We all know how big a role observability plays in DevOps, but using it is easier said than done. Logz.io, with its cloud-native SaaS observability platform, Open 360, pulls together logs and metrics. It traces data from open source monitoring programs such as Prometheus, OpenTelemetry, Fluetnd with OpenSearch above it all to help you see what’s what in your systems.

Now Logz.io has added IQ Assistant, an AI-driven chatbot, to make it easier to find your important monitoring data and make sense of it.

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Which of the following best describes how you currently monitor and manage systems across your hybrid cloud environment?(Required)

Multiple separate tools for different environments

Multiple separate tools for different monitoring and management functions

Single consolidated dashboard

Custom built monitoring and management solution

No full monitoring and management solution

Don’t know

How valuable would it be for your monitoring and management solution to incorporate predictive analytics to help you identify and address system issues that are critical and not waste time or effort working through all the data and “noise” that is not critical?(Required)

Extremely valuable

Moderately valuable

Somewhat valuable

Slightly valuable

Not valuable

How do you most typically address potential system issues BEFORE they impact your operations?(Required)

Automated workflows and playbooks to remediate potential issues as soon as they are detected

An orchestrated response process that leverages automation, AI, and human expertise to rapidly respond to potential issues

Advanced monitoring tools such as predictive analytics and anomaly detection to proactively identify and address potential issues

Respond to issues as they arise, using manual processes and troubleshooting techniques

A combination of automated remediation, orchestrated response, and/or proactive monitoring to address potential system issues

Don’t know

Other

What methods does your organization use to control and monitor cloud/IT spending?(Required)

Centralized procurement processes

Pre-approved budget thresholds

Automated cost monitoring tools

Department-level spending controls

Regular cost audits and reviews

Cloud cost optimization platform

Approval workflows for resource provisioning

Native financial operations tools provided by the cloud provider

No formal controls in place

Don’t know

Other

Please specify:

Which methods do you rely on most to detect security vulnerabilities in your systems?(Required)

Automated scanning tools

Manual assessments

Vendor notifications

Threat modeling (for early detection)

None of these

Don’t know

Other

Please specify:

What are the TWO most common ways you prioritize vulnerabilities to address within your environment?(Required)

Based on potential impact

Based on exploitability

Regulatory compliance requirements

Based on CVSS scores

Risk-based analysis of multiple factors

First-in-first-out approach

No formal prioritization process

Don’t know

How do you typically handle system updates and security patches?(Required)

Automated updates and patches

Manually review and apply updates and patches on a regular schedule

A combination of automation and manual review

Apply updates and patches on an as-needed basis.

Outsourced update and patch management

No defined process

Don’t know

Other

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Logz.io has been working on the chatbot’s back engine, AI-Driven Observability IQ, for some time. The goal is to provide not just quick answers for real-time issues via the Apache Lucerne search engine, but to provide context, conclusions and response steps.

I like this plan. I like this plan a lot.

What appeals to me about Logz.io’s AI chatbot is that it’s not just a passive AI that relies on known errors and proven troubleshooting steps. Rather, the AI-driven SaaS application combines a proprietary Logz.io AI with a large language model (LLM) to learn from an environment and user actions. That means it can get better at its job of analyzing your environment and providing you with the best mitigation suggestions. If it works, this will help you resolve issues faster and reduce your mean time to remediation (MTTR).

Other current Open 360 platform AI-based features include:

• Anomaly detection for App 360: Uses AI to detect service anomalies, providing real-time insights into the performance of services, operations, metrics, and endpoints.
• Alert recommendations: Employs supervised machine learning to model user actions and advise subsequent users facing similar issues.
• Data optimization hub: Offers AI-based observability pipeline analytics, providing automated recommendations for managing data volumes and costs effectively.
• Cognitive insights: Uses generative AI to highlight important log data, linking to related information and best practices for quick issue resolution.

The chatbot is only the start. Logz.io is formalizing its AI strategy to include several additional features, and it promises announcements throughout 2024 and beyond.

So, if you want to use the data from your open source observability programs better and more timely, check it out. As Logz.io CTO Asaf Yigal pointed out, it won’t replace your DevOps team, but it will make it easier for the team to do its job.