What is Software Composition Analysis (SCA)?

Most of what you ship was built by people you’ll never meet; we’re all shipping code that we didn’t write, didn’t explicitly accept, or didn’t review.

Even the most trivial-looking software builds can pull in large volumes of external packages. The external package imports trigger updates to your lock files, which your pipeline transforms into a dependency tree you didn’t explicitly request. The tree is packaged into a container image, which is then pushed and rolled into production. Nobody reads the code of all of the extra dependencies. Nobody could. This is a trade-off we’ve all agreed to so we can work efficiently.

Attackers exploit this trade-off decision. ReversingLabs reports a more than 1,300% increase in active threats based on open-source packages and repositories between 2020 and 2023. It only takes one compromised upstream component to introduce a downstream incident at scale, without anyone on your team touching or modifying your application’s source code. The default “we’ll catch it in prod” strategy won’t scale fast enough.

Software composition analysis (SCA) is built for this reality. SCA tools identify all of the components and dependencies you’re shipping, map them to known vulnerabilities and license compliance issues, and wire the findings into your automated workflows. They’ll start with feedback in your IDE and cover pull request checks, CI/CD gates, build pipeline automation, dashboards, and continuous monitoring tools. The strategy is straightforward: catch the obvious stuff as soon as possible, when it’s easier to address, and fold the work smoothly into your development flow so you can avoid risk without slowing velocity.

What is Software Composition Analysis (SCA)?

Software composition analysis produces the ingredient labels for the software you ship.

Done correctly, SCA scans will enumerate all third-party software and components in your pipeline, highlighting open-source dependencies. It will then map them to all of the relevant information you want for them: known vulnerabilities and license data. Some folk treat SCAs as an academic exercise, but they’re actually a deeply operational and actionable component in your stack. You’ll want SCA scans so you can make informed decisions about vulnerabilities: keep them out of your releases to begin with, address them appropriately if they end up there, and proactively learn about your software supply chain before problems become urgent incidents.

SCA complements other kinds of security applications and scans you might be running. You’ll want to use static application security testing (SAST) tools for deep checks of your application’s source code, near the start of your CI pipeline. And dynamic application security testing (DAST) tools to black-box test your application and infrastructure (mimicking HTTP requests, fuzzing inputs, and so on), checking the final results. SCA sits between them, looking at everything you’re shipping that you didn’t write yourself. That’s the majority of your software by volume of written code, and is an attractive target for attackers, who don’t have to expend any effort to understand your software if it includes a compromised component they already know how to exploit.

SCA, in practice, translates to workflow controls:

• Developers learn of dependency issues early (real-time checks in the IDE or in pull requests) and can route around them

• CI/CD pipelines can enforce policies and raise alerts for anything that sneaks through or isn’t related to a code change. They can block releases with critical common vulnerabilities and exposures (CVEs), refuse unknown package inclusions, block license mismatches, and so on

• Security teams get a consistent, continuously updated view of exposure across attack surfaces, with notifications as soon as vulnerabilities drop into repos, services, or pipelines

SCA done well blends into your processes without slowing you down. It stays out of your way and only stops the line when it has to (because a release has become too risky), as part of a normal release flow, and in ways that can be addressed during regular business hours.

How does software composition analysis work?

Software composition analysis works by running automated scans of applications to inventory dependencies and open-source components. Then it pattern-matches what it finds against fingerprints and metadata associated with known open-source software (OSS) codebases. After identifying matches, SCA tools correlate component versions with vulnerabilities from databases, registries, and security advisories to generate detailed reports listing components and their associated CVEs. Developers and security teams then have a concrete view of what’s in each build and which upstream risks are coming along for the ride. Some tools will also generate a software bill of materials (SBOM), a standardized and automatically managed inventory that is increasingly important for regulatory compliance.

SCA scans usually work best when they assess application source code, since raw source code more closely matches what’s included in your shipped product, and package names and versions can be explicitly resolved from manifests and lockfiles. However, some SCA tools can also scan application binaries (ie, compiled source code) and identify similarities based on binary fingerprints. Scanning binaries is generally less efficient than scanning the source, since binary matching relies on fuzzy heuristics to decide what’s in your shipped product, and can yield conflicting reports or false positives. But it works as a good fallback for proprietary components where the source might not be available.

