LLM Risks for Application Development

As large language models (LLMs) like ChatGPT, Llama, Gemini, Claude (just to name a few) continue to advance and become more powerful and intelligent, they are currently being used to build various applications, from chatbots and virtual assistants to content generation tools and code autocompletion.

While LLMs offer incredible capabilities, it's important to understand the potential risks involved in using them to develop applications.

One of the biggest risks of using LLMs is the potential for bias and toxicity in their outputs. These models are trained on vast datasets scraped from the internet, which can include biased, offensive, or factually incorrect content. If not properly filtered and curated, or if not properly trained with a diverse set of factual data, this "data bias" can manifest in the model's generations in the form of discriminatory, racist, sexist, or otherwise harmful language and perspectives. Deploying applications built with biased LLMs could propagate harm and discrimination, have a negative impact on your customers and render your application inappropriate for public use.

AI-enabled applications with bias and toxicity in the data are no different to applications with security vulnerabilities. Customers will eventually shy away from biased and toxic AI-enabled applications.

While Generative LLMs are incredibly fluent at generating human-like text, images, videos and speech, their information isn't necessarily grounded in truth or facts. LLMs can confidently state falsehoods, make up statistics, or misrepresent information based on what patterns they've picked up in their training data. Applications relying on LLM outputs as a key information source run the risk of disseminating misinformation.

Another major risk is the potential for LLMs to inadvertently expose sensitive information in their outputs based on what data they were trained on. Researchers have shown that language models can be prompted to reproduce memorized training data, including personal names, phone numbers and email addresses: first with GPT-2 in 2020, and again with ChatGPT in 2023. If this private information makes its way into application outputs, it could lead to data privacy violations and breaches.

While LLMs can produce highly coherent and contextually relevant text, images, video and speech, their outputs can often be inconsistent across different prompts and generations. The same prompt could receive wildly different responses each time it is given to an LLM, making the results unreliable for applications requiring stable and predictable performance.

A core challenge with LLMs is that they are essentially opaque black boxes that provide little insight into how they arrive at their outputs. For those building applications with LLMs, it can be difficult to fully understand, validate, explain and maintain oversight over the model's reasoning process, making it harder to prevent harmful or undesirable behaviours from emerging. The "Black Box" in LLMs using Deep Learning Methods to train their models is a risk and concern because Humans cannot explain how the output was derived.

The challenge with LLMs is that they contain billions of parameters and require immense computational resources to run. Efficiently scaling and deploying LLM-powered applications cost-effectively and sustainably presents major engineering challenges, especially for resource-constrained organizations.

Finally, building applications that rely on LLMs raises broader ethical questions around issues like automation and job displacement, intellectual property, consent over training data, and centralization of AI capabilities among a few big tech companies. Using LLMs could contribute to the concentration of power and lack of public accountability.

If you are using an LLM for internal drafting, summarizing meeting notes or helping developers with boilerplate, and no output reaches a customer or a decision without a person reading it first, most of this list is overkill. Set an acceptable use policy, tell staff not to paste customer or confidential data into public tools, and pick a vendor plan that does not train on your inputs. That is enough.

A full bias evaluation, explainability review and pre-deployment test program only earns its cost when the model output is customer-facing, automated or tied to a regulated decision such as credit, health, hiring or fraud. If you are still deciding whether the feature is worth building, run a limited pilot with clear success criteria before investing in governance tooling.

The exception is data exposure: that risk applies from day one, whatever the use case, so the privacy controls in the table are not optional even for internal use.

While large language models offer immense potential for building powerful applications, developers must carefully weigh and manage these risks. Some possible risk mitigation strategies include, but are not limited to:

As LLMs continue advancing, developers will need to prioritize responsible development practices and security to harness their capabilities while mitigating potential harm. Open communication and collaborations between AI labs, tech companies, academia, and policymakers will be key for building safe, responsible and trustworthy LLM-powered applications.

IRM Consulting & Advisory provides Strategic and Tactical Advice on how to mitigate risks associated with using AI to build responsible applications, products and services.

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