This article is based on analysis originally published on ITProPortal, authored by Luca Ravazzolo. It has been rewritten and expanded for a DevOps engineering audience.
DevOps: A Rapidly Maturing Discipline
The introduction of DevOps fundamentally changed how development and operations teams work together. Practices like continuous integration (CI) and continuous delivery (CD) moved from niche concepts to industry standards, and organisations that adopted them saw measurable improvements in deployment frequency, lead time, and system stability.
As DevOps matured, several consistent benefits became clear across adopting organisations:
- Auditability and repeatability: Infrastructure as code and pipeline-driven deployments mean every change is logged, versioned, and reproducible.
- Low cost of change: Automated testing and short feedback loops reduce the risk and expense of making changes.
- Cross-functional collaboration: Breaking down silos between development, operations, and QA teams accelerates delivery.
- Speed at scale: Automation enables teams to ship features and fixes faster without sacrificing quality.
At the same time, the cloud ecosystem evolved in parallel, providing developers with richer, more intelligent tooling — from managed Kubernetes services to serverless platforms — that reinforces DevOps discipline and increases operational efficiency.
The Emergence of DevSecOps
One significant gap in early DevOps adoption was security. The pressure to ship quickly often meant that security checks were treated as a final gate rather than an integral part of the pipeline. Retrofitting security after a build is both costly and error-prone — vulnerabilities that could have been caught at the design or coding stage become much more expensive to remediate post-deployment.
DevSecOps addresses this directly by embedding security practices throughout the entire software development lifecycle (SDLC). Rather than a separate security review at the end, security becomes a shared responsibility starting from the first line of code.
Two Pillars of a Successful DevSecOps Adoption
1. Collaboration Across Disciplines
Historically, developers focused on logic, algorithms, and feature delivery, with security treated as someone else’s concern. DevSecOps requires a cultural shift: security engineers must be involved from the architecture stage, and developers must be equipped with the knowledge to write secure code. This means:
- Including security experts in design reviews and sprint planning.
- Training developers to recognise common vulnerability patterns (e.g., OWASP Top 10).
- Establishing shared ownership of security outcomes across development, operations, and security teams.
2. Continuous Review and Monitoring
A key principle of DevSecOps is that security review is not a one-time event — it is a continuous process. Practical implementation includes:
- Static application security testing (SAST) integrated into CI pipelines to catch vulnerable code before it merges.
- Dynamic application security testing (DAST) run against staging environments.
- Compliance monitoring for frameworks such as PCI-DSS and GDPR, enforced programmatically rather than manually.
- Defined incident response playbooks so teams know exactly how to react when a threat is detected.
- Security gate criteria applied at each stage: from developer commit, through build, to test and production deployment.
The goal is that security posture improves continuously rather than degrading between infrequent audits.
What Comes After DevSecOps? Two Emerging Theories
DevSecOps represents one evolution of the DevOps model, but it is unlikely to be the last. Two distinct theories about the future of Ops practices have gained traction in the engineering community.
Theory 1: NoOps — Full Automation of Operations
The NoOps concept takes automation to its logical extreme. In a mature NoOps environment, the software provisioning pipeline is so thoroughly automated that human intervention for routine operational tasks is largely eliminated. Key characteristics of a NoOps vision include:
- Code standards, security policies, library compliance, and regulatory requirements are enforced automatically at build time.
- Infrastructure provisioning, scaling, and remediation are handled by the platform with no manual input required.
- Engineers shift from performing operations to monitoring automated systems and investigating anomalies.
In practice, NoOps is more of a spectrum than a binary state. Fully serverless architectures and managed platform services (such as fully managed databases, API gateways, and container orchestration services) move teams significantly along this spectrum. The potential upside is a higher, more consistent baseline of security and reliability — because every deployment automatically meets a defined standard.
Theory 2: MLOps — Machine Learning Meets DevOps
The second theory is that DevOps will not disappear but will evolve into specialised variants, with MLOps being the most prominent example. MLOps applies DevOps principles — automation, versioning, CI/CD, monitoring — to the machine learning lifecycle.
Where traditional DevOps manages the lifecycle of application code, MLOps manages the lifecycle of data, models, and experiments alongside application code. Core MLOps capabilities include:
- Repeatable workflows: Data pipelines and model training runs are versioned and reproducible, so any model can be retrained from scratch with the same result.
- Automated model validation: Quality and performance gates are applied to models before they are deployed to production — analogous to unit and integration tests for code.
- Model monitoring: Production models are monitored for data drift, concept drift, and performance degradation, triggering automated retraining pipelines when needed.
- Regulatory compliance: Because model training runs are reproducible and auditable, organisations can demonstrate to regulators exactly how a model was built and what data it was trained on.
Like DevSecOps, effective MLOps requires breaking down organisational silos. Data scientists, ML engineers, software developers, and platform engineers must collaborate closely for MLOps to deliver value. Isolated data science work that produces notebooks but not deployable, monitored models is the MLOps equivalent of security bolted on at the end of a DevOps pipeline.
The Common Thread: Collaboration and Shifting Left
Whether the discussion is DevSecOps, NoOps, or MLOps, a consistent theme emerges: the most effective evolution of DevOps is one that moves key concerns — security, compliance, quality, reproducibility — earlier in the development lifecycle rather than addressing them at the end. This “shift left” philosophy reduces rework, lowers the cost of defects, and produces more resilient systems.
Equally consistent is the requirement for genuine cross-functional collaboration. No Ops evolution succeeds as a purely technical exercise. It requires teams with different expertise — development, operations, security, data science — to share ownership of outcomes rather than handing work over a wall.
Looking Ahead
DevOps is not a finished idea. The arrival of DevSecOps demonstrates that the model can absorb new disciplines and improve as a result. MLOps is a natural next step as machine learning becomes a standard component of production software systems rather than a specialist research activity.
For engineering teams planning their tooling and process roadmap, the practical implications are clear:
- Treat security, compliance, and model governance as engineering problems to be automated, not manual checkpoints.
- Invest in platform capabilities that reduce toil and push operational concerns toward automated enforcement.
- Build cross-functional teams and cultures rather than passing artefacts between siloed groups.
- Adopt MLOps practices early if machine learning is part of your product roadmap — retrofitting operational discipline onto models in production is as painful as retrofitting security onto deployed code.
The evolution of DevOps is ongoing. Teams that engage with it proactively — rather than waiting for practices to fully mature before adopting them — will be best positioned to deliver secure, reliable, and intelligent software systems.