Why software composition analysis matters

Based on the insights that SCA offers, businesses are better positioned to handle three practical risks:

1. Security vulnerabilities: SCA scans can find and flag third-party software components affected by CVEs. These are vulnerabilities that are both easy to remediate, since patched versions are usually available and straightforward to install, and high risk, since attackers know about them and how to use them.

2. Licensing risks: Third-party code can trigger license obligations, including open-source related ones. That can mean copyleft terms that conflict with your distribution model, missing attribution/NOTICE requirements, or internal policy violations that show up late and block a release. Your dependency risk might come with legal risk, license compliance violations, and issues that act as last-minute release blockers.

3. Operational blindspots: Organizations that skip SCA will regularly ship components they don’t fully understand or control, but are responsible for. With SCA, you shine a light into the darkest corners of your shipped products: the transitive dependencies that are pulled in through CI/CD pipelines.

In a perfect world, software composition analysis scans wouldn’t be necessary because your build pipelines would continuously generate SBOMs and keep them up to date with each release. In the real world, SBOMs are often incomplete, outdated, or missing. When generated without a matching SCA scan, SBOMs might overlook components, come from incomplete code inventories, or otherwise be inconsistent and attached to container images whose contents don’t match what the source repo implies.

SCA safeguards against these oversights by continuously inventorying an application’s “ingredients” and surfacing risky components early enough to address them.

Benefits of software composition analysis for DevOps teams

The biggest payoffs from using SCA come once it’s integrated into how your software is shipped. Platform teams own the guardrails, security teams own the risk models, and developers own the initial change. DevOps teams connect all of these together and own the underlying pipelines. For SCA, this connection happens through a shared artifact: a continuously updated view of what’s in the build, along with checks that run before the build makes it to production.

SCA turns dependency scanning into outcomes that you find useful and care about:

• Fewer vulnerable components make it to production. Catch problems in source code, PRS, and pre-release gates

• Less noise and faster prioritization. Orient your focus toward the dependencies that matter most, with a smaller backlog that’s focused on actionable findings

• Audit readiness without stalled delivery. Audit-specific evidence is available continuously and on demand.

• “No surprises” license compliance. Detect license obligations before they become painful to fulfill, and keep ownership clear throughout

• Staying current without rework. Components become linked to upstream sources, and upgrades and remediations are routine, avoiding emergency re-work of component trees

Checks that only run after a deploy are only effective as receipts. SCA goes beyond documenting what you’ve shipped by reaching into your DevOps pipeline and supporting policy controls before release.

Software composition analysis vs. other application security testing methods

Software security work is messy and can get very complicated very quickly. Different tools and strategies solve different problems. To avoid surprises in production, you’ll want to cover everything, including custom source code, running behavior, and third-party components. Crucially, all of this monitoring has to be smoothly wired into your CI/CD workflow. The table below shows how SCA fits in and compares to other methods and strategies.

These kinds of security protections are not interchangeable. They complement each other and stack: SAST prevents obvious shipping problems, DAST/IAST catches issues that only show up after the app is live, and SCA covers work you didn’t author but are still deploying. Let’s now look at turning SCA actionable and working within its limitations and boundaries.

Common software composition nnalysis challenges and limitations

SCA is easy to buy and integrate: your scans run, the reports land, and you can keep track of them over time. What’s harder is downstream, when SCA shows up as noise in your reports, blocked upgrades, and findings that aren’t cleanly actionable.

• False positives, noise, and alert fatigue: Most SCA tools will start with presence-based matching: “these dependencies are associated with a CVE.” Some of the CVEs you’re alerted on are false positives, since, for example, they’re not exploitable, or they might not be reachable from the application, or you may not use the vulnerable functionality and code paths. High volumes of alerts generate noise, and the resulting triage work is time-consuming and can lead to teams flooding out on alerts (alert fatigue). To manage noise, rely on reachability analysis (which we discuss in depth in the FAQs) to eliminate alerts for vulnerable functions your code never actually uses.

