Edge AI: the enterprise AI revolution you haven’t heard about (yet)

Artificial intelligence (AI) is increasingly becoming integral to business operations, transforming creativity and automation to enhance human potential. Large language models (LLMs) have shown promise in enterprise applications such as customer service chatbots, code assistance and in marketing content creation. Yet they often fall short of the reliability needed for critical tasks. To enhance AI’s accuracy and trustworthiness, a revolutionary development is emerging right at where you have control of this technology: Edge AI.

 

Edge AI involves processing data locally from sensors, devices and products rather than relying on a centralised cloud. This method is gaining traction among enterprises, tapping into the valuable data generated by assets such as building sensors, smart cameras, vehicle fleets, robotics and even humble Raspberry Pi units used to automate your factories. This localised data processing is key to leveraging the full potential of business data, providing AI solutions tailored to specific needs and enriched by sensor-driven data. Trusted data is the cornerstone of effective innovation.

 

While current LLMs can access up-to-date data to enhance accuracy, they don’t always ensure data security or privacy. For example, AI applications in code creation or customer service chatbots might access sensitive information from CRM systems, user data or confidential files. Without proper security measures, this data could be at risk of exposure or leaks.

 

Researchers and developers are working on AI advancements such as retrieval-augmented generation (RAG), which allows LLMs to retrieve only specific database queries for more accurate responses. However, merely limiting AI to retrieve from data queries isn’t sufficient; securing data at its origin is crucial.This begins with the identity of your devices, so that data generated can be authenticated and secured.

 

The integration of iSIM technology simplifies this process. Using a secure element such as the eSIM as a security anchor, iSIM technology ensures data security on devices, during transmission and in use. Even devices with limited network and power capabilities can authenticate securely, thanks to products adhering to GSMA standards such as Kigen IoT SAFE. Cryptographically signing and sealing each data piece enhances trust and verification. This ensures that when data reaches an LLM, it includes access controls and cryptographic verification, preventing unauthorised access or training. These measures also bolster data provenance and verification, contributing to digital authenticity in AI outputs. Model explainability and device-data provenance are essential for gaining stakeholder trust and maximising AI’s business value.

 

This technology is readily available to a wide range of industries, including retail, healthcare, manufacturing, automotive and consumer electronics. At Kigen, we’re witnessing a surge in edge AI applications across various sectors, from EV charging to food packaging. However, for enterprise AI to evolve responsibly, it’s vital to safeguard, govern and manage data usage by LLMs. C-suite leaders must cultivate a security-first culture, turning it into a strategic advantage to protect intellectual property and maintain competitiveness.

 

Implementing this mindset starts with educating teams in "Cybersmarts for AI". Regular training and workshops can keep everyone informed about best practices for working with AI while protecting privacy and intellectual property.

 

As AI continues to transform enterprise operations, security becomes a fundamental necessity. By prioritising data and trust security from the outset, C-suite leaders can ensure their enterprises remain at the forefront of innovation, safeguarding their most valuable assets.

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by Kigen, Vincent Korstanje, CEO 

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For more information please visit kigen.com/cybersmartsforAI/