ESG research: leveraging observability data for DevSecOps

4 MIN READ
MIN READ

There’s a call throughout the industry to shift security left in the software development lifecycle, expanding the DevOps methodologies that have been growing in adoption for more than a decade. DevSecOps is based on the idea that security is not an afterthought. Rather, it is a collaborative process that must be integrated from the start of the development process. To make this collaboration possible, every team—from security to development to operations—needs tools and a common language to communicate and succeed together. 

At Mezmo, some of our most successful customers are adopting DevSecOps methodologies to ship and maintain more resilient and secure products. For them, using observability data as a single source of truth has been crucial to the success of these initiatives. So, we partnered with Enterprise Strategy Group (ESG) to understand how other enterprise companies feel about DevSecOps and how they can leverage their observability data to move the needle forward. Check out the highlights of their findings below and download the full report here.

DevSecOps adoption

As organizations adopt modern software development processes in the cloud, they are looking to incorporate security processes and controls into their software development lifecycle (SDLC)

Though only 22% of organizations said they have developed a DevSecOps strategy, 62% of organizations have a plan or are evaluating use cases for it, showing significant future growth. Across the board, 82% of respondents say accelerating the mean time to resolve security alerts and incidents is a top priority for their security team. Of those who are already leveraging DevSecOps, an overwhelming 95% report a positive impact on accelerating incident detection and 96% on response efforts. 

DevSecOps challenges today

Developer efficiency is all about shortening feedback loops so that developers can get the information they need to take action. It’s essential that they get accurate, timely information at every stage of the SDLC so that they can write high quality code and address issues once that code is in production. 

As organizations increase the speed and volume of releases to serve more customers, they are collecting huge volumes of data. Almost one-third of organizations surveyed capture hundreds of terabytes of application data per month. But, it’s costly to collect this much data just for the sake of collecting it. In fact, 69% of respondents admit that they don’t capture certain data sources because of the high cost of storage and retention. This is problematic if there is an incident and the organization has incomplete data for a thorough analysis and timely response, not to mention potential compliance issues. 

For those who have adopted DevSecOps, data capture and analysis are at the top of the list of challenges related to implementing it. 

Observability and DevSecOps

“DevSecOps has been a challenge because traditional security methods are too disruptive to processes; organizations need solutions that work within developer workflows and tools along with their cloud-native tech stack,” said Melinda Marks, Senior Analyst at ESG. “Leveraging observability data can help drive efficiency by utilizing data to provide insight for better security processes, policies, and faster incident response.”

The study shows that 91% of organizations are using more than one tool to get the most value out of their data, which makes it difficult for multiple teams to have access to the data they need to do their jobs. Not having a “single source of truth” is reported as the greatest challenge holding teams back.

Eighty-seven percent of respondents said that they are using open source tools as part or all of their observability stack today because they are more customizable. But, 84% believe it will become challenging to manage, adopt, and scale with these solutions. Nearly all survey respondents (98%), with titles across teams, from application developers to IT and security professionals, said they will likely investigate a managed observability solution over the next 12 months. This reflects what we’re hearing in the market and from leaders like Gartner, who recently showed observability on the decline from the “height of inflated expectations” in their Hype Cycle for Monitoring, Observability and Cloud Operations, 2022. It’s clear that the observability solutions that enterprises have used for the last decade aren’t providing what they need as they adopt modern ways of working, like DevSecOps. 

For more insight about DevSecOps and the role observability plays in successful adoption, download the report or reach out to an expert at Mezmo to find out how to modernize your observability practice at outreach@mezmo.com