• Remediation bottlenecks: The scanner is usually right, but there’s no guarantee you can actually do anything about the issues it finds. There might not be an upstream patch you can apply, or the patch may require a breaking upgrade. Or the vulnerable component is deep in a transitive dependency tree, you can’t modify in any way. You’ll face an expensive decision: accepting risk, forking and patching, or replacing a dependency. The earlier in the software development life cycle (SDLC) you’re alerted to a finding, the easier it is to avoid blocking a release and dragging multiple teams into a coordinated fix.

• Gap between detection and actionable response: Your scans tell you where you’re vulnerable. Turning that information into a plan of action is significant work and will require owners, prioritization, timelines, and figuring out how to safely make changes. You’ll want to create and iterate on remediation workflows and clear accountability flows, which tie into scan results.

• Transitive dependency challenges: Your dependencies have dependencies, and those also have dependencies. Tracing vulnerabilities through your dependency graph is hard and necessary work that helps identify the scope and impact of a vulnerability, as well as the remediation effort required. SCA scans expose your dependency graph to your team and help drive the work to simplify, reduce, and intentionally manage it.

These problems are well-known and manageable, and highlight that scanning should help inform prevention. SCA shines when paired with safer, better-managed inputs, and can drive and inform the work that will get you there.

Tips for choosing a software composition analysis tool

SCA tools are optimized to find as much information as they can, so most will find something. It’s important to find ones that align with your priorities and help you ship safer software within your context. Treat selection like you’d treat other delivery critical controls: optimize for signal, workflow fit, and how quickly you can move flow from finding to decision to remediation.

Ask questions like these:

• Where does it run? You’ll want to cover IDE, pull requests, CI/CD pipelines, and continuous monitoring after release. Partial coverage is unlikely to change outcomes.

• How good is the signal? Does it work well with reachability analysis and other contexts to reduce false positives? Is there a way to convert the presence signals into a priority list?

• Does it cover your ecosystem? Itemize your languages, package managers, build patterns, repositories, and so on. Are there any significant gaps, and can you accept them?

• Can it produce and ingest SBOMs? What does it take to export an SBOM, reconcile it with SBOMs from upstream tools and containers, and integrate it into your system-wide inventory?

• What does remediation look like? How or what will you have to do to turn a finding into clear guidance for a fix? Can you integrate it into ticketing systems, ownership routing, and meet relevant SLAs? How do you avoid turning temporary policy exemptions into permanent loopholes?

• Does it help prevent bad inputs? Once a source is tagged as risky or a component is tagged as untrusted, can you block them? Does it automatically prevent bad inputs from entering your build?

Prioritize tools that you can operationalize. You’ll want your DevOps, security, platform, and development teams to agree on thresholds, ownership, and pipeline gates, and to find ways to integrate the scanner into their flows that work for them. It should work as a control and avoid spamming your backlog.

Securing the software supply chain with Chainguard

SCA helps you see what’s in your software supply chain. It’s not how you fix the risky parts that keep coming into your systems from upstream. With Chainguard, you fix your systems before SCA by reducing the risks that make it into your builds in the first place.

Used together, the workflow is straightforward. Chainguard tightens upstream components, and SCA tools verify Chainguard’s work and catch what’s left:

• Fewer findings to triage: Secure-by-default components reduce the volume of known vulnerabilities and recurring CVEs that SCA scans will surface.

• Lower severity issues when they appear: A smaller, tighter dependency footprint means lower numbers of high-impact packages are included, and there’s less transitive dependency sprawl.

• Cleaner CI/CD gates: When your baselines are safer, you can enforce stricter, simpler gates without risking spammy security exceptions at every release.

• Better signal-to-noise for security teams: Preventable issues in your base layers are pre-emptively addressed, so they disappear from your SCA tool output. This means more of your SCA output is focused on application-level risks and related to the work you are actually doing.

• Faster remediation loop: Chainguard's daily container builds make it easy to swap up-to-date images into your flows, unblocking upgrade chains for long chains of upstream dependencies.

Use Chainguard as a complement to your SCA scans. You can reduce the blast radius for found issues and shrink your backlogs. Contact Chainguard’s team to learn more.