The Top 3 Trends in LLM and AI Security

How can enterprises accelerate AI adoption in a safe and secure manner?

Originally published by Enkrypt AI.

Written by Sahil Agarwal.

As a Math PhD scholar and AI expert, I’ve had the pleasure of attending numerous industry conferences and listening to Fortune 100 executives on the latest AI trends. After listening to countless hours, here are the 3 major trends that keep emerging when it comes to LLM and AI security.

1 - Security versus Risk

The choice between AI security and risk is an easy decision – especially if you are a C-level executive. With the ever-growing number of threats emerging daily, it’s a foregone conclusion that you’ll be hacked. It’s just a matter of time. What matters more is the risk impact from those breaches and the degree of difficulty to recover – both financially and legally.

To best manage risk, you should find a solution that automatically and dynamically detects and removes LLM vulnerabilities. Your risk posture improves even more if you can show cost savings for such actions in real-time.

2 – Risk Tolerance Varies by Vertical

Another trend is the huge differences in industry risk tolerance. For example, a Life Sciences enterprise is laser focused on their custom-built AI chatbot. They get daily reports on the bias, and toxicity risk scores from this use case alone to ensure an optimal patient experience.

Whereas an insurance company I’ve spoken to on the east coast is maniacal about the safe and compliant use of AI for processing sensitive claims data. Their custom LLM is more prone to bias than industry averages, so further testing and alignment is needed to ensure information is accurate.

How can you ensure your risk is constantly reducing the more your AI is used? Find a solution that doesn’t just test against the usual threat suspects, but one that can surface vulnerabilities from additional prompting against your internal and external data sets. So over time, your AI applications become safer, improving your risk profile and corporate social responsibility.

3 - Minimum Requirement: Red Teaming

AI experts for every industry agree: if you had to choose just one thing to improve your AI security posture, then do Red Teaming testing (i.e. AI threat detection). It’s the biggest trend I’ve been hearing for over the past year, with the audience voices becoming louder in the last few months.

There are obvious differences in testing services when it comes to Red Teaming. One big thing we suggest is conducting both dynamic and static testing.

Dynamic testing involves injecting a variety of real-time, iterative prompts that become increasingly sharper based on the LLM use case. Such methods detect significantly more vulnerabilities than static testing alone.

Summary

Overall, it’s clear that everyone has the same goal: to accelerate AI adoption in a safe and secure manner while gaining a competitive advantage. As AI matures and more applications are deployed in production, it’s imperative that we harness its potential for the greater good.