As enterprises increasingly rely on AI-powered solutions and edge computing IoT devices for a promising future, experts believe it creates new vulnerabilities and risks sensitive data

Technologies like artificial intelligence (AI), machine learning (ML) and augmented reality (AR) now play an advanced role in industrial environments. However, no change is risk-free.

Adapting to new technologies into traditionally analogue environments increases security risk as more ‘things’ get digital. While AI-powered and automation tools streamline operations, maintenance, and user experience, they also create new doors for the intrusion. This can have negative results such as the downtime, loss of IP, or even bodily harm.

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Market research firm IoT Analytics has forecasted that spending on Industry 4.0 services and products will grow from $119 billion to $310 billion in 2023. The industries most likely to gain the industrial IoT investments include manufacturing, transportation & logistics and utilities, where each has been projected to spend over $40 billion on IoT systems, platforms, and services by 2020.

One of the most significant trends to watch out for that can affect how an enterprise manages vulnerabilities, is the risk from AI-powered solutions. The application of AI, ML to cyber operations makes it easier to carry out attacks at machine speeds. Experts look at in a way where there is smart malware that can learn as well as adapt as it spreads. So ML can coordinate global attacks, while, predictive analysis can optimize the attack. Experts warn that the hackers are all closer to reality.

As the AI technology develops, in industrial settings, more and more operational technology (OT) security teams will adopt AI-powered defense mechanisms to thwart smart threats. Cyber experts believe that even these tools can be subject to sabotage as threat actors can poison the data that AI tools train on. With biased data sets, an algorithm’s training can be completely thrown off.

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This makes it even more crucial for human operators to be more involved in industrial environments with automated assets and processes. Organizations need to consider the ‘Human in the Loop-type frameworks’ that combine management aspects and technical approaches with the deployment and use of AI and ML.

Another significant risk that CISOs need to consider is from edge computing and the spread of sensitive assets. As an increasing number of IoT devices come online, it popularizes the idea of edge computing as a way to deal with the vast amount of data that is being generated. Especially when focus is on analyzing and processing data on devices at the edge network, instead of the data center or central hub. The end goal is to deliver better performance to reduce operational strain and cost.

Experts believe that implementing such a kind of infrastructure expands organizations’ attack surfaces with new attack vectors. This issue gets worse, considering the diversity of IoT cases and the way they are different from older, legacy IT technology. Unlike IT, currently there are no existing IoT standards that can help regulate security,.

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The devices on the edge can create problems with mere tracking and monitoring. Default and weak credentials for the devices and insecure communication could be an issue as well. Though all devices are not viewed as critical, sometimes, seemingly insignificant information can sometimes be valuable to attackers as it could signal the companies’ plans. Enterprises also need to consider the physical security risk like damage and tampering around edge computing.

Ultimately, both edge technology and AI-powered solutions have tremendous potential to leverage the promises of Industry 4.0 reality. However, the technologies cannot be taken at face value. CTOs need to ensure that in any way, the adoption of these technologies in IIoT does not introduce more risk than benefit.

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