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If your AppSec program only has the time, budget, or people to deploy one new security control, which should come first: Software Composition Analysis or static code analysis?
Practitioners genuinely disagree.
Some argue SCA should come first because modern applications are built heavily from open-source code, deployment is relatively straightforward, and the findings tend to be concrete. Others argue SAST should come first because vulnerabilities in custom code are entirely within the organization's control and can expose risks that dependency scanning will never find.
There is no universal answer to software composition analysis vs static code analysis.
The right starting point depends on three practical questions: how much of the application comes from dependencies, what is driving the security initiative, and how much capacity the team has to tune and triage a new tool.
And importantly, this is a sequencing decision, not a permanent choice.
Key Takeaways
- There is no universal winner between SAST or SCA. The right first step depends on the application and the team supporting it.
- The case for SCA first is strong for dependency-heavy applications because it is generally faster to deploy, produces comparatively less false-positive noise, and addresses the large amount of third-party code inside modern applications.
- The case for SAST first is strongest when proprietary business logic dominates and teams want findings in code they directly control.
- Dependency ratio, team bandwidth, and the reason for starting the AppSec initiative should determine which control goes first.
- Mature AppSec programs ultimately need both. The real question is which one the organization can deploy, operationalize, and act on first.
For additional perspectives on the distinction, see JetBrains' comparison of software composition analysis and static code analysis and Endor Labs' discussion of static and dynamic SCA.
The Real Question Is Sequencing, Not Either-Or
Most organizations do not permanently choose between the two.
Software composition analysis identifies risk in open-source and third-party dependencies. Static code analysis examines code the organization writes itself for insecure patterns and other defects.
A mature AppSec roadmap generally includes both.
For a smaller or resource-constrained team, however, turning on two new sources of findings at once can create more work than security and engineering can absorb. The practical decision is which control to deploy first, learn from, build processes around, and make operational before adding the second.
The Case for Software Composition Analysis First
Most of Your Codebase Is Already Someone Else's Code
Modern development depends heavily on open source.
Black Duck's 2025 Open Source Security and Risk Analysis report found that 97% of audited commercial codebases contained open source and that, on average, roughly 70% of scanned code had open-source origins.
That changes the math for AppSec.
A relatively small internal development team may be responsible for an application assembled from hundreds or thousands of direct and transitive dependencies. Starting with dependency scanning can therefore provide visibility across a large portion of the software footprint very quickly.
Lower Noise, Faster Setup
SCA is also typically easier to operationalize initially.
A scanner can inspect dependency manifests, lock files, artifacts, or SBOM data and compare identified package versions against known vulnerability databases.
If a package version is present and associated with a CVE, that finding is usually factually correct. This gives SCA a comparatively lower false-positive burden than many static analysis approaches.
That matters for a small team. A security program with limited tuning capacity may get usable signal from dependency scanning faster than it can from a large set of new static-analysis rules.
The Fix Is Usually Simple
Many SCA findings also have a straightforward remediation path.
A vulnerable package may require upgrading from one version to another, replacing a dependency, or applying an available patch.
That does not mean dependency updates are always easy. Upgrades can introduce breaking changes or require regression testing. But the remediation path is generally more defined than a vulnerability in custom business logic where a developer may need to redesign part of the application.
The Case for Static Code Analysis First
The Findings Are Fully Within Your Control
The opposing argument is just as strong.
A vulnerability in custom code belongs to your team. Developers can inspect it, change it, test the fix, and deploy the correction without waiting for an upstream project to release a patch.
With an open-source vulnerability, the safest version may introduce breaking changes, the dependency may be poorly maintained, or a fix may not yet exist.
For teams that value direct control over remediation, static analysis can therefore provide a clearer ownership model.
It Catches What SCA Structurally Cannot
SCA is designed to find risk in dependencies.
It cannot identify every security flaw introduced by your own application logic.
Hard-coded credentials, unsafe input handling, some injection vulnerabilities, insecure authentication flows, and other problems in proprietary code require analysis of the code your team actually wrote.
That is the role of static analysis. For a deeper explanation, see what SAST is and how it works.
If the most important risks in an application live in custom logic rather than third-party packages, static code analysis tools can provide more immediate value.
A Compliance or Custom-Logic-Heavy Codebase Tips the Scale
The composition of the application matters.
A company building proprietary financial calculations, authentication systems, transaction logic, or other complex internal functionality may have substantially more custom-code risk than an application largely assembled from common frameworks and packages.
Compliance can also change the answer.
