AI + a16z

Augmenting Incident Response with LLMs

Jul 26, 2024

Dean de Beer, cofounder and CTO of Command Zero, dives into how large language models can revolutionize cybersecurity. He shares insights on the challenges of scaling LLMs, including infrastructure limitations and model appropriateness for specific use cases. Dean emphasizes the importance of effective entity extraction and memory management to enhance model performance. The discussion also touches on the evolution of cybercrime and the need for scalable solutions in incident response, underscoring the critical intersection of AI and cybersecurity.

40:14

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Dean de Beer

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Quick takeaways

Training large language models on security data enhances incident response but requires careful selection based on specific use cases.

User-centered design and minimizing latency are crucial for improving digital experiences, especially when integrating complex AI technologies.

Deep dives

User Experience and Frustration

Creating an effective user interface is crucial when developing a product, particularly in technology-infused industries. Users often experience frustration with waiting times during operations, such as the classic example of waiting for elevators. This concept translates directly into digital experiences, where delays in data presentation can lead to significant dissatisfaction among users. Consequently, the importance of minimizing latency and streamlining user interactions is underscored, especially when combining user-centered design with complex technologies like language models.

Intro

2min

Navigating Cybersecurity Challenges

10min

The Evolution of Cybercrime: From Hobbyists to Organized Enterprises

3min

Enhancing Incident Response with LLMs

21min

Practical Insights for Building with Language Models

4min

In this episode of the AI + a16z podcast, Command Zero cofounder and CTO Dean de Beer joins a16z's Joel de la Garza and Derrick Harris to discuss the benefits of training large language models on security data, as well as the myriad factors product teams need to consider when building on LLMs.

Here's an excerpt of Dean discussing the challenges and concerns around scaling up LLMs:

"Scaling out infrastructure has a lot of limitations: the APIs you're using, tokens, inbound and outbound, the cost associated with that — the nuances of the models, if you will. And not all models are created equal, and they oftentimes are very good for specific use cases and they might not be appropriate for your use case, which is why we tend to use a lot of different models for our use cases . . .

"So your use cases will heavily determine the models that you're going to use. Very quickly, you'll find that you'll be spending more time on the adjacent technologies or infrastructure. So, memory management for models. How do you go beyond the context window for a model? How do you maintain the context of the data, when given back to the model? How do you do entity extraction so that the model understands that there are certain entities that it needs to prioritize when looking at new data? How do you leverage semantic search as something to augment the capabilities of the model and the data that you're ingesting?

"That's where we have found that we spend a lot more of our time today than on the models themselves. We have found a good combination of models that run our use cases; we augment them with those adjacent technologies."

Learn more:

The Cuckoo's Egg

1995 Citigroup hack

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Dean de Beer

Joel de la Garza

Derrick Harris

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