SAST vs SCA: The Key Differences

Layering these two approaches to code security might seem like a non-negotiable part of a more secure development lifecycle. However, when considering security tools like static application security testing (SAST) and software composition analysis (SCA), it’s easy to get caught in high-level definitions that don’t answer the real question: What do they actually solve, where do they overlap, and where do they leave gaps? We’re looking at how their strengths and limitations align — or fail to align — with modern security challenges. We’re also examining how tools like Upwind’s shift-left capabilities address infrastructure security in ways that these two traditional approaches cannot for overall more comprehensive cloud-native security.

What are SAST and SCA? 

First off, here’s a quick recap of both SAST and SCA.

SAST is a security testing methodology that analyzes application proprietary source code, bytecode, or binaries without executing the code. Its goal is to identify vulnerabilities early in the software development lifecycle (SDLC). It’s code-level scanning that catches issues like SQL injections, cross-site scripting (XSS), improper authentication, and buffer overflow.

SCA evaluates risks in open-source dependencies. SCA tools scan the dependency tree of an application, identifying risks associated with the use of open-source libraries. SCA tools scan for issues including license compliance, version tracking, and known vulnerabilities in dependencies. Many include SBOM explorers in their feature sets, so teams get visibility into software dependencies.

Both are key to securing software during development. Their similarities don’t stop there: SAST and SCA both:

• Shift-left: Both are designed to identify vulnerabilities early in the software development lifecycle (SDLC) when remediation is faster and easier.
• Work in the pre-deployment environment: Both operate before the application is live, working within CI/CD pipelines to integrate security checks.
• Are built for developers: They are targeted at developers and AppSec teams to improve the security of the codebase.
• Support risk reduction goals: Both work to prevent security incidents by identifying potential weaknesses attackers might exploit after applications are live.
• Help automate a secure pipeline: Both tools automate security scanning.
• Support compliance: Both help organizations meet regulatory and compliance requirements by identifying and documenting security risks.

But because SAST and SCA scan different types of code, looking for different types of risks, they come with significant differences, too:

• Their reports require different kinds of remediation. SAST reports take developers time to analyze, triage false positives, and fix custom code issues. With SCA, flagged vulnerabilities, such as updating a dependency or switching libraries, may be quicker to act upon.
• Their integration points differ. SAST tools integrate directly into the environments developers use to write code, allowing SAST to scan code continuously or on demand and detect vulnerabilities as developers write and modify code. SCA integrates with package managers or build managers to scan code in dependencies. Developers check alerts and integrate changes during builds or dependency updates.
• Their relationship to runtime differs. SAST rarely applies beyond the pre-deployment phase, though it may still inform future remediation and updates. On the other hand, SCA provides ongoing value by tracking new vulnerabilities in dependencies after deployment, when it continues to monitor for updates to known vulnerabilities in dependencies. Despite ongoing monitoring, SCA won’t be able to contextualize risks in running systems.

What are the Challenges of SAST and SCA?

SAST emerged in the 2000s to address growing concerns about vulnerabilities in custom application code that coincided with the rise of web applications and the increasing sophistication of attacks like SQL injection and cross-site scripting (XSS).

SCA followed on its heels by the 2010s, following the exponential adoption of open-source software. High-profile vulnerabilities like Heartbleed (2014) and Log4Shell (2021) further highlighted the need for managing risks in third-party dependencies.

Today, 75% of breaches are aimed at components of the software supply chain.

It’s undeniable: both innovations furthered a wave of necessary security goals, from handling open-source risks to staying on top of an ever-growing list of known vulnerabilities. But as environments became even more complex, new challenges emerged from both tools, as well as from stitching together a host of other piecemeal solutions. 

Today, teams working with SAST see benefits, from cleaner proprietary code to earlier detection of vulnerabilities and reduced remediation costs and compliance support. However, they’ll also contend with drawbacks:

• False Positives: SAST can generate multiple false positives, requiring manual attention and adding to developers’ workload.
• Limited to Custom Code: SAST does not address risks from third-party dependencies or runtime environments.
• High Tuning Effort: SAST requires fine-tuning to reduce noise, especially in complex codebases.
• Performance Impact: SAST can slow CI/CD pipelines if not optimized for large or complex projects.
• Static Scanning: SAST lacks context for runtime behaviors or infrastructure risks, which leads to missed vulnerabilities.
• Skill and Resource Dependency: Skilled developers must interpret and act on SAST findings. 