If an audit or standard specifically requires source code review or secure coding analysis, SAST may need to come first regardless of dependency volume.
A Decision Framework: Three Questions to Ask
Rather than treating SCA versus SAST as a philosophical debate, teams can make the decision using three concrete inputs.
Check Your Dependency Ratio
Start with the application itself.
If the application pulls in hundreds or thousands of packages while the organization writes comparatively little custom functionality, that weighs heavily toward SCA.
If the application consists primarily of proprietary code with a small dependency tree, SAST becomes more compelling.
You do not need a perfect percentage. The goal is to understand where most of the software risk surface comes from.
Check What's Driving the Initiative
Next, ask why AppSec is investing in a new control now.
A recently disclosed critical CVE or open-source incident usually points toward SCA because the immediate question is:
Where are we running this vulnerable package?
A vulnerability caused by internal code, an injection flaw, or a compliance requirement focused on secure coding practices may point toward SAST.
The security event creating urgency should influence the first problem the team solves.
Check Your Team's Tuning Capacity
Finally, look at the people who will operate the tool.
SAST can produce substantial value, but it often requires tuning rules, suppressing accepted findings, modeling framework behavior, and establishing policies around what should block development.
SCA generally has a lower false-positive burden because identifying that a particular dependency version exists is a comparatively deterministic problem.
If the security team has minimal time for configuration and tuning, SCA can be an easier first control to operationalize.
If a security champion or AppSec engineer can actively tune static analysis and work with development teams, SAST can generate value much sooner.
Quick Decision Scorecard
The Blind Spot Either Way
Whichever You Pick First, the Other Attack Surface Is Uncovered
Sequencing has an unavoidable tradeoff.
If SCA goes live first, custom application logic remains outside that control until static analysis is added. If SAST goes first, third-party dependencies remain outside the dependency-scanning workflow.
For resource-constrained teams, the second deployment may take months.
There is also a second problem on the SCA side that becomes more visible over time.
A low false-positive rate does not mean a short vulnerability list.
If SCA correctly identifies a vulnerable dependency, that is not a false positive. The package really is present and the CVE really does apply to that version.
But presence does not prove exposure.
A vulnerable package may be included in an artifact but never loaded. A vulnerable function may never be called. A valid path may exist in a static call graph but sit behind a feature flag or branch that never executes in production.
So a traditional SCA tool can be highly accurate about what exists and still leave security teams with hundreds or thousands of technically correct findings that do not carry the same real-world risk.
That is a volume problem, not a false-positive problem.
This distinction is central to reachability, re-imagined.
Closing the Volume Gap With Runtime SCA
Prioritizing by What Actually Executes
This is where Runtime SCA changes the dependency side of the equation.
Traditional SCA asks whether a vulnerable dependency exists and, in more advanced platforms, whether vulnerable code appears reachable based on static analysis.
Raven adds production evidence.
Rather than reporting every CVE matched to a manifest and treating the resulting backlog as equally urgent, Raven observes vulnerable functions at runtime and determines which ones actually execute in production.
That changes the question from:
Could this vulnerable function run?
to:
Did this vulnerable function actually run?
Raven uses function-level runtime evidence to de-prioritize up to 99% of vulnerabilities, allowing teams to focus remediation on the smaller subset that represents active production exposure.
This does not require source code access. Raven reconstructs application behavior through eBPF-based runtime instrumentation.
That distinction strengthens the case for starting with SCA because it addresses one of the biggest operational challenges that appears after deployment: the vulnerability backlog.
Traditional SCA can quickly provide comprehensive visibility into third-party risk. Runtime evidence can then help separate dependency findings that are simply present from vulnerable functions that are active in the running application.
It is not a replacement for the static code analysis side of the decision. Custom-code vulnerabilities still require their own controls.
It is a way to make dependency vulnerability management more actionable.
This is the structural difference between Raven vs traditional SCA tools.
The progression becomes:
Find the vulnerable dependency.
Determine whether the vulnerable function actually executes.
Prioritize the exposure that matters now.
There is no universally correct order between SCA and SAST.
A dependency-heavy application with a small security team may get more immediate value from SCA. A proprietary application with complex business logic and a dedicated AppSec function may have a stronger case for static analysis first.
The right starting point depends on dependency ratio, what is driving the initiative, and how much tuning capacity the organization has.
Whichever comes first, mature programs eventually need both. And on the dependency side, teams eventually need an answer to a second question beyond whether a CVE exists in the software:
Does the vulnerable code actually matter in this running application?
See how Raven makes starting with SCA practical, without the CVE backlog that usually comes with it.