Table of Contents

    Share Article

    RSS Feed

    Next blog post
    You're viewing our latest blog post.
    Previous blog post
    You're viewing our oldest blog post.
    Mezmo + Catchpoint deliver observability SREs can rely on
    Mezmo’s AI-powered Site Reliability Engineering (SRE) agent for Root Cause Analysis (RCA)
    What is Active Telemetry
    Launching an agentic SRE for root cause analysis
    Paving the way for a new era: Mezmo's Active Telemetry
    The Answer to SRE Agent Failures: Context Engineering
    Empowering an MCP server with a telemetry pipeline
    The Debugging Bottleneck: A Manual Log-Sifting Expedition
    The Smartest Member of Your Developer Ecosystem: Introducing the Mezmo MCP Server
    Your New AI Assistant for a Smarter Workflow
    The Observability Problem Isn't Data Volume Anymore—It's Context
    Beyond the Pipeline: Data Isn't Oil, It's Power.
    The Platform Engineer's Playbook: Mastering OpenTelemetry & Compliance with Mezmo and Dynatrace
    From Alert to Answer in Seconds: Accelerating Incident Response in Dynatrace
    Taming Your Dynatrace Bill: How to Cut Observability Costs, Not Visibility
    Architecting for Value: A Playbook for Sustainable Observability
    How to Cut Observability Costs with Synthetic Monitoring and Responsive Pipelines
    Unlock Deeper Insights: Introducing GitLab Event Integration with Mezmo
    Introducing the New Mezmo Product Homepage
    The Inconvenient Truth About AI Ethics in Observability
    Observability's Moneyball Moment: How AI Is Changing the Game (Not Ending It)
    Do you Grok It?
    Top Five Reasons Telemetry Pipelines Should Be on Every Engineer’s Radar
    Is It a Cup or a Pot? Helping You Pinpoint the Problem—and Sleep Through the Night
    Smarter Telemetry Pipelines: The Key to Cutting Datadog Costs and Observability Chaos
    Why Datadog Falls Short for Log Management and What to Do Instead
    Telemetry for Modern Apps: Reducing MTTR with Smarter Signals
    Transforming Observability: Simpler, Smarter, and More Affordable Data Control
    Datadog: The Good, The Bad, The Costly
    Mezmo Recognized with 25 G2 Awards for Spring 2025
    Reducing Telemetry Toil with Rapid Pipelining
    Cut Costs, Not Insights:   A Practical Guide to Telemetry Data Optimization
    Webinar Recap: Telemetry Pipeline 101
    Petabyte Scale, Gigabyte Costs: Mezmo’s Evolution from ElasticSearch to Quickwit
    2024 Recap - Highlights of Mezmo’s product enhancements
    My Favorite Observability and DevOps Articles of 2024
    AWS re:Invent ‘24: Generative AI Observability, Platform Engineering, and 99.9995% Availability
    From Gartner IOCS 2024 Conference: AI, Observability Data, and Telemetry Pipelines
    Our team’s learnings from Kubecon: Use Exemplars, Configuring OTel, and OTTL cookbook
    How Mezmo Uses a Telemetry Pipeline to Handle Metrics, Part II
    Webinar Recap: 2024 DORA Report: Accelerate State of DevOps
    Kubecon ‘24 recap: Patent Trolls, OTel Lessons at Scale, and Principle Platform Abstractions
    Announcing Mezmo Flow: Build a Telemetry Pipeline in 15 minutes
    Key Takeaways from the 2024 DORA Report
    Webinar Recap | Telemetry Data Management: Tales from the Trenches
    What are SLOs/SLIs/SLAs?
    Webinar Recap | Next Gen Log Management: Maximize Log Value with Telemetry Pipelines
    Creating In-Stream Alerts for Telemetry Data
    Creating Re-Usable Components for Telemetry Pipelines
    Optimizing Data for Service Management Objective Monitoring
    More Value From Your Logs: Next Generation Log Management from Mezmo
    A Day in the Life of a Mezmo SRE
    Webinar Recap: Applying a Data Engineering Approach to Telemetry Data
    Dogfooding at Mezmo: How we used telemetry pipeline to reduce data volume
    Unlocking Business Insights with Telemetry Pipelines
    Why Your Telemetry (Observability) Pipelines Need to be Responsive
    How Data Profiling Can Reduce Burnout
    Data Optimization Technique: Route Data to Specialized Processing Chains
    Data Privacy Takeaways from Gartner Security & Risk Summit
    Mastering Telemetry Pipelines: Driving Compliance and Data Optimization
    A Recap of Gartner Security and Risk Summit: GenAI, Augmented Cybersecurity, Burnout
    Why Telemetry Pipelines Should Be A Part Of Your Compliance Strategy
    Pipeline Module: Event to Metric
    Telemetry Data Compliance Module
    OpenTelemetry: The Key To Unified Telemetry Data
    Data optimization technique: convert events to metrics
    What’s New With Mezmo: In-stream Alerting
    How Mezmo Used Telemetry Pipeline to Handle Metrics
    Webinar Recap: Mastering Telemetry Pipelines - A DevOps Lifecycle Approach to Data Management
    Open-source Telemetry Pipelines: An Overview
    SRECon Recap: Product Reliability, Burn Out, and more
    Webinar Recap: How to Manage Telemetry Data with Confidence
    Webinar Recap: Myths and Realities in Telemetry Data Handling
    Using Vector to Build a Telemetry Pipeline Solution
    Managing Telemetry Data Overflow in Kubernetes with Resource Quotas and Limits
    How To Optimize Telemetry Pipelines For Better Observability and Security
    Gartner IOCS Conference Recap: Monitoring and Observing Environments with Telemetry Pipelines
    AWS re:Invent 2023 highlights: Observability at Stripe, Capital One, and McDonald’s
    Webinar Recap: Best Practices for Observability Pipelines
    Introducing Responsive Pipelines from Mezmo
    My First KubeCon - Tales of the K8’s community, DE&I, sustainability, and OTel
    Modernize Telemetry Pipeline Management with Mezmo Pipeline as Code
    How To Profile and Optimize Telemetry Data: A Deep Dive
    Kubernetes Telemetry Data Optimization in Five Steps with Mezmo
    Introducing Mezmo Edge: A Secure Approach To Telemetry Data
    Understand Kubernetes Telemetry Data Immediately With Mezmo’s Welcome Pipeline
    Unearthing Gold: Deriving Metrics from Logs with Mezmo Telemetry Pipeline
    Webinar Recap: The Single Pane of Glass Myth
    Empower Observability Engineers: Enhance Engineering With Mezmo
    Webinar Recap: How to Get More Out of Your Log Data
    Unraveling the Log Data Explosion: New Market Research Shows Trends and Challenges
    Webinar Recap: Unlocking the Full Value of Telemetry Data
    Data-Driven Decision Making: Leveraging Metrics and Logs-to-Metrics Processors
    How To Configure The Mezmo Telemetry Pipeline
    Supercharge Elasticsearch Observability With Telemetry Pipelines
    Enhancing Grafana Observability With Telemetry Pipelines
    Optimizing Your Splunk Experience with Telemetry Pipelines
    Webinar Recap: Unlocking Business Performance with Telemetry Data
    Enhancing Datadog Observability with Telemetry Pipelines
    Transforming Your Data With Telemetry Pipelines