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Edge AI: Changing the Cyber Security Landscape through Scalable and Flexible Data Safety

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Edge AI: Changing the Cyber Security Landscape through Scalable and Flexible Data Safety

The year 2020 was a watershed moment for technology adoption. The COVID-19 pandemic forced businesses around the world to transition to remote working and run operations through cloud-based platforms. Fields such as telehealth, e-commerce, and digital collaboration technologies also saw record growth. Experts anticipate that some of these changes might be here to stay. In fact, research reveals that in the US alone, about 36 million people will be working remotely by the end of 2025. This amounts to nearly 22% of the entire workforce—a massive 87% jump from pre-pandemic figures.

While this shift has irrefutable benefits, it also exposes new vulnerabilities. A more distributed network opens up multiple endpoints and increases security concerns across operations. 2020 also witnessed the 5g rollout which made devices more connected than ever before but has also exacerbated the security risks associated with IoT devices.

All things considered, cybersecurity has perhaps never been more crucial. In 2021 the frequency of new cyber-attack incidents is estimated to be one every 11 seconds, almost twice the rate of 2019 (one every 19 seconds). Moreover, the total annual cost borne by the victims of cybercrimes around the world is pegged to be north of US$ 6 trillion by 2021—A figure substantial enough to rival large national GDPs!

Under these circumstances, enterprises must leave no stone unturned when it comes to leveraging the right technologies to secure their digital futures. Traditionally, enterprises have deployed integrated cybersecurity solutions based on legacy, centralized architectures.

But there is a better, more robust way to tackle threats. Edge AI is a system that harnesses AI and ML algorithms and processes the data generated by a local Edge Computing environment locally. It holds the potential to greatly enhance security levels, especially in terms of data privacy due to the lack of a centralized repository. But how can organizations go about implementing and translating Edge AI, a relatively new concept, to tangible business success?

Edge AI: An Introduction

Edge AI transfers the ability to process information to a distributed model rather than the legacy central model. This increases the speed of both data processing and data churning. Edge AI’s distributed model can address privacy requirements and maintain a much stronger operational security posture.

For a remote workforce, Edge AI is a highly efficient and effective cybersecurity solution as it can effectively counter the challenges pertaining to data privacy and security that arise out of having multiple endpoints. In fact, smart enterprises have already woken up to their advantages and the market value of Edge AI is expected to grow from just US$355 million in 2018 to US$ 1.12 trillion in 2023.

Taking the Edge off Security Vulnerabilities

Edge AI combines the productivity and efficiency of automation with the security of edge computing. As the operations are handled in smaller chunks at the individual endpoints, users can incorporate more security capabilities without disrupting overall performance. By adequately addressing data privacy and security concerns, organizations are relatively more confident of their ability to comply with the different regulatory as-well-as standards and including the cybersecurity capabilities, wherein the business teams can take a more aggressive and confident approach towards business growth in a highly secure and compliant environment. The AI capabilities can help automate tasks and accelerate DevOps, which boosts overall productivity.

Of course, there are certain caveats of leveraging Edge AI as well. For an enterprise, Edge computing expands the area of operation by supporting a more distributed form of operations. This means that threat actors can target each edge individually. Although, at the same time, Edge AI-powered distributed frameworks support the standardizing of security postures. Advanced security measures and upgrades thus can be replicated in a uniform manner creating a more secure environment than a centralized model.

Enterprises that seek to implement Edge AI must be careful to account for the capabilities and considerations of both AI and Edge computing. They will do well to adopt and follow a set of guidelines and best practices that ensure a smooth optimal Edge AI implementation.

What to Watch for When Implementing Edge AI

Here is a suggestive list of factors that enterprises must account for while implementing Edge AI.

  • Users must apply basic security best practices with devices involved in edge computing. This includes ensuring the use of well-defined and demarcated levels of protection, access types, robust patch management programs and version update status of programs/OS.
  • Platforms and solutions that are leveraged to enable edge AI must be highly secure as well. Their base capabilities must tie back to a much stronger security posture.
  • Access and roles from administration to processing to consuming data output must be defined clearly. Specific accessibility criteria and privileges for the entire chain including bots and system functions must be defined and secured. Basically any data endpoint within the security chain has to be protected.
  • IT must have clear visibility into the entire chain and respective processes. They need to closely monitor access and privilege elevations so that they can observe and analyze all the information and events, and successfully preempt any kind of malicious activity. Monitoring can add the capability to localize and remediate any suspicious activities. It is especially important because malicious activities traversing from one edge to others can be catastrophic to the entire system.
  • A culture of process monitoring on the edges can help prepare for enterprise disaster recovery (DR) as well. As IT does not have absolute control over the edge, enterprise DR must account for extensive DR planning with special focus on communication, recalibration of DR testing, and network redundancy.


Edge AI can have a direct impact on businesses and their bottom-line. However, organizations must tread carefully around the various complexities and considerations related to implementing Edge AI. Partnering with experienced service delivery teams can help organizations enjoy a smooth and optimal Edge AI implementation experience. Getting Edge AI right can help enterprises navigate the murky and untested waters of virtual work but also help them usher in an era of more secure and productive operations.


  • Edge AI holds the potential to enhance security levels, especially in terms of data privacy as the data is not bound to a centralized repository
  • Edge AI can be a highly efficient solution of remote working as it can counter the myriad challenges pertaining to data privacy and security arising out of multiple endpoints
  • Edge AI blends in the productivity and efficiency capabilities of Automation with the security of edge computing

Authored by:

Renju Varghese,

Fellow and Chief Architect of the Cybersecurity & GRC business, HCL Technologies

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