Behind the scenes of Elastic Security’s generative AI features

Elastic has long been developing machine learning (ML) and AI-powered security detections. We constantly bring in new technologies when available to help make our users’ lives easier. So, with the rise of generative AI (GenAI), we have developed even more Elastic Security features to use this powerful, new technology. Among those are:

• Elastic AI Assistant for Security: Our chatbot is built to answer questions about Elastic Security, help generate or translate natural language queries to ES|QL, provide context on alerts, and integrate with custom knowledge sources for bespoke organizational questions.

• Attack Discovery (AD): This feature reviews alerts and discovers any active attacks, prioritizing and summarizing them for the user.

• Automatic Import: This feature creates custom integrations based on a few sample log lines, alleviating the burden of creating parsing logic and normalization pipelines.

For those familiar with GenAI development, the area is rapidly growing. At Elastic, we are in a unique position in that we have real and proven GenAI-powered products that are serving users at scale — not just tinkering or as proof-of-concepts. This unique position is two-fold — we closely partner with and use leading GenAI development frameworks. In fact, we were named #2 in the Top 5 LangGraph Agents in Production 2024 by LangChain. We were also named GenAI Infrastructure and Data Partner of the Year by Amazon Web Services.

As mentioned in the previous section, we started using LangSmith and LangGraph together, enabling us to capture traces of each LLM call. On top of that, we developed a tailored evaluation framework, which became an essential tool in our development process. As we developed more improvements, there was more to consider. For example, which LLM model should we pick? We have a recommended LLM matrix as an outcome of those tests. And which prompts and variations perform the best?

Here are the components of the evaluation framework (which we will walk through in detail in following sections):

1. Test scenarios: Diverse scenarios that the user may come across and each with its own gold standard examples

2. Curated test dataset: An accumulation of gold standard examples covering various test scenarios

3. Tracing: Capturing the AI Agent execution graph as well as LLM calls and run metadata

4. Evaluation rubrics: Various behavior rubrics; for example, does this response seem like a hallucination? Does this response capture all the known user IDs in the query?

5. Scoring mechanism: A mathematical way to calculate final scores based on business requirements or desired heuristics

First, we’ll go through the test scenarios and curated test dataset, as well as how we easily created and tracked them with LangSmith.

If the output is qualitatively good enough, we add it to our curated test dataset. Since we are using LangGraph and LangSmith, adding an example to a dataset is further simplified as the LangSmith UI has ways to add an existing output to a dataset.

It is important to have the scenarios and test datasets in order to have a baseline of “goodness” of GenAI outputs. But we didn’t immediately get to this point; the initial effort of creating scenarios can be time-consuming, and since LLM outputs can have a high variance, some of the selection of curated examples can be difficult. 

However, this was a well-invested effort on an ongoing basis in order to know if our improvements are actually making the product better. This also enabled us to run automated LLM evaluations (“LLM-as-judge”) and experiment whenever we deploy a new change. The prompts used for LLM-as-judge can also be tuned. For simplicity, we will refer to both the prompts used to generate outputs as well as the “judge” outputs as prompts in this article.

Next, we’ll touch on the tracing components. As mentioned above, we use LangGraph to design and run our AI Agent workflows behind the scenes, while LangSmith provides the tracing capabilities as well as streamlined tools for us to create test datasets and run evaluations.

For completeness, the following image illustrates the high-level workflow of how the Elastic Security AI Agents work — from when it gets a user request to when it generates the response. We use Elasticsearch as a vector database to power retrieval augmented generation (RAG) functionality. 

Rubrics are a way of evaluating a defined “desired behavior” of the LLM outputs and can contain many items — each responsible for checking a subset of desired behaviors. For instance, this could include the desired behavior of “the LLM should respond with plain language” and the evaluation rubric including the item “is the response written in plain language?”

For Elastic Security, this is an example of our rubric prompts and contains many evaluation items:

5. Evaluate the value of the "summaryMarkdown" field of all the "attackDiscoveries" in the submission JSON. Are the values of "summaryMarkdown" in the "submission" at least partially similar to that of the "expected response", regardless of the order in which they appear, and summarize the same incident(s)? Summarize each summary, and explain your answer with lettered steps.

6. Evaluate the value of the "title" field of all the "attackDiscoveries" in the submission json. Are the "title" values in the submission at least partially similar to the title(s) of the "expected response", regardless of the order in which they appear, and mention the same incident(s)?

With this rubric, we then use an LLM evaluator to check if the responses satisfy the rubric as illustrated in the image below. This is done directly in the flow between when a user submits their query and when the response is displayed. The rubric prompt checks in real time if the LLM output is good enough; if not, it will go back to the initial generator LLM to regenerate a response. See an example LangSmith trace.

For your use case, you may want to compare a few LLMs to determine which ones work the best for you. In our case, with this framework, we can evaluate an “evaluator” LLM as well as rubric prompts quantitatively as well.

Lastly, the scoring mechanism can create a final score based on your defined behaviors. For example, if you want to weigh a certain rubric higher, then you can multiply that score by a weight. In our case, we wanted to have a threshold of accuracy, so we would drop a prompt if the accuracy was lower than 85%. This is doable with your program of choice.

Putting it all together, you’d have an easily understandable results table — and you’d be able to see at a glance:

• Is this new prompt doing better? Is it doing better on certain rubric items or not?

  • Rubrics themselves can also be treated as prompts to improve on! For example, we tightened up the wording of the rubrics in one improvement, and when we reran this framework, it confirmed that they performed better.

• Which LLM was best at a specific task?

• Which LLM has that highest score per our scoring mechanism? 

  • If you’re troubleshooting or tweaking the scoring mechanism, setting the results table up this way helps to easily find what might have gone wrong.

In this blog, we’ve walked through our GenAI development process — particularly how we can improve prompts and compare different configurations like selecting different LLMs, which is extensible to comparing and selecting all components, such as vector databases. This is the backing behind future improvements to Attack Discovery, Elastic AI Assistant, and more.

If you’re a user of Attack Discovery or Elastic AI Assistant for Security, thank you for using our tools. We look forward to your feedback! If you’re interested in learning more and using AI to speed up attack triage, check out the Attack Discovery page and Security Labs articles. 

Lastly, if you’re a GenAI developer, we hope that this article can help you with structuring an evaluation workflow. We’re also continuously improving on our GenAI development systems and looking forward to sharing more. 

If you’re interested in learning more about how Elastic enables and powers GenAI tools around the world, check out our Elasticsearch Labs articles.

The release and timing of any features or functionality described in this post remain at Elastic's sole discretion. Any features or functionality not currently available may not be delivered on time or at all.

In this blog post, we may have used or referred to third party generative AI tools, which are owned and operated by their respective owners. Elastic does not have any control over the third party tools and we have no responsibility or liability for their content, operation or use, nor for any loss or damage that may arise from your use of such tools. Please exercise caution when using AI tools with personal, sensitive or confidential information. Any data you submit may be used for AI training or other purposes. There is no guarantee that information you provide will be kept secure or confidential. You should familiarize yourself with the privacy practices and terms of use of any generative AI tools prior to use. 

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