Introduction to AI Security
As the adoption of AI accelerates, organisations may overlook the importance of securing their Gen AI products. Companies must validate and secure the underlying large language models (LLMs) to prevent malicious actors from exploiting these technologies. Furthermore, AI itself should be able to recognise when it is being used for criminal purposes.
Understanding the Risks
Enhanced observability and monitoring of model behaviours, along with a focus on data lineage can help identify when LLMs have been compromised. These techniques are crucial in strengthening the security of an organisation’s Gen AI products. Additionally, new debugging techniques can ensure optimal performance for those products.
The Importance of Caution
It’s essential that given the rapid pace of adoption, organisations should take a more cautious approach when developing or implementing LLMs to safeguard their investments in AI.
Establishing Guardrails
The implementation 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 and, importantly, how this data will be interpreted and communicated back to customers. Due to their non-deterministic nature, LLM applications can unpredictably “hallucinate”, generating inaccurate, irrelevant, or potentially harmful responses. To mitigate this risk, organisations should establish guardrails to prevent LLMs from absorbing and relaying illegal or dangerous information.
Monitoring for Malicious Intent
It’s also crucial for AI systems to recognise 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 guardrails set by its application team. This poses a significant risk of exposing sensitive information.
Validation through Data Lineage
The nature of threats to an organisation’s security – and that of its data – continues to evolve. As a result, LLMs are at risk of being hacked and being fed false data, which can distort their responses. While it’s 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.
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 use 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.
Conclusion
Since the release of LLMs like GPT, LaMDA, LLaMA, and several others, Gen AI has quickly become more integral to aspects of business, finance, security, and research than ever before. 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.
FAQs
Q: What is the main risk associated with Gen AI products?
A: The main risk is that organisations may overlook the importance of securing their Gen AI products, making them vulnerable to malicious actors.
Q: How can organisations strengthen the security of their Gen AI products?
A: By implementing enhanced observability and monitoring of model behaviours, focusing on data lineage, and using new debugging techniques.
Q: What is the role of data lineage in Gen AI security?
A: Data lineage plays a vital role in tracking the origins and movement of data throughout its lifecycle, helping to prevent LLMs from being hacked and fed false data.
Q: How can DevOps debug AI products and services?
A: By using techniques such as clustering, which allows them to group events to identify trends and aid in debugging.
Q: Where can I learn more about AI and big data from industry leaders?
A: You can check out the AI & Big Data Expo taking place in Amsterdam, California, and London, which is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.
