The supply chain how materials enter into the production process, and semi or fully finished goods are distributed outside is fundamental to any manufacturing organization. It is also tightly connected to consumer demand. Many global organizations use demand forecasts to determine the quantity of materials necessary, manufacturing line requirements, and distribution channel loads. Analytics have also become more sophisticated, so that today’s organizations are able to utilize data and analytics to understand and predict customer buying patterns. Industry 4.0 technologies are expected to prompt a further evolution in the traditional linear supply chain structure by introducing intelligent, connected platforms and devices across the ecosystem, resulting in a digital supply network (DSN) capable of capturing data from points across the value chain to inform each other. The result may be better management and flow of materials and goods, more efficient use of resources, and supplies that more appropriately meet customer needs.

Organizations may thus want to consider ways to secure that information to prevent unauthorized users from accessing it across the network. They would also likely need to remain disciplined about maintaining those safeguards across all supporting processes, such as vendor acceptance, information sharing, and system access. Not only may these processes be proprietary in their own right, they may also potentially serve as access points to other internal information.

Data sharing : Increased Access to data for more stakeholders

Organizations will likely need to consider what data should be shared, and how to protect the systems and underlying data that may be proprietary or have privacy risks. For example, some suppliers in a particular DSN may be competitors in other areas, and may not wish to make certain types of data available, such as pricing or information about proprietary materials. Alternatively, the suppliers may be subject to regulations that limit the type of information that can be shared. Opening up just part of the data may make it possible for those with malicious intent to gain access to other information. Organizations should utilize good hygiene techniques such as network segmentation and intermediary systems that serve as “middlemen” to gather, protect, and provide information. Additionally, technologies such as trusted platform modules or hardware security modules should be incorporated into future devices to provide robust cryptologic support, hardware authentication, and attestation. By combining this approach with robust access controls, mission-critical operations technology is secured at the application points and endpoints to protect its data and processes.

Increasing production, increasing risk

As production facilities increase integration and deployment of IoT devices, it typically becomes even more important to consider the security risks these devices pose to manufacturing, production, and enterprise networks. Security implications of compromised IoT devices include production downtime, damage to equipment or facilities that could include catastrophic equipment failure, and, in extreme cases, loss of life. In addition, potential monetary losses are not limited to production downtime and incident remediation but can extend to fines, litigation expenses, and loss of revenue from brand damage that can persist for months or even years, well beyond an actual incident. Current approaches to safeguarding connected objects, some of which are listed below, may prove insufficient as both objects and attendant risks proliferate.

Building Cybersecurity into the design process from the start

Manufacturers may be feeling a growing responsibility to deploy hardened, almost military grade connected devices. Many have articulated a need for IoT device manufacturers to incorporate secure coding practices that include planning, designing and incorporating cybersecurity leading practices from the beginning and throughout the hardware and software development life cycle. This secure software development life cycle (S-SDLC) incorporates security gateways throughout the development process to assess whether security controls are effective, implements security leading practices, and uses secure software code and libraries to produce a functional and secure device.

The safety of sensitive data throughout the data life cycle will likely also need to be protected with the same sound security approach required to produce hardened devices. IoT device manufacturers would therefore need to develop approaches to maintain protection not only securely store all device, local and cloud stored data but also quickly detect and report any conditions or activities that may jeopardize the security of those data. Protecting cloud data storage and data in motion often necessitates the use of strong encryption, artificial intelligence (AI), and machine learning solutions to create robust and responsive threat intelligence, intrusion detection, and intrusion prevention solutions.