There is a category of work in security that nobody puts on a roadmap because it sounds infrastructural and unglamorous. Runtime asset attribution is one of those. The question it answers is small: when this pod, container, or process generates an alert, what service does it belong to, who owns it, what is its source repository, and what does its SBOM look like. Every part of that question has to be answerable in seconds for the rest of the security program to function. When the question is hard to answer, every alert turns into a ticket that takes hours of investigation before anyone can decide what to do.
This post is about the surprisingly involved engineering behind runtime attribution and why it is worth investing in deliberately.
Why this is harder than it sounds
In the old data center, the question was straightforward. A process ran on a host. The host had a name, an owner team, and a known purpose. You looked it up in the inventory and you were done.
In a container orchestration environment, the relationships are looser. A pod is a short lived runtime instance. The image it runs comes from a registry, but the registry path is sometimes a human readable name and sometimes a digest reference. The image was built by a pipeline that pulled source from a repository, but the link from image to repository is only as good as the build provenance the pipeline produced. The pod runs in a namespace, but namespaces are sometimes mapped to teams and sometimes mapped to environments or applications.
Attribution at the pod level is therefore a multi hop join. You need to traverse from pod identifier, through image digest, through build attestation, through source repository, through ownership records, to land on a human team. Each hop has its own data quality issues. Each hop is owned by a different platform component. Each hop is the place where the chain breaks in practice.
The minimum data model
A useful attribution graph stores a small set of well chosen entities and edges.
Pods, with their UID, namespace, node, labels, and a reference to the image they run.
Images, with their digest, registry path, and a reference to the build event that produced them.
Build events, with the source repository, commit, branch, builder identity, and the build attestation if one exists.
Repositories, with their canonical identifier, owning team, and current contributors.
Services, which are the human level concept that ties together a repository, an image stream, and one or more deployments.
Owners, which are teams or named individuals reachable through the company directory.
The edges that matter are runs (pod to image), built from (image to build event), produced from (build event to repository), implements (repository to service), and owned by (service to team). Every other piece of attribution can be derived by traversing this graph.
The signals that fill the graph
The attribution graph is filled from signals you already have, but they have to be ingested deliberately.
Container runtime telemetry. The pod and image digest are observable from the orchestrator and from runtime sidecars or eBPF probes. This is the most reliable signal because it reflects what is actually running, not what the deployment manifest claims.
Image registry metadata. The image digest, the tag, and the build labels are queryable from the registry. Build labels often contain the repository, commit, and pipeline run identifier when the pipeline was configured to set them.
Build attestations. SLSA style attestations and the new in toto formats encode the link from image to build event in a verifiable way. Where present, attestations are the gold standard for attribution.
SCM metadata. Repositories carry CODEOWNERS, team mappings, and contributor history. These are the inputs to ownership.
Service catalogs. Where a service catalog exists, it is the link between the repository and the human level service concept. Where it does not, the catalog is something the security team often has to derive itself.
Each signal alone is incomplete. Combined into a single graph with reconciliation between them, the result is an attribution layer that answers the alert routing question reliably.
Where the chain breaks in practice
The honest version of this story is that in most environments, several links in the chain are weak.
Image to build event. Many pipelines do not produce attestations. The link relies on tag conventions and label hygiene, both of which decay over time.
Build event to repository. CI runs sometimes run against branches in personal forks, against bot generated commits, or against repositories that have since been deleted or renamed. The link survives only if the build event preserves the canonical repository identifier.
Repository to service. CODEOWNERS files are aspirational in many organizations. The actual maintainers of the code may differ from what the file says, especially after a reorg.
Service to owner. Team rosters change. The pager rotation that the alert should route to is not always the same as the team listed in the catalog.
Each of these gaps is fixable, but the fix requires investment from a team that is not usually staffed for it. The platform team owns part of it. The security team owns part of it. Engineering org wide standards own part of it. Alignment is the limiting factor, not technology.
What attribution unlocks once it works
When attribution works, several downstream capabilities become cheap that were previously expensive.
Alert routing. A runtime alert can be routed to the responsible team in seconds without a human triage step. This compresses incident response timelines significantly.
Vulnerability prioritization. A new CVE in a transitive dependency can be evaluated by looking at which services run images that include the vulnerable package, then ranked by the criticality of those services.
Coverage reporting. Coverage of any control, EDR, scanning, signing, can be reported per service rather than per host, which is the level executives actually want.
Cost and risk allocation. Cloud and operational cost can be attributed to teams. Security investment can be aimed at the services with the highest combined risk and value.
Without attribution, each of these capabilities requires a separate side investigation. With attribution, they are queries.
How to build attribution incrementally
Teams that try to build attribution as a single project often fail because the scope is too large. Incremental adoption works better.
Start with image to repository. Make sure every image produced by the company's pipelines carries a label or attestation that points to the source. Reject builds that do not.
Add repository to service. Build a thin service catalog that maps repositories to services and services to teams. Treat the catalog as code, store it in a repository, and require updates when ownership changes.
Add pod to image at runtime. Use the orchestrator and runtime telemetry to maintain a live mapping. This is mostly free because the data is already exposed.
Add SBOM linkage. Link each image to the SBOM produced at build time so attribution queries can include component level questions, not just service level ones.
Each step is independently useful. The full chain is the goal, but partial coverage already pays off.
How Safeguard Helps
Safeguard's continuous asset discovery includes runtime attribution as a first class capability rather than a separate add on. The platform ingests pod and process telemetry alongside image registry data, build provenance, SCM ownership, and service catalogs, and reconciles them into a single graph that maps every running workload back to its source, its team, and its SBOM in a single query.
When a vulnerability is detected in a component, the graph already knows which images contain it, which deployments run those images, and which teams own them. The AI-BOM extends the same model to AI assets, so a model loaded by a pod is attributable in the same way a library is. The MCP registry covers agent tooling in the same model. Continuous discovery turns runtime attribution from an investigation into a query you do not have to think about.