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Today, DevOps teams are expected to deliver new apps and services at record speeds. But at the same time, the complexity of their architectures, technology stacks and software supply chains continues to spiral upward. Amid this complexity, and in an age of CI/CD, about one in three CIOs (34%) report that their teams have had to compromise on security to keep up with the pace of innovation. This increasing speed and rising complexity call for a new approach that eliminates the barriers between DevOps and security teams. It also demands a new approach to automation.
The need for teams to integrate development, operations and security processes has given rise to the DevSecOps movement. DevSecOps aims to break down manual handovers and delays between the work of DevOps and security teams. It’s unsurprising, then, that DevSecOps is seen as one of the most promising ways to enable faster release cycles while minimizing the risk of bugs or vulnerabilities.
However, DevSecOps adoption has a long way to go. Only 25% of security teams report that they can access comprehensive, accurate and continuous reports of applications and code running in production, which suggests limited insight into DevOps processes. To address this gap, organizations must find ways to remove the manual work to power the processes and the exchange of information between DevOps and security teams. They need to enable intelligent automation that drives smarter collaboration and decision-making across departments.
Intelligent automation is an automated IT process that acts on reliable answers derived from real-time data and a synergistic combination of predictive, causal and generative AI techniques.
Predictive AI models recommend future actions based on learnings from the past but acts on real-time data. Causal AI, which uses fault-tree analysis, determines root causes based on contextual relationships derived from dynamic topology and dependency maps. Using insights from predictive AI and precise answers from causal AI, intelligent automation can use generative AI with large language models to code specific responses and generate custom runbooks and workflows.
By bringing these different AI methods together in a synergistic, single framework, organizations can take advantage of the benefits that each brings to specific automation use cases. To make this integration work effectively, organizations need to bring security and observability data together into a single analytics platform that is scalable and optimized for privacy compliance. A unified platform approach breaks down silos, maintains the context between data sets and enables teams to automate more DevSecOps processes.
The following are three examples of intelligent automation in practice when it comes to enabling DevSecOps.
Quality gates are a critical part of the delivery process in which teams validate code to ensure it meets performance and quality criteria as well as service-level objectives (SLOs). Validating results of test runs, including functional, integration, performance and so on, has historically been a manual process that requires input from both DevOps and security teams, which in turn has made it a natural bottleneck for many projects.
However, with the rise of cloud native environments and contemporary CI/CD practices, there’s a clear need to automate validation testing with clear, consistent and effective quality gates. Automation is especially vital for verifying code, performing data analysis on software test results and generating post-test reports.
Automating many of the manual and repetitive phases of the release validation process means significant time savings and productivity boosts. But the real benefit comes when teams can introduce intelligent automation, wherein predictive, causal and generative AI come together to anticipate potential release validation issues that affect SLOs, determine their root causes and downstream effects, and develop and execute dynamic responses.
Along with saving time, this coordinated approach improves accuracy and creates standards among DevOps, site reliability engineering and security teams.
Tickets often serve as the main communication platform between security and DevOps teams. In most organizations, issuing and responding to tickets can be a highly manual process. However, the manual approach introduces significant room for human error, which could result in costly mistakes and delays.
Automating ticket assignments is an important first step to addressing these problems. The key to automating ticket assignments is clearly classifying error types and defining ownership information. But even if teams automate ticket assignments, they still need to remediate the underlying problems. Automated remediation is a harder problem to solve.
To automate remediation, teams need accurate answers. Intelligent automation based on AI provides an adaptable and reliable answer-driven framework for highly variable and dynamic application environments.
When it comes to identifying and responding automatically to issues, causal AI can conduct automated root cause analysis (RCA) to precisely identify the cause of a problem. The system can then notify the responsible team and trigger an automated response, such as applying an updated configuration or rolling back to a previous version. Using causal and predictive AI capabilities, teams can automatically detect, flag, assign and resolve a security or performance problem in seconds by triggering remediation workflows and automated runbooks.
Intelligent automation can also support progressive delivery by making it easier for organizations to embed feature flags into their pipelines. Feature flags allow developers to maintain more control when releasing new features or code by quickly toggling their changes on or off. This helps to prevent user or customer experience degradations if a new feature or line of code introduces a defect that hurts the performance or stability of the software.
Developers can use intelligent automation, fueled by observability data, to support these practices by maintaining real-time situational awareness into the health of their applications. This enables them to quickly determine when a new release degrades the user experience so that they can respond immediately and mitigate the impact by toggling feature flags on or off.
By feeding key performance indicators (KPIs) such as error rates and response times into a platform powered by causal AI, developers can instantly identify what triggered a change in systems behavior and understand its impact on user experience. In addition to mitigating negative effects, using feature flags can also identify when a new release has a positive impact on KPIs. This enables developers to quickly scale the release to a wider user base.
By using these insights to make decisions about when to toggle feature flags on or off, developers have a more certain way of accelerating innovation while minimizing the risk of service disruption. As a result, they can focus on continuously and proactively optimizing the user experience and driving value for the business.
To gain an advantage over the rapid pace of software delivery and the explosion of data in cloud native environments, organizations need a way to unify data and harness AI effectively.
Together, causal, predictive and generative AI methods provide benefits for a gamut of DevOps and security automation use cases. Intelligent automation effectively applies these synergistic AI methods together using a unified platform approach to provide the widest benefits of observability data for use cases across the organization.
By aggregating data in context from any source, cloud-hosted or on premises, development, security and operations teams can work collaboratively from the same rich data set. As a result, intelligent automation tears down department silos and guarantees precise and trustworthy answers from AI systems to drive faster, more secure, more reliable software releases. And as teams are able to automate more processes, they can also drive greater DevSecOps adoption without compromising security.
