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Embracing DevSecOps: An Approach to Enhance Software Security and Delivery

Veteran engineers likely remember the days of early software development, when software releases were delayed until they included all desired code complete features. This method resulted in extremely long wait times for users, not to mention security concerns and errors that were often treated as an afterthought and addressed through patches and updates. Worse, developers often released these updates on an annual or semiannual basis, creating significant delay between the recognition of an error and its resolution.

Agile development methodologies and continuous deployment (CD) have since arisen as the antithesis of slow, buggy releases. Agile development and CD enable faster updates with fewer errors by combining automation, continuous development, and integration to streamline the software development and delivery process.

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Beyond Observability: Putting Intelligence in Modern Monitoring

If you’re paying attention to anything that’s happening in the development world, you’re likely familiar with the term “observability.” We’re seeing more and more monitoring companies from all different backgrounds jumping on the term to describe their solutions, many claiming their observability tool to be the factor that will take businesses to the next level.

Growing out-of-control system engineering, observability allows dev teams to unify and study the behaviors of various IT systems through the external outputs of the internal systems. In the case of software, that’s log events, distributed tracing, and time-series metrics. By unifying the data streaming through today’s complex IT environments, it certainly gives SREs and DevOps practitioners a leg up from traditional monitoring. But the data alone is no longer enough.

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How Observability and AI Support DevOps’ New Way of Working

Throughout the last few years, the traditional tasks of a DevOps practitioner have evolved to focus less on keeping the lights on and more on adding value to the customer experience. This, in part, is because automation has taken over some of the mundane, daily tasks presented by old monitoring systems. But new solutions have emerged that support the new way of working for DevOps practitioners — bring in intelligent observability, or observability and AI. 

Observability takes data from complex environments to infer from the outside what’s happening on the inside, i.e.: giving developers insight into what’s happening and where. But that’s not enough information to show what action needs to be taken, who is responsible, and how to prevent future incidents. With observability and AI, or intelligent observability, DevOps practitioners experience a whole new way of working. They can automate system monitoring, act quickly on insights into the issues that need attention, and understand what needs to be done to resolve incidents because they now understand why they happen. 

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How to Operate Less and Innovate More Using Observability and AI

From software engineers to CEOs, everyone wants more time to think strategically instead of tactically executing tasks. While checking those tasks off your to-do list is important, and usually essential, are they the best use of your time? Humans prefer to do rather than to think, but those million-dollar ideas come from thinking. How can we fit more time into our day to make that happen? Unfortunately, we can’t. Time is finite, and we only have 24 hours in a day. But, what we can do is take some of those tasks off our plate. And I’m not talking about through a hiring spree, but rather investing in technology that can do the work for us. 

 This is especially true for DevOps practitioners and SRE teams who face enormous amounts of data and customer-facing issues. Today’s business leaders are pushing for their teams to spend more time innovating and less time fixing issues, yet some leaders haven’t invested in the technology to empower their teams to do so. By bringing AI-driven observability to DevOps, these issues can be addressed proactively through automation. As a result, teams can save hundreds of hours of work per year, empowering them to innovate more, operate less, and unlock their true potential. Let’s look at a few ways DevOps pros and SRE teams can leverage observability and AI to operate less and innovate more.