Safeguarding your AI Integration

Imanuel Babadostov
Mar 28, 2024
5 min read

In the cutting-edge landscape of artificial intelligence, Large Language Models (LLMs) stand at the forefront for practical business integration, possibly transforming the business world with unparalleled capabilities. As we harness LLMs' power for writing documents, application code and even medical diagnostics, we must also confront the potential challenges that arise in this technological revolution. In this article, we delve into the top 5 security issues surrounding the use of LLMs in these critical domains and propose suggested solutions to address them.

Before we get into it, let's talk about what a Large Language Model (LLM) is and what it is not. What it is, is an advanced artificial intelligence system that utilizes deep learning techniques and massive datasets (generally public ones) to process and understand natural language. LLMs are designed to generate human-like text, engage in conversation, and perform various language-related tasks, making them valuable tools for applications such as natural language processing, language translation, text generation, on the fly customer communication and more. What it is NOT however, is able to understand if the information being provided is correct, nor does it have common sense to understand intent by “reading into” questions, or deduce new and original ideas. It is also not able to tell if the training data was legally obtained, is complete, or has been tampered with. So you can imagine how a polished sounding answer may be based on really poor data.

So lets get into it, and figure out what your business has to worry about if you decide to use one of these wonder-tools to increase your revenue. Do keep in mind, this isn't meant to put down any specific product, or discourage any users, just to shed light at some problems and hopefully save you some headaches.

1. Data Privacy and Confidentiality

The cornerstone of AI's potency lies in the vast amounts of data it processes. However, when utilizing LLMs for software development and communication, sensitive information inevitably comes into play. Without clearly defined training data sources an integration of such a product into the business could land you into a situation where security flaws are written into your products code either from sloppy source code, or from intentional supply chain poisoning. In the opposite direction, customer data is sent out and permanently integrated into the LLM training data, be it through prompts or an API. Lastly, like any other API, yours might be automatically cleaning up data as it goes, but might be an avenue to pull data directly from the source database on the network, used to craft these requests.

Suggested Solution: Implement strong encryption algorithms for data storage and transmission, employing multi-factor authentication to limit access to authorized personnel only. Review your API agreements from your AI service provider to see what data should be accessed, and how long it is kept or whom it is shared with while the vendor has it. By Anonymizing the source of the data used, the organization can limit possible data leaks from the get-go and ensure no future leaks.

2. Bias and Fairness

LLMs' proficiency depends on the datasets they learn from, potentially leading to biased outputs. In the business world, biased software code or medical diagnoses can have profound consequences. Bias might be inadvertently perpetuated from the training data, exacerbating existing inequalities and yielding unfair outcomes. To address this issue, organizations must adopt ethical practices, conduct bias audits, and prioritize diverse, representative datasets to ensure fairness in LLM-driven applications.

Suggested Solution: Establish diverse and inclusive datasets for training LLMs, incorporating data from various demographic groups and ensuring a balanced representation. Develop fairness-aware learning algorithms that actively counteract biases during training and utilize techniques like adversarial debiasing to reduce unwanted biases in the model.

3. Adversarial Attacks

As AI becomes increasingly integral to businesses, adversaries seek to exploit LLM vulnerabilities. Adversarial attacks manipulate LLMs to produce incorrect results, endangering critical operations and causing financial or health-related harm.

Understanding and bolstering LLMs' resilience against such attacks is vital. Robust testing, adversarial training, and continuous monitoring are imperative to safeguard against these threats that take a natural input/prompt and cause an unexpected result to take place. In the case of a LLM, you will obtain outputs that were otherwise filtered out, or restricted. In the classic case of a self-driving car, you might speed up if you saw a stop sign with a properly designed sticker on it.

Suggested Solution: Regularly subject LLMs to adversarial testing using adversarial examples to identify and address weaknesses. Employ AI vulnerability testers like PromptMap or Garak, and invest in adversarial training techniques to improve the model's robustness against adversarial attacks. Continuously update and refine the model to stay ahead of evolving attack methods and where possible, monitor the input and output with the same input validation techniques we all learned from the OWASP top 10. (see more notes here: https://www.wired.com/story/ai-adversarial-attacks/)

4. AI SAAS, The fine print.

Integrating LLM-based AI into everyday business without reviewing the provider's EULA poses significant risks. Data privacy and security concerns arise when sensitive information is exposed, potentially leading to data breaches and legal issues. Intellectual property rights may be compromised if ownership of AI-generated content remains with the provider. Compliance and regulatory violations could occur, leading to penalties. Hidden costs, usage limitations, and vendor lock-in may also surface. Ethical concerns arise if biased or harmful outputs are not adequately controlled.

Solution: mitigate these risks is a comprehensive review of the EULA before integration. Businesses should carefully assess data usage and storage policies, ensuring data protection and compliance with relevant regulations. Negotiating intellectual property ownership terms can safeguard businesses' rights to AI-generated content. Understanding pricing structures and usage limits will prevent unexpected costs and ensure adherence to contractual agreements. Seeking legal expertise during the review process can provide valuable insights and clarity on the EULA's implications. And remember, an API based connection is probably different from the GUI you’ve been looking at during the demo.

5. Garbage-in only makes garbage-out.

Using AI-generated training data to train AI models might seem like an efficient solution at first glance, but it comes with inherent risks and limitations that should not be overlooked. The primary concern lies in the potential for biased and unreliable data. AI-generated data is only as good as the source data it was trained on, and if the source data contains biases, inaccuracies, or gaps, the AI-generated data will inherit and amplify those flaws. As a result, the AI model's performance and decision-making abilities can be compromised, leading to increasingly erroneous outputs and potentially harmful consequences.

Human-generated data is grounded in real experiences and tied to real world scenarios, emotions, and context, enabling AI models to learn from a broader spectrum of situations and respond more appropriately in dynamic, unpredictable environments.

The rapid adoption of AI technology to create new content means that more and more input will be watered down with generated content. Even with a tiny theoretical quality loss rate per generation, one can see a rapid degradation of future generated artifacts if input data is left unchecked. In short, check your sources, and make sure they are based in reality. (Learn more here: https://futurism.com/ai-trained-ai-generated-data-interview)

Conclusion

As the business world eagerly embraces LLMs for software development, customer engagement, and other human interactions, we must confront the paramount security issues that accompany this technological leap. Ensuring data privacy, addressing bias, fortifying against adversarial attacks, promoting model transparency, and overcoming AI hallucinations are central to harnessing LLMs' potential safely and responsibly. By navigating these challenges with vigilance and adopting the suggested solutions, businesses can unlock unprecedented achievements, while keeping risks visible and mitigated.