Teams that choose SCA will see benefits like visibility into dependencies, vulnerability identification, licensing compliance, and potentially fast-fix recommendations. But they’ll also see drawbacks:

• False Positives: SCA may flag vulnerabilities that aren’t exploitable in the specific context.
• Limited to Dependencies: SCA does not cover custom code vulnerabilities or runtime issues.
• Complex Dependency Chains: SCA can scan the entire dependency tree, but developers can’t update transitive dependencies, and complexity in supply chains with multiple dependencies remains an ongoing challenge, even with SCA.
• Build Pipeline Delays: SCA scanning of dependencies during builds can slow down CI/CD pipelines.
• Resource Intensive: SCA requires time and expertise to interpret results and apply recommended updates.
• Version Conflicts: Updating libraries to resolve vulnerabilities can sometimes break functionality due to dependency mismatches.

So while the different code targets of SAST and SCA mean the two tools aren’t direct competitors, using both won’t solve both coding issues entirely. Gaps remain. Neither addresses misconfigurations in infrastructure-as-code (IaC) templates (e.g., Terraform, Kubernetes manifests) or runtime environments, for example. Neither do these tools handle runtime risks and behavioral anomalies, so resources still face risks from workload drift, zero-day exploits, and API misuse. At the same time, neither tool contextualizes vulnerabilities in context with specific environments. Runtime insight is also key to filling this gap. Here’s an overview:

Gap	SAST Coverage	SCA Coverage	Unaddressed Risk
Infrastructure Security	No	No	Misconfigured IaC, insecure cloud settings
Runtime Risks	No	No	Zero-day exploits, workload drift, behavioral anomalies
Exploitability Context	Partial	Partial	Prioritizing vulnerabilities based on actual risk
API Security	Partial	Partial	Live API misuse, shadow APIs
Business Logic Vulnerabilities	No	No	Logical flaws, authorization bypasses
Supply Chain Risks	No	Partial	Malicious packages, abandoned dependencies
Insider Threats	No	No	Misuse of credentials, phishing
Continuous Monitoring	No	No	Post-deployment vulnerabilities, drift detection

To fill the gaps, teams can look to IaC scanners, runtime monitoring, API security tools, and continuous monitoring (or comprehensive solutions like CNAPPs) for a more complete security posture.

How Does Shift Left Fit In?

While SAST and SCA offer significant value in securing code and dependencies, they leave multiple gaps in modern application security. These gaps are particularly evident in cloud-native environments, where risks extend beyond code and libraries into the infrastructure and runtime — misconfigurations in IaC templates, workload drift, and API misuse often go unnoticed by these tools. 

That’s where shift-left infrastructure security steps in, bridging the divide between traditional application-focused tools and the realities of cloud-native ecosystems. By integrating infrastructure and deployment security into the pre-deployment phase, runtime-informed development phase security gets a boost. 

Here’s how SAST, SCA, and Upwind’s shift-left approach compare:

	SAST	SCA	Upwind Shift Left
Primary Focus	Custom code vulnerabilities	Open-source dependency risks	Running “what if” scenarios pre-deployment based on real environmental context.
Key Use Case	Secure coding practices	Manage open-source vulnerabilities	Determining if a deployment is safe to push to production.
Stage in the software development lifecycle (SDLC)	Development (coding phase)	Dependency management	Non-blocking CI/CD step.
Target Risks	Code flaws (e.g., SQL injection)	Vulnerable libraries, license risks	Image vulnerabilities
Audience	Developers, AppSec teams	Developers, AppSec teams	DevSecOps, cloud engineers
Output	Code vulnerability reports	Dependency vulnerability reports	Streamlined deployment recommendations
Cloud-Native Fit	Limited: App-level focus only	Moderate: Manages libraries but not infrastructure	Strong: Tailored for cloud-native environments

Teams using SAST will likely see cleaner, more secure codebases that adhere to secure coding standards like OWASP Top 10. However, SAST does not address risks outside the proprietary code (e.g., runtime configurations or dependencies).

