Remote Work and Cybersecurity: 3 Experts Describe the Tech They Wish Everyone Could Use

The pandemic forced organizations to adapt quickly to a distributed workforce, a shift that resulted in unprecedented challenges to business continuity and productivity

Cybercriminals decided to shift their tactics as well, targeting remote workers with a wave of phishing scams and breach attempts. 

With some major multinational organizations planning to implement working models that incorporate a remote element in varying degrees, it has become apparent that the future of work will be one defined by increased flexibility.

For most organizations, that means a continued effort to secure their networks, with new considerations for securing cloud-based systems and integrated networks.  

We asked three IEEE Impact Creators, each a cybersecurity expert, a simple question: 

If there was one piece of technology you could implement to improve the security of a remote workforce, what would it be?

Their answers provide a fascinating look at the technologies of the future, and the state of the world today. 

Federated Learning: Enterprise security systems rely on artificial intelligence to detect behavioral anomalies and intrusions on a network. When more data is available, the system is better. But sometimes, the various components of a network don’t want to share data for privacy reasons. There is a lot of data on a mobile device, for example, but sharing that information could be detrimental to the owner of the device. Larger organizations might not want to share data with smaller organizations, because the risk of data loss outweighs the security benefits. Federated learning is a decentralized machine learning technology that allows multiple entities (which can include businesses, nodes on a network, or mobile devices) to contribute to a machine learning model without sharing or exchanging data. Federated learning systems can also use blockchain as a means of sharing information. 

 “More system protection policies and edge intelligence-based calculation modes are vital for data security. Data is always intercepted during transmission from the perceptual side to the service side. The design of edge servers and the development of federated learning can greatly perfect the protection schemes of the infrastructure for daily life.” — IEEE Senior Member Guangjie Han 

Better Bring Your Own Device (BYOD) Policies: For a variety of reasons, like personal preference or situational need, people often use their own laptops and their own phones for work. In fact, it’s hard to stop them. But these devices may have less security than their corporate counterparts. The solution is a set of policies that allow systems administrators to selectively wipe application data from an employee-provided device to remove sensitive company information from a personal device. These policies come in handy in the event an employee’s phone is lost or stolen, or if an employee separates from a company.

“I would recommend system administrators use BYOD programs which can let administrators do selective wipes of devices and clean App data without wiping the entire device. Additionally, when a full wipe is needed, the policy can force an SD card wipe along with the internal storage of the device if necessary.” — IEEE Senior Member Kevin Curran

Endpoint data loss prevention: This technology monitors end-point devices (like company smartphones and laptops) to determine when sensitive or high-value data is used or shared. It’s a vital tool to prevent insider thefts of intellectual property.

“As part of the great resignation of 2021, we’ve seen an increasingly fragmented view of intellectual property on the part of departing employees. Businesses can reduce the substantial risk associated with data exfiltration of trade secrets, regulated data and other sensitive data by deploying and monitoring DLP across the enterprise, including remote endpoints.” — IEEE Senior Member Kayne McGladrey


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