FUTURE TECHNOLOGY
How secure are those APIs interconnecting LLMs with applications ?
Steven Duckaert , Director of Customer Success EMEA and APJ , Noname Security
With organisations using multiple LLMs that require numerous APIs , the lack of a robust API security monitoring and remediation strategy for LLMs can have a snowball effect , exposing APIs and the data they handle to bad actors , says Steven Duckaert at Noname Security .
The popularity of Large Language Models , LLMs has prompted an unprecedented wave of interest and experimentation in AI and machine learning solutions . Far from simply using popular LLMs for sporadic background research and writing assistance , LLMs have now matured to the degree where particular solutions are being used within specific workflows to solve genuine business problems .
Industries such as retail , education , technology , and manufacturing are using LLMs to create business solutions , delivering the required tools to automate complex processes , enhance customer experiences , and obtain actionable insights from large datasets .
APIs play a central role in democratising access to LLMs , offering a simplified interface for incorporating these models into an organisation ’ s applications , and for LLMs to communicate with each other
They frequently have access to a diverse library of sensitive data , automating the collection of information , that enables LLMs to provide tailored business solutions to meet specific needs .
During LLM development , or when using APIs to integrate multiple LLMs into existing technology stacks or applications , their efficiency is entirely dependent on the security posture of each API that ties them together .
With organisations using multiple , purpose-built LLMs that require numerous
APIs play a central role in democratising access to LLMs , offering a simplified interface for incorporating these models into an organisation ’ s applications .
APIs , the lack of a robust API security monitoring and remediation strategy for LLMs can have a snowball effect . It can expose new vulnerabilities that may not have been considered , and leave APIs and the data they handle , dangerously exposed to bad actors .
Before thinking about how to automate tasks , create content , and improve customer engagement , businesses must take a proactive stance towards API security throughout the entire lifecycle of an LLM . This includes :
Design and development Without a proactive approach to API security , new vulnerabilities can be introduced .
Training and testing Developers must anonymise and encrypt training data , and use adversarial testing to simulate attacks and identify vulnerabilities .
Deployment If secure deployment practices are not followed , attackers can exploit unsecured
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