Cloud-native security system suffers from limited internet connectivity, bandwidth constraints, latency, and data sovereignty, leaving edge infrastructure vulnerable to the increasing number of sophisticated cyberattacks.
The widespread adoption of edge computing technology has significantly benefited various industries, including oil and gas, retail, manufacturing, healthcare, logistics, transportation, and renewable energy. However, as edge applications are deployed across diverse geographical locations, new cybersecurity challenges have emerged for this distributed infrastructure.
The deployment of thousands of edge devices in various locations poses a bandwidth cost challenge, particularly for critical sectors like oil, gas, and energy. To address this issue, many large organizations have attempted to extend their existing traditional security solutions to protect the distributed edge infrastructure. However, there have been serious drawbacks to this approach. To understand them, watch the webinar.
Many organizations have turned to cloud-based security solutions to tackle these challenges to safeguard their edge environments. However, in this article, we will explore that there are issues associated with such conventional security measures and discuss the advantages of an edge-native security approach.
Issues with cloud-native security
Edge environments typically have distributed locations that often suffer from limited internet connectivity. Depending on a cloud-based security system in such situations can expose vulnerabilities during network outages, compromising real-time data processing and threat detection. Consequently, this connectivity inconsistency weakens edge devices' overall security, leaving them susceptible to potential cyber threats.
Moreover, edge devices' bandwidth constraints are another security concern, as they aim to minimize costs and resource usage. Cloud-native security solutions demand continuous data transmission between the edge devices and the cloud server for analysis and monitoring, putting a strain on the limited network bandwidth.
Latency poses yet another challenge for cloud-native security implementation in edge environments. With data processing and analysis taking place in remote data centers, there are inherent delays in response times. In critical applications where immediate action is important, the latency introduced by cloud-native security could result in operational disruptions.
Furthermore, data sovereignty becomes a critical issue with cloud-native security, as it involves sending proprietary or sensitive information to external cloud servers for storage and analysis. This data movement raises security concerns regarding data protection and compliance.
Why choose the edge-native security approach?
AI EdgeLabs has introduced an edge security solution designed for enterprise-scale distributed edge infrastructure, significantly improving overall security. Its key advantages are its resilience to disruption, a critical factor for industries like oil and gas and transportation, where disruptions can have severe consequences.
In contrast to conventional cloud security solutions, AI EdgeLabs' innovative approach equips edge devices to function autonomously and independently process security measures, even in environments with limited internet connectivity. This autonomous capability ensures continuous security operations, even during network outages, thereby reducing exposure to cyber threats and establishing a robust security framework.
Furthermore, the edge-native security system AI EdgeLabs provides a cost-effective solution by reducing bandwidth costs. The need for constant data transmission to remote servers for analysis is eliminated by enabling security tasks to be performed directly on the edge servers. This optimized approach eases the strain on network bandwidth, allowing edge environments to allocate their limited resources more efficiently for essential operational tasks.
The real-time threat detection and low latency features of AI EdgeLabs' solution offer particular benefits in mission-critical scenarios. With a secure data approach, the edge-native software-defined security solution addresses data privacy concerns by keeping sensitive information localized within the edge environment, eliminating the necessity for data transmission to external servers.
AI EdgeLabs is a future-proof security model by leveraging artificial intelligence. This incorporation of AI equips the security system to effectively safeguard against unknown attacks and eliminate zero-day vulnerabilities in edge devices.
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