The increasing adoption of AI has led to a rising concern that organisations may neglect the security of their General AI (Gen AI) products. To prevent malicious actors from exploiting these technologies, companies must validate and secure the underlying large language models (LLMs). Moreover, AI systems should be capable of recognizing when they are being utilized for illicit purposes.

To enhance the security of Gen AI products, organisations can employ techniques such as improved observability and monitoring of model behaviours, as well as a focus on data lineage. These methods can help identify compromised LLMs and are crucial in strengthening the security of an organisation’s Gen AI products. Additionally, novel debugging techniques can ensure optimal performance for these products.

Given the rapid pace of AI adoption, organisations should exercise caution when developing or implementing LLMs to safeguard their investments in AI.

Implementing Safeguards

The integration of new Gen AI products significantly increases the volume of data flowing through businesses today. Organisations must be aware of the type of data they provide to the LLMs that power their AI products, as well as how this data will be interpreted and communicated back to customers.

Due to their non-deterministic nature, LLM applications can unpredictably generate inaccurate, irrelevant, or potentially harmful responses. To mitigate this risk, organisations should establish safeguards to prevent LLMs from absorbing and relaying illegal or dangerous information.

Detecting Malicious Intent

It is essential for AI systems to recognize when they are being exploited for malicious purposes. User-facing LLMs, such as chatbots, are particularly vulnerable to attacks like jailbreaking, where an attacker issues a malicious prompt that tricks the LLM into bypassing the moderation safeguards set by its application team, posing a significant risk of exposing sensitive information.

Monitoring model behaviours for potential security vulnerabilities or malicious attacks is vital. LLM observability plays a critical role in enhancing the security of LLM applications. By tracking access patterns, input data, and model outputs, observability tools can detect anomalies that may indicate data leaks or adversarial attacks, enabling data scientists and security teams to proactively identify and mitigate security threats, protecting sensitive data and ensuring the integrity of LLM applications.

Validation through Data Lineage

The nature of threats to an organisation’s security – and that of its data – is constantly evolving. As a result, LLMs are at risk of being hacked and being fed false data, which can distort their responses. While it is necessary to implement measures to prevent LLMs from being breached, it is equally important to closely monitor data sources to ensure they remain uncorrupted.

In this context, data lineage will play a vital role in tracking the origins and movement of data throughout its lifecycle. By questioning the security and authenticity of the data, as well as the validity of the data libraries and dependencies that support the LLM, teams can critically assess the LLM data and accurately determine its source. Consequently, data lineage processes and investigations will enable teams to validate all new LLM data before integrating it into their Gen AI products.

A Clustering Approach to Debugging

Ensuring the security of AI products is a key consideration, but organisations must also maintain ongoing performance to maximise their return on investment. DevOps can utilise techniques such as clustering, which allows them to group events to identify trends, aiding in the debugging of AI products and services.

For instance, when analysing a chatbot’s performance to pinpoint inaccurate responses, clustering can be used to group the most commonly asked questions, helping determine which questions are receiving incorrect answers. By identifying trends among sets of questions that are otherwise different and unrelated, teams can better understand the issue at hand.

A streamlined and centralised method of collecting and analysing clusters of data, the technique helps save time and resources, enabling DevOps to drill down to the root of a problem and address it effectively. As a result, this ability to fix bugs both in the lab and in real-world scenarios improves the overall performance of a company’s AI products.

Since the release of LLMs like GPT, LaMDA, LLaMA, and several others, Gen AI has become increasingly integral to aspects of business, finance, security, and research. In their rush to implement the latest Gen AI products, however, organisations must remain mindful of security and performance. A compromised or bug-ridden product could be, at best, an expensive liability and, at worst, illegal and potentially dangerous. Data lineage, observability, and debugging are vital to the successful performance of any Gen AI investment.

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