SCA users will get visibility into the full dependency tree, with actionable insights to update or replace vulnerable components. They’ll strengthen supply chain security but find they’re still challenged by gaps in detecting custom code issues and runtime misconfigurations.

Upwind’s shift-left capabilities are designed to address some of these gaps, affording teams reduced risks from infrastructure misconfigurations like overly permissive IAM roles, unencrypted data storage, or exposed secrets. Those additional protections prevent security incidents that stem from improper deployment setups, which neither SAST nor SCA can address. 

In other words, while SAST looks at what developers write and SCA focuses on what developers borrow, Upwind Shift-Left focuses on where applications run.

Upwind Adds Runtime Context to SAST and SCA

By leveraging runtime context for pre-deployment scans, Upwind is transforming how vulnerabilities are prioritized and remediated. While SAST and SCA focus on identifying risks in code and dependencies, they can’t tell which vulnerabilities are truly impactful in a live environment. Upwind bridges this gap with workload behavior, API usage patterns, and infrastructure configurations, all to inform better pre-deployment scanning. 

Want to connect the static world of code with the dynamic reality of your cloud-native runtime environment? Get a demo today.

FAQ

What is SCA in NIST?

In the NIST (National Institute of Standards and Technology) cybersecurity framework, SCA is part of the specific controls related to supply chain risk management (SCRM). NIST emphasizes the importance of understanding and managing the risks associated with software supply chains, and SCA can fulfill this role by:

• Finding vulnerabilities in open-source and third-party libraries.
• Ensuring compliance with licensing and regulatory requirements.
• Tracking dependencies to mitigate risks related to abandoned or malicious components.

NIST includes the Secure Software Development Framework (SSDF) which outlines practices for secure software development, and includes relevant guidance that teams should take the following actions, all of which can be achieved through SCA:

• Create and maintain Software Bills of Materials (SBOMs).
• Scan dependencies against vulnerability databases like the NVD (National Vulnerability Database).
• Monitor and remediate risks across the software lifecycle.

What is the difference between SAST and DAST?

SAST analyzes an application’s code without executing it, in order to identify vulnerabilities like SQL injection and hardcoded secrets during the development phase. In contrast, DAST (Dynamic Application Security Testing) simulates attacks on a running application. In doing so, it can uncover runtime vulnerabilities like authentication issues and security misconfigurations. Here’s what it looks like:

• SAST works pre-deployment and targets code-level flaws to ensure secure coding practices.
• DAST operates post-deployment and tests the application’s behavior under simulated real-world attacks.
• SAST is prone to false positives due to the lack of runtime context, while DAST may miss untested paths, leading to false negatives.

Together, they address static and dynamic vulnerabilities, providing complementary layers of security. Organizations may use the 2 approaches together to cover a greater portion of the software development lifecycle, from development to deployment.

What is the difference between SCA and DAST? What about  DAST and PT?

SCA is a tool for analyzing dependencies in the software supply chain. It works during the development phase and relies on dependency data and vulnerability databases. 

DAST handles applications after deployment, testing them for runtime vulnerabilities like input validation flaws, authentication issues, and misconfigurations. It tests a deployed application’s behavior and environment, using dynamic inputs not visible in static scans.

By combining SCA’s supply chain insights with DAST’s runtime testing, teams address risks in both dependencies and live environments for more comprehensive app security.

Where does penetration testing (PT) fit in? While DAST tests applications, PT involves the manual or semi-automated simulation of attacks on applications, but also networks and systems. It evaluates overall security posture from business logic vulnerabilities to infrastructure weaknesses and social engineering risks. On the other hand, DAST focuses specifically on application-level vulnerabilities like authentication issues and runtime misconfigurations.

Overall, PT is a more manual and comprehensive approach that looks at the entire security posture. Both might be used together to secure different layers and aspects of cloud security.