders who use data to make positive impacts on their business, customers, and the world. To see all current episodes, explore the podcast episodes library below.
Kurt John, Chief Cybersecurity Officer at Siemens USA, has the tough assignment of being smarter and faster with real-time security practices because, as he acknowledges, “the bad guys just need to find one way in.”
Siemens is big, really big. In the United States alone, it is a $25 billion enterprise with over 50 thousand employees providing technology and software on some of the largest industrial projects, including smart infrastructure, power grids, and building automation. Kurt and a team of 1,200 global security experts protect petabytes of data, and their cybersecurity team has been in place for a long time, going back to 1984.
Kurt is a guest on The Data Economy, a podcast presented by Redis and hosted by Michael Krigsman of CXOTalk. In this episode, he shares insights on how Siemens manages security risks and leads its transformation into a data-first, cloud-first company. Siemens’ innovations include HVAC systems, industrial motors, electrical switchgear, and software systems for factory automation, embedded systems, and digital twins.
Kurt’s security charter spans everything from the devices that Siemens’s employees use, the software systems they sell, and the best practices they share with their partners. He stresses, “With cyber defense, strength lies in the volume of data and how quickly we can raise the flag of an anomaly, or that something’s just not right, as quickly as possible.” Kurt’s team watches for an indicator of compromise (IoC), which requires collecting streaming data, understanding usage patterns, developing models, and enabling real-time decisions on whether an application or group of them is showing anomalous behaviors. Because of Siemens’ size and complexity, it has a mix of commercial, open source, and proprietary security systems and anomaly-detection technologies. Detecting anomalies is one aspect of fraud detection, a form of risk mitigation for businesses processing purchases, trades, insurance claims, and other financial transactions. These algorithms use historical transactions to create a decision model, and real-time activities are then tested for irregularities, including fraud and other conditions requiring flagging or intervention.
What can CIOs, chief data officers (CDOs), and technology leaders learn from the head of cybersecurity at one of the largest industrial software companies?
An enterprise’s efforts to collect real-time data often start internally with efforts to reduce costs, drive efficiencies, improve quality, and address risks. Some business areas may not be competitive due to the cost of delivery, and many operations require added resiliency in this post-peak pandemic world of volatile supply chains and changing customer needs. Siemens knows this all too well, as its cybersecurity program extends to the 24,000 suppliers in its network.
One bad breach can undermine the brand’s reputation. But adding real-time data and analytics can be a cyber defense and operational investment that evolves into a strategic differentiator.
“The value of data shifts from cost and efficiency to an absolute treasure trove of insights into operations, products, and market feedback,” says Kurt. “We can deliver an improved product, in less time, with fewer resources, and with more intentionality.”
Capturing market feedback and looking for insights on customer opportunities and pain points are essential disciplines for transforming organizations. Fast data ingestion processes real-time feedback, such as when customers struggle to complete a transaction on a newly released mobile site, but analysis of the aggregated data can help justify investments in new digital products and services.
Kurt speaks about how digital twins technology is transforming Siemens’ business model. He says, “Digital twins have been absolutely transformational on two fronts: The first is efficiency in engineering. Instead of having to build and rebuild and then rebuild again, and create multiple prototypes to test how a system interacts, you build a digital twin, run all of your tasks, and simulate all of your tests.”
That speaks to the internal efficiency possible with a digital twin system and is an opportunity for every manufacturing, construction, and energy company to review their operations and consider where digital twins can test and validate new products and services.
Kurt explains how digital twins are used in production operations to identify anomalies, including operational issues and security events. The digital twin should receive real-time data and events similar to what’s occurring in the physical world. Its outputs can then be compared to real-world conditions, and when there are significant deviations, the operations and field engineers know there’s a problem.
That’s Kurt’s second front, and then he adds two more. Digital twins are a training tool for technicians who can learn operations on a model before working on a physical system. And when technicians are in the field, they can connect their virtual reality headsets to the digital twin and validate their services while working on the physical system.
These innovations start with IT leaders scaling a real-time data layer, modernizing applications, and optimizing hybrid clouds. IT leaders can find short-term benefits through cost, efficiency, and other operational improvements, but the longer-term benefits come from learning customer needs, deploying real-time experiences, and developing customer-facing solutions.
Tune in to the podcast to hear more of Kurt’s insights on how Siemens provides digital twins as part of its service offerings, a true transformation to an industrial software company.
Watch more episodes of The Data Economy podcast.