How observability, application security, and AI enhance DevOps and platform engineering maturity

DevOps and platform engineering are essential disciplines that provide immense value in the realm of cloud-native technology and software delivery. However, these practices cannot stand alone. Rather, they must be bolstered by additional technological investments to ensure reliability, security, and efficiency. One of these key investments includes observability.

Observability of applications and infrastructure serves as a critical foundation for DevOps and platform engineering, offering a comprehensive view into system performance and behavior. It goes beyond traditional monitoring—metrics, logs, and traces—to encompass topology mapping, code-level details, and user experience metrics that provide real-time insights. This real-time awareness enables teams to rapidly detect and resolve issues: both indispensable capabilities for maintaining the agility and reliability that are central to DevOps and platform engineering processes.

Moreover, observability is the launchpad for maturing DevOps and platform engineering postures. Recent research found that 71% of organizations actively use observability data and insights to drive automation decisions and improvements in DevOps workflows. The technology has also enabled 78% of organizations to automate release validation and 74% of organizations to identify bottlenecks and automate delivery pipelines.

Observability and DevSecOps: Shifting left

Observability empowers teams to embrace a shift-left approach. The deep visibility and insights that observability provides allow teams to take proactive measures early in the software development life cycle (SDLC).

Shifting left is an approach that includes software quality, performance, and security testing as a part of the SDLC. These practices ensure optimal software functioning and the quick resolution of issues before they proliferate. This practice has become integral to DevSecOps, which fosters a culture of shared responsibility where all stakeholders play a role in maintaining the integrity of the software and infrastructure.

Without observability, a DevSecOps approach becomes increasingly difficult to execute. A lack of insights and visibility into a digital environment leads to inefficient management and resolution of vulnerabilities, attacks, and threats. Conversely, with observability providing a clear explanation of the root cause or origin of security issues, teams can immediately begin addressing issues. Ultimately, observability-powered insights preserve resources and enable DevSecOps at scale.

Observability and platform engineering: Unlock DevOps efficiency

Platform engineering teams also benefit immensely from observability. Beyond simply monitoring infrastructure health and diagnosing issues, observability can assist platform engineers by providing key insights for capacity management, performance optimization, compliance, and other critical aspects of platform maintenance and provisioning.

For example, an observability solution can track and analyze usage data to help engineers understand how and when to scale resources based on system demand. Incorporating observability into software delivery practices enables platform engineering and DevSecOps teams alike to execute high-importance tasks and responsibilities with confidence.

However, observability remains only one piece of the puzzle when it comes to ensuring the success of both DevSecOps and platform engineering.

The role of AI in DevSecOps

When integrated into DevSecOps, artificial intelligence (AI) helps teams transform data into an actionable asset for automating workflows across development, security, and operations. Combining causal AI with machine learning-based algorithms analyzes vast datasets in real-time and provides practitioners with precise answers driven by root cause analysis.

This capability is monumental for DevSecOps teams. AI helps provide in-depth context around system issues, anomalies, and other events instead of merely identifying them. Without this level of context, datasets become exponentially difficult to analyze and use for any effective or efficient DevSecOps processes. Causal AI also bolsters DevSecOps by allowing for early anomaly and vulnerability detection, rapid issue resolution, and system performance optimization.

Informed by past performances, predictive AI paired with observability forecasts future system needs and offers predictive insights. This fosters a proactive approach to system health and maintenance. These capabilities enable technical teams to minimize disruptions, cyberattacks, and downtime by identifying potential issues before they escalate.

AI strengthens the “Sec” in DevSecOps by not only offering continuous real-time insights into system vulnerabilities but also providing intelligence and answers regarding future potential vulnerabilities.

The role of AI in platform engineering

For platform engineers, AI creates an environment where its capabilities converge harmoniously with observability and security.

AI-driven insights optimize resource allocation, bolster internal developer platform scalability, and introduce autonomous operations for platform engineers. Autonomous operations can adapt to changing circumstances within an environment and automatically self-adjust and execute necessary workflows. For example, AI enables intelligent resource allocation for the optimal scaling of platform infrastructure without the need for any human intervention.

Automated rollbacks or rollouts based on observability data also become a reality with AI.  These automated actions enhance system reliability and overall platform resilience. The introduction of AI in platform engineering marks a new horizon for the discipline, enhancing its efficiency, efficacy, and security like never before.

Observability, security, and AI: Better together

Together, the synergy of observability, security, and AI redefines DevOps and platform engineering. These three capabilities are a recipe for accelerating software delivery and fortifying tech stacks and applications against emerging security challenges. This combination is essential for organizations that are navigating the complex terrain of modern software development and infrastructure management.

Observability, security, and AI play a crucial role in strengthening DevSecOps and platform engineering. Each component offers myriad benefits, including accelerating software delivery, enhancing software resilience, reducing manual tasks, improving developer productivity and satisfaction, and more.

Dynatrace offers a unified solution containing each of these key ingredients for DevSecOps and platform engineering success. With end-to-end observability powered by hypermodal AI and built-in security analytics and security protection, the Dynatrace platform empowers organizations with the capabilities they need to unlock agility, efficiency, and scale.