What Is Software Composition Analysis (SCA)?

Software Composition Analysis (SCA) scans open source components for vulnerabilities, license risks, and supply chain threats across your application portfolio.

What Is Software Composition Analysis?

Your development team just pushed a critical update to production. The deployment succeeds. Three weeks later, you discover that the update contained a vulnerable open source component actively exploited by attackers. The library was versions behind, flagged as critical in the National Vulnerability Database, and sitting in a transitive dependency you didn't know existed.

Open source software now comprises the overwhelming majority of enterprise codebases, with applications carrying hundreds of dependencies. Software Composition Analysis (SCA) exists to prevent these blind spots from becoming breach vectors. SCA is the process of analyzing software to identify, assess, and manage risks in open source and third-party components. You scan source code, binaries, or package manifests to inventory every component, compare them against vulnerability databases, check license compliance, and generate actionable reports. When integrated into your CI/CD pipeline, SCA stops vulnerable components before they reach production.

Understanding what SCA does is only half the picture. The scale of open source risk in modern software development explains why it has become a security requirement rather than an optional tool.

Why Software Composition Analysis Matters

Open source components carry real risk at a scale most teams underestimate. According to CISA's Open Source Software Security Roadmap, one study found open source software present in 96% of studied codebases across various sectors. NIST has acknowledged a growing backlog of vulnerabilities submitted to the NVD and requiring analysis, with CVE submissions increasing 32% in 2024 alone. This backlog leaves organizations unable to properly prioritize risks using standardized severity guidance.

Recent supply chain attacks make the case:

• The SolarWinds attack in 2020 compromised software build processes, affecting 18,000+ organizations including U.S. government agencies and Fortune 500 companies.
• The Log4Shell vulnerability (CVE-2021-44228) in December 2021 exposed millions of applications using the Log4j library to remote code execution. CISA called it one of the most serious vulnerabilities ever discovered.
• The Codecov breach in 2021 compromised CI/CD pipelines, exposing credentials and source code from 29,000+ customers over two months before identification.

Each incident exploited weaknesses in open source components or build processes that SCA is designed to find. SCA integrates into your security operations as continuous monitoring, not point-in-time scanning. Your SCA platform alerts you when new vulnerabilities affect production components and blocks builds containing known vulnerabilities or incompatible licenses. When attackers inject malicious packages into public repositories, your SCA tools compare component hashes against known-good signatures to find supply chain tampering.

SCA is one of several application security testing methods. Understanding how it differs from SAST and DAST clarifies where each fits in your security program.

SCA vs SAST vs DAST

Application security testing splits into three distinct disciplines, each targeting a different attack surface in your codebase.

1. Software Composition Analysis (SCA) examines third-party open source components and libraries bundled into your applications. It identifies known vulnerabilities, license risks, and supply chain threats in code your team did not write. SCA scans package manifests, binaries, and dependency trees, then cross-references findings against vulnerability databases like the NVD and GitHub Security Advisories.
2. Static Application Security Testing (SAST) analyzes your proprietary source code for security flaws, including injection vulnerabilities, insecure data handling, and authentication weaknesses, in code your team did write. SAST operates without executing the application, scanning raw source or compiled bytecode to flag issues before runtime.
3. Dynamic Application Security Testing (DAST) tests running applications from the outside by simulating real attacks against live endpoints. DAST finds runtime vulnerabilities like cross-site scripting (XSS), broken authentication, and server misconfigurations that only appear when the application is deployed and responding to requests.

Capability	SCA	SAST	DAST
What it scans	Third-party/open source components	Proprietary source code	Running applications
When it runs	Build time, CI/CD, continuous monitoring	Development/build time	Post-deployment
Vulnerability types	Known CVEs, license conflicts, malicious packages	Code-level flaws (injection, auth issues)	Runtime exploits (XSS, misconfigurations)
Source access needed	Package manifests or binaries	Full source code or bytecode	No source access required

Enterprise codebases predominantly contain open source components, so you need all three. Run SCA first in your CI/CD pipeline to catch vulnerable dependencies before SAST examines your proprietary code, then validate with DAST against the deployed application. This layered approach covers both the code you write and the code you inherit.

With these distinctions clear, the next step is understanding the core components that make SCA platforms effective.

Core Components of Software Composition Analysis

SCA platforms combine four complementary capabilities that transform component inventories into actionable security intelligence.

1. Open source inventory and vulnerability identification forms the foundation. Your SCA tool performs deep dependency resolution, mapping the libraries you explicitly declared in package.json or pom.xml files along with the transitive dependencies those libraries pull in. According to the OWASP dependency guidance, the vast majority of vulnerabilities exist in transitive dependencies rather than the direct dependencies you control. The tool cross-references your inventory against multiple vulnerability databases including the National Vulnerability Database (NVD), MITRE CVE database, GitHub Security Advisories, and vendor-specific security bulletins.
2. License compliance management provides autonomous tracking of open source licenses across your software portfolio. The tool identifies license types and flags potential conflicts between incompatible licenses. Independent analyst evaluations identify license analysis and guidance as key criteria distinguishing enterprise-grade SCA platforms from basic scanning tools.
3. Software Bill of Materials (SBOM) generation creates complete inventories cataloging all components, versions, licenses, and relationships. Your SCA tool exports SBOMs in standardized formats like CycloneDX or SPDX, enabling interoperability across security tools and meeting regulatory requirements. According to NIST's Secure Software Development Framework (SSDF) guidance, organizations must autonomously generate SBOM and SLSA provenance documents during development to identify, assess, and stop software supply chain risks.
4. Policy enforcement and risk prioritization ties the other three capabilities together. Your SCA platform applies configurable policies that block builds containing high-severity vulnerabilities, unapproved licenses, or components from untrusted sources. Advanced implementations add reachability analysis and exploit prediction scoring to prioritize findings by actual risk rather than raw vulnerability counts.

Together, these capabilities form the engine that drives SCA scanning and analysis in practice.

How Software Composition Analysis Works

Scanning Techniques

Modern SCA uses four complementary scanning techniques to build a complete picture of your software's composition:

1. Manifest parsing examines package manager files like package.json, pom.xml, or requirements.txt for declared dependencies.
2. Source code scanning analyzes application code to find imported libraries.
3. Binary analysis examines compiled applications when source access is limited.
4. Dependency tree mapping builds complete dependency graphs spanning multiple layers.

You place SCA early in your CI/CD pipeline, before SAST and DAST activities, to stop vulnerable libraries from reaching production. Autonomous checks run on all artifacts in pull requests, including dependency manifests.

Matching and Remediation

Your SCA tool performs multi-database correlation, querying the NVD, CVE databases, GitHub Security Advisories, and vendor-specific security bulletins to maximize coverage. The matching workflow catalogs all components with exact version information, queries consolidated vulnerability databases to determine if installed versions fall within vulnerable ranges, and analyzes license types for policy conflicts.

Effective SCA also generates remediation recommendations: version upgrade paths, patