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

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 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.

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…