The Missing Observability Layer in DevSecOps

DevOps was meant to break silos. It was supposed to bridge developers and operations. Increase feedback loops. Improve reliability. Reduce friction.

But somewhere along the way, we created new silos:

• DevOps teams,
• SRE teams,
• Security teams,
• Cloud teams,
• Security Operations and many others.

All reporting to the same leadership. All measured on similar KPIs. All claiming shared ownership.

And yet operating in isolation, fighting internally, working not together but against each other.

Security tools report to security dashboards, detached from engineering reality of infrastructure as code. SRE tools report to reliability dashboards, generate zillion alerts spamming your on-callers. CI/CD tools report to DevOps dashboards, logs and build statuses.

Everyone sees their own metrics. No one sees the system-wide picture.

The Observability Illusion

We have incredible observability today. We can observe:

• What jobs were executed
• What failed
• What passed
• How long builds took
• What logs were emitted
• What metrics were collected
• Even telemetry for AI systems and LLM inference

But there is a deeper problem.

We can observe what runs. We cannot observe what is missing.

We don't know:

• Which security job should have existed but doesn't
• Which SRE practice is disconnected from SLOs
• Which chaos experiment never ran
• Which artifact was never signed
• Which IaC validation stage was never implemented

Absence does not produce logs. And absence is where risk hides.

Security Is Not Just About Vulnerabilities

Security includes availability. Chaos engineering is security. GameDays are security. SLO violations are security signals.

And yet:

• Security teams often focus on CVEs and scanning.
• SRE teams focus on uptime and latency.
• DevOps teams focus on pipeline efficiency.

These domains overlap, but the tooling does not connect to them. We have seen that in so many variations repeating across various industries.

The problem is not lack of tools. It is the lack of shared context.

We Didn't Want to Build "Another Security-DevOps-SRE Tool"

When we started our journey, we didn't want to build a security platform. We wanted to build something that connects:

• Security
• DevOps
• SRE

The idea was simple but difficult: Generate metadata about engineering practices.

Not just scan for vulnerabilities, misconfigurations, bad code, and you name it. But also tells you:

• This job is missing
• This practice is incomplete
• This responsibility overlaps teams
• This tooling is redundant
• This control is implemented but not verified

Every finding on our platform is labeled with team responsibility. For example, a single issue might relate to:

• Release management
• Security
• DevOps

That labeling is intentional. It forces collaboration. It makes visible that this is not "Security's problem." It is not "DevOps' problem." It is shared.

Research Before Building

Before building the engine, we analyzed hundreds of real-world production grade repositories — over 600, close to 1,000. In our beta, more than 20 companies contribute to feedback.

We studied:

• CI/CD YAML structures
• Tool combinations
• Missing security stages
• Redundant linters
• Broken orchestration
• Terraform patterns
• Artifact handling
• Dependency evolution
• ML pipeline configurations

What we found was consistent across industries:

• Multiple linters without orchestration
• SAST implemented but no artifact signing, or wrong choice of tooling.
• Terraform without validation gates, problematic infrastructure workflows, or usage of legacy or dangerous infrastructure modules.
• Chaos engineering disconnected from SLOs
• Security controls present but not enforced
• AI training pipelines without deterministic validation

The issue was not lack of tools. It was a lack of systemic visibility.

The AI Era Makes This Worse

Now we are entering the AI era. Agents can write code. Agents can modify infrastructure. Agents can deploy models. Agents can chain workflows.

Without deterministic observability on top of that, systems become chaotic.

AI deployment pipelines introduce:

• Model training stages
• Artifact registries and versioning
• Feature stores
• Inference endpoints
• Guardrails
• Prompt validation
• Model evaluation stages

Most organizations do not have strong CI/CD observability for these workflows. And AI agents amplify mistakes at scale.

If your pipeline lacks structural validation before AI touches it, you will accumulate invisible risk very quickly.

From SBOM to Engineering Posture

Originally, we wanted to build a software bill of materials trend platform. Not just vulnerability-based SBOM analysis. But historical comparison:

• What dependencies were added?
• What had disappeared?
• How did builds evolve over time?

That thinking evolved into something broader. With every verification run of a pipeline, we generate structured metadata:

• Evidence of executed jobs
• Evidence of skipped audits
• Reasons for skipped controls
• CI/CD posture data
• Cloud integration posture
• Artifact integrity posture
• Cryptographic usage metadata
• SBOM
• Crypto Bill of Materials (CBOM)
• ML Bill of Materials (ML-BOM)
• AI Bill of Materials (AI-BOM)

At a specific snapshot in time, you can see:

• How your software was built
• How your AI services were deployed
• What cryptography was used
• What practices were verified
• What was intentionally skipped

This is not runtime observability. This is engineering posture observability.

Verified Remediation, Not Just Detection

Detection is easy. Remediation is hard. Because remediation requires context.

We don't just flag missing stages. We recommend code patterns that were:

• Verified in real environments
• Tested in production
• Designed by engineers working across DevOps, Security, and SRE

Our remediation suggestions are not theoretical best practices. They are field-tested.

A New DevSecOps Posture Layer

What we believe is emerging is a new category: DevSecOps posture verification.

Not runtime monitoring. Not vulnerability scanning. Not cloud configuration scanning alone.

But verification of engineering intent. The ability to answer:

• Are we building what we think we are building?
• Are we missing structural controls?
• Are teams aligned on shared responsibilities?
• Can we prove how our software and AI systems were built at a specific point in time?

That is why we built KvantumCI. Not as a security tool. But as a bridge.

Between DevOps, SRE, Security — and now AI engineering.