Everyone Sees Risk Differently—and That’s the Problem
Ask an engineer what a vulnerability is, and you’ll get one answer. Ask a compliance officer, and you’ll get another. Security teams talk in CVEs and severity scores. Legal teams look for contracts, attestations, and evidence. Meanwhile, engineering sees bugs, backlog, and release blockers.
None of them are wrong. But if everyone is working from a different understanding of what a ‘component’, ‘finding’, or ‘product’ even means, you’re going to end up with conflicting reports, duplicated effort, and missed signals.
Why a Shared Data Model Matters
Before you can unify your tools or automate workflows, you need to agree on the basics. What is a product? What counts as a component? What’s a finding versus a vulnerability? Who owns what?
These questions might seem simple, but without shared answers, you’ll constantly run into:
- Version drift between teams
- Misunderstandings in ownership and accountability
- Disputes over the “right” data or source of truth
Defining a shared data model—your common language—is the foundation for product security maturity. It enables consistency, traceability, and scale.
Start with Definitions, Then Build Workflows
It’s tempting to jump into tooling or dashboards, but if your teams aren’t aligned on terminology, those tools will only magnify the confusion. Start by getting agreement on core terms:
- What constitutes a ‘product’ and how is it versioned?
- How are ‘components’ identified and tracked?
- What’s the difference between a ‘finding’ and a ‘vulnerability’?
- What statuses are used to track triage and remediation?
When teams are aligned here, automation becomes possible. Policies can be applied consistently. Ownership can be tracked with confidence. And reporting becomes credible.
From Language to Collaboration
Once you have a clear data model, the next step is enabling teams to work from it. This doesn’t mean forcing everyone into the same view; it means delivering contextually relevant views built on shared foundations.
For example:
- Engineers see security findings tied directly to components they own.
- Security teams work from the same data but filtered through governance and risk lenses.
- Compliance and legal teams trace actions, ownership, and evidence across product versions.
This is where a unified platform like Finite State can help—not by replacing team workflows, but by anchoring them to one source of truth.
What It Looks Like in Practice
We’ve worked with organisations where every department had its own SBOM. Security scanned one set of components, engineering used another, and legal tracked vulnerabilities through a spreadsheet. The result? Missed issues, repeated triage, and weeks of rework during audits.
Once we helped them align their definitions and data model, everything changed. SBOMs were normalised. Findings were triaged consistently. Each team still had its own workflows, but they all pointed back to the same shared model.
Collaboration improved. Audit prep time dropped. And most importantly, everyone trusted the data.
Final Thought: Don’t Skip the Foundation
Security teams often want to jump straight into tooling, but alignment on terminology and structure is what actually makes those tools effective. Without a shared understanding of what your data represents, you’re building on shaky ground.
Define your data model early. Revisit it often. And use it to guide not just how you track risk but how you work together to reduce it.
Want help building a unified model for your product security? Book a demo with Finite State to see how we help teams align their data, tools, and decisions around a single risk picture.
