April 15, 2024

Overcoming Software Risk for LLMs (Large Language Models)

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In the age of digital transformation, mounting pressures to rapidly create and deploy applications are faced by software developers. This is especially true for goals concerning migration to cloud technology. One solution being explored by developer teams is AI-enabled tools such as Large Language Models (LLMs) to simplify and automate tasks. The use of AI and LLMs promises huge potential for business operations and such tools are poised to transform business. LLMs can potentially streamline company operations, optimise financial planning and strategy, increase workflow productivity, and enhance company marketing. 

LLMs were initially considered to be advantageous due to the potential to provide firms with transparency and flexibility, savings on cost, and added features to software. However, this is only the case if companies are able to manage the software development risks associated with LLM deployment. Investing in their research and development is critical to their capacity for innovation. Otherwise, LLMs can be extremely costly in terms of budget and time, and ultimately cause more problems than benefits for companies and COOs. 


Richard Dawson, Senior Developer at Synetec explores the importance of software risk management for LLMs and how stakeholders can mitigate against these potentially expensive hurdles. When it comes to the design and implementation of LLMs, decision makers must consider the following risks:

1.    Data quality and bias

In order to successfully deploy LLMs, large data inputs are required for development of language patterns. The quality of their output is highly dependent on the data that they use and are trained on. A key example of this is ChatGPT-4. Following the deliberate choice by Open AI, the tool was originally trained on data up to September 2021 so that reinforcement learning from human feedback and safety testing of the model could be successfully completed.

Without such checks and testing, any issues, mistakes or biases within the data may be amplified by LLMs, which will significantly hinder a model’s overall performance and become problematic for software developers.


2.    Ownership of content and intellectual property 

LLMs are generated by content that is developed by others, and a critical issue therefore arises in that the business may be subjected to intellectual property risk. Using such data presents plagiarism issues. However, due to the broad contextual reach of LLMs, exposure of intellectual property to unintended parties also becomes a risk. This can be particularly problematic in the event of private company data being made public.

 CTOs and COOs can work to overcome such risks by implementing robust data classification during training and fine tuning processes within software development stages. Equally, it is the responsibility of leaders of development teams to educate their developers about the potential risk of unintentional disclosure of intellectual property that may arise when using LLMs, and manage the users of the software accordingly. 


3.    Privacy and security  

As with all AI technology, the privacy and security of LLMs is a key concern across the leadership team. Not. only is the business tasked with keeping their own data within LLMs safe, they also need to mitigate against the threat that their data could be used to cause harm. According to the UK’s National Cyber Security Centre (NCSC), there have been ‘some incredible demonstrations of how LLMs can help write malware’. Cybercriminals are motivated by hacking LLMs and managing this risk is therefore crucial for the software development, use and implementation.


In essence, LLM security is data security. As a result, software developers need to have security measures in place to ensure they can safely use data when programming and avoid the risk of accidentally leaking confidential information and propagating it to others. In order to mitigate against the software risks associated with LLMs, CTOs and especially COOs must first understand how their software developers are using AI and tools to carry out their work. Following that, they can create and implement governance policies to minimise the potential risks.  It should also be a priority to have a correctly configured code repository in place to avoid risks associated with data quality and biases. Creating software from a single source of truth (SSOT) ensures that firms are not working from false data sets and are less likely to have problems later in a software development’s life cycle. 


If you would like to learn more about how Synetec can help your company with managing the software risks associated with deploying LLMs, contact us to learn more.












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