From vision to reality: Your guide to using generative AI to improve operational resilience

Generative AI (GAI) is at the forefront of nearly everyone’s minds. Consumers want to use it to improve their digital experiences, organizations want to use it to cut costs and be more efficient, and employees are learning how to harness its power to make their jobs easier. As GAI rapidly matures, it’s essential to dream big about its possibilities and be realistic about implementing it strategically and impactfully in ways that can improve your operations.

As an enterprise IT leader, your teams spend countless hours keeping systems up and running and are monitoring and resolving issues in real time. Most likely, your organization has already created a mountain of telemetry data that continues to grow as your organization looks to identify application bugs, detect user errors, and, most importantly, identify cyberattacks before they impact your business.

Tapping into GAI can help.

Using GAI to get answers that matter

The key to using GAI for a business case like improving operational resilience lies in asking it for answers in the context of your specific data and signals. GAI chatbots like ChatGPT are extraordinarily helpful in answering questions. However, they leverage large language models (LLM) that deliver answers based on publicly available data from the entire internet. If you ask a question about a specific instance you’re seeing in your environment, you’ll get an answer based on generic information that may not be relevant to your unique situation.

The power of GAI for your organization in real-world scenarios lies in bringing your proprietary data into your LLM.

Integrating GAI in observability and security workflows

The good news in all of this is that you have already built your in-house repository of data that can be used to train your observability and security monitoring learning capabilities for your organization. This infrastructure will allow you to pivot your current automated operations monitoring and response capabilities into a much more robust AIOps capability. This will augment security and observability workflows and give your teams better visibility into IT systems faster so they can remediate incidents and increase your organization’s operational resilience. And that’s just the tip of the iceberg of what GAI can do.

Who doesn’t love a streamlined process? Instead of poring over telemetry data — like metrics, logs, and traces — to identify an app or security issue, your IT team can securely pass the data to a GAI tool that can quickly analyze the data to provide insight on any anomalies, problems, or potential problems — and give ways to resolve them. More importantly, the more data you feed it, the more it will learn about the ideal baseline process. This will allow the model to not just react to events, but to identify changes in patterns before the events occur.

Say you’re a retailer and your site’s shopping cart stops working. Customers can’t complete purchases and your sales are dropping. Instead of reacting to the events and automating the response to limit the business impact, you now have the ability to feed that same data into your GAI to begin to learn the patterns and automate your responses. This translates to less customer impact and more sales. It’s a win-win.

Or perhaps one of your systems is running slowly and no matter how fast your IT teams analyze the information, it takes time to figure out the root cause. By creating the model and having it learn from the full volume of data that you are capturing, it can identify the pattern faster than your team. The result: You will know much sooner if it is a bug, an error, or malware that’s causing things to run slowly — and you can act quickly to address the problem.

To do all this, you must feed your data into the GAI technology for highly relevant business insights and answers—and, of course, do so efficiently with the highest possible level of privacy. This gives your teams the best answers based on the proper context.

GAI-driven operational resilience starts with your data platform

You might be wondering how to do all of this. To use GAI to get the insights you need for improved operational resilience, you must first implement a unified data platform that seamlessly transforms all of your data into outcomes and all of your questions into answers continuously and in real time.

It should capture all your data — and make it searchable, analyzable, explorable, and visualizable. And it should be built on a distributed architecture, so your data store can run across multiple servers and locations, improving performance, preventing single points of failure, and ensuring business continuity in case of interruption.

When you have this foundation in place, your team can leverage the data stored in the unified platform and safely pass anonymized data into your GAI of choice to quickly surface the relevant information they need when they need it.

Advanced technology like the Elasticsearch Relevance Engine™ (ESRE™) enables your developers to create custom, highly relevant AI search applications. Additionally, users of every skill level can leverage a GAI assistant powered by ESRE for their security and observability needs too. With pre-built GAI prompts that pass editable, organization-specific context to the LLM, all analysts can tailor answers to their specific use cases. Your teams can get alerts, workflow suggestions, integration advice, and more to help keep your systems running seamlessly.

Push the boundaries of what’s possible

You’ve likely spent years perfecting your processes and finding the security and observability solutions that work for your team. Now, with GAI at your fingertips and the flexibility to create custom applications that integrate with it, you have the power to dramatically advance those workflows and solutions to find answers quickly, solve problems efficiently, and improve operational resilience.

Learn how Elastic can help your team create GAI apps to improve your company’s operational resilience.

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