Modern applications aren’t built from scratch. They’re assembled. When you look at most typical enterprise codebases, you’ll see that they contain a large proportion of open source elements, many of which are pulled from public repositories and bolted together with their own proprietary logic.
From a development perspective, this approach makes a lot of sense. Why spend months writing out your own authentication libraries or data parsing various functions when you can just plug and play with versions that already exist?
Well, the core issues boil down to security. Each component has its own security history, and within that history there is the potential for vulnerabilities, outdated dependencies, and in the worst cases, deliberate malicious code waiting for its next victim.
The Open Source Paradox
Open source adoption has been accelerating at pace for a while now, and the reasons make sense. A lot of development teams are facing increasing pressure to hit tight delivery deadlines as CI/CD cycles speed up, but the problem is that developers are often forced to ship faster while not being given all of the resources they need to get the job done to a high standard of quality.
And given today’s talent shortage in the software dev market, especially amongst more senior roles, building everything in-house is just no longer feasible.
While going open source solves many of these issues (since devs don’t need to build everything from scratch), it creates a visibility problem. Security teams don’t have real insights into what’s actually running in production, and one single app could be published with dozens or even hundreds of different dependences, each with their own set of sub-dependencies.
When a critical vulnerability drops, like the Log4j incident, security teams around the world end up scrambling to figure out whether that component even exists anywhere in their codebase.
Without clear visibility into dependencies, it can be extremely tricky to figure out your exposure. This is where software composition analysis comes into play.
What Software Composition Analysis Actually Does
Software composition analysis tools perform a systematic inventory of third-party components within your codebase.
Instead of relying on developers to keep accurate, manual records of everything they’re using (which isn’t always practical), software composition analysis automates the entire inventory process.
Building the Inventory
Taking a look into how software composition analysis works, the tools initially begin by scanning your applications to identify all of the different open source components that are being used. This means inspecting things like containers, binary files, package managers, and the source code itself.
This process looks at both direct dependencies (such as libraries that your devs explicitly added) and transitive dependencies (libraries that came along for the ride as dependencies of other libraries). The latter category often accounts for the majority of your actual exposure.
Matching Against Known Vulnerabilities
Once you’ve got your inventory built up, software composition analysis tools can then run cross referencing checks for each of your components against known vulnerability databases.
If any of your components match, you can get instant visibility into these potential weak points and further inspect them to see if they are affected and what the correct course of action should be.
Checking Licence Compliance
Software composition analysis tools also look past known vulnerabilities and check the current status of licensing. Just because something is open-source doesn’t automatically mean that it’s free.
Components come with obligations ranging from permissive (MIT, Apache) to restrictive (GPL variants that may require you to open source your own code). For organisations in regulated industries or those with strict IP policies, this compliance check is arguably as important as the security scan.
Integration Points That Matter
If you want to get the most out of your software composition analysis, you need to put some thought into where it sits within your workflow.
The first point to keep in mind is not to simply run scans as an afterthought. It can be easy to prioritise other tasks, but running scans once a quarter as part of an audit is going to surface issues too late. By that point, vulnerabilities may have already been deployed and issues become more costly to fix.
Some of the most effective implementations happen when software composition analysis is introduced into the CI/CD pipeline so that every build triggers a new scan. Developers receive immediate feedback when they introduce a problematic dependency. This shifts the remediation cost left, catching issues while they’re still cheap to fix.
Some organisations go further, integrating directly into developer IDEs. This surfaces risks at the point of import, before code even reaches the repository. The trade-off is potential friction. Developers may push back if tooling slows their workflow and takes them away from coding. Getting this balance right usually requires close collaboration between security and engineering teams.
Prioritisation: Separating Signal From Noise
As most security teams will be quick to tell you, one of the most common (and frustrating) challenges of bringing in new tools is alert fatigue. And a poorly executed software composition analysis implementation could easily flag hundreds of vulnerabilities across a wide-spread codebase, giving teams more of a headache than they had before.
Context Matters
Not every vulnerability represents the same risk, neither are they equally as exploitable. Some affected code paths may never actually execute in your application, so you can deprioritise those for more pressing needs.
Newer SCA tools attempt to trace whether vulnerable functions are actually reachable at runtime. This is a technique sometimes called exploitable path analysis and it helps teams focus their remediation efforts towards the issues that genuinely matter.
Severity Ratings Aren’t Gospel
Ratings from vulnerability databases provide a useful filter, but it’s a good idea to treat these as solid starting points rather than gospel.
A “critical” vulnerability in a component you use in a sandboxed internal tool presents a much different level of risk than a “medium” issue in customer-facing authentication code.
Practical Steps for Implementation
If your organisation is starting to think about implementing a software composition analysis process, here are a few things to keep in mind.
Start with visibility. Before you optimise anything, you need to have a full understanding of what currently exists inside all of your apps. You might be surprised once you peel back the layers and find out the extent of your open source footprint once you run an initial scan.
Automate where possible. Manually tracking your entire codebase and updating your record after every build is not a tactic that scales well. Instead, integrate software composition analysis into your existing workflows and try not to see it as a standalone exercise.
Define clear policies. What licence types are acceptable? What’s the threshold for blocking a build versus logging a warning? These decisions should be explicit and consistently enforced.
Maintain an SBOM. Software bills of materials have moved from nice-to-have to regulatory expectation in some sectors. SCA tools can generate these automatically, providing documentation for customers or auditors who ask what’s inside your products.
The Bottom Line
Open source isn’t going away, and nor should it. But using third-party components responsibly requires knowing what’s in your codebase and staying on top of emerging risks.
Software composition analysis gives security teams that visibility. The organisations that see the most value from it are those embedding SCA into development workflows rather than treating it as a quarterly audit.
