Logz.io has integrated the ChatGPT generative artificial intelligence (AI) platform with its Open 360 observability platform to extend its automated recommendation capabilities to reduce mean-time-to-remediation.
ChatGPT, developed by OpenAI, is based on a large language model that Logz.io uses to enrich crowdsourced data. That approach enables ChatGPT to surface links to related information and best practices for resolving IT issues.
Logz.io CTO Asaf Yigal said the company has integrated ChatGPT with its existing AI engine, dubbed Cognitive Insights, that applies machine learning algorithms to identify anomalies indicative of potentially critical IT issues. The company expects to be able to add other generative AI capabilities as additional advances are made, he added.
The Logz.io Open 360 platform is based on open source OpenSearch, Prometheus and Jaeger software that the company has infused with machine learning algorithms. The integration with ChatGPT is enabled via an application programming interface (API) that OpenAI has provided.
The overall goal is to employ AI to surface the most relevant issues a DevOps team needs to focus on without having to search huge volume of logs, tracing and metrics data, said Yigal. Generative AI platforms solve a fundamental cognitive load problem created today as DevOps teams collect more data, noted Yial. The more data a DevOps team collects, the harder it becomes to determine what’s going on in an IT environment, said Yigal. He added that generative AI platforms make it possible to analyze massive amounts of data at a scale that isn’t otherwise achievable.
AI tools should make it possible to manage IT at a level of scale that eliminates many of the low-level data engineering and analytics tasks that previously required manual effort from a DevOps engineering team. It’s not clear how many other DevOps tools and platforms will be invoking generative AI capabilities in the months ahead, but DevOps teams should assume that large language models will soon be augmenting DevOps teams in ways that previously would have been thought unlikely.
For example, discovering vulnerabilities in software running in production environments is about to become trivial as artificial intelligence (AI) platforms such as ChatGPT evolve. A generative AI platform can identify code vulnerabilities in much the same way as a traditional scanning tool. An AI platform, however, could theoretically monitor code repositories and scan them for vulnerabilities as updates and commits are being made in real-time. DevOps teams should be able to use generative AI platforms to discover and remediate these issues before code is deployed in a production environment.
It’s just as probable bad actors will use the same platform to achieve the same goal, so from that perspective, many DevOps teams will now find themselves in an AI arms race to improve DevSecOps workflows.
There are still lots of issues to be worked out in terms of the overall accuracy of generative AI platforms, but they will only become more intelligent as processors continue to advance. The issue now will be determining precisely where the man-machine interface for DevOps teams is today and in the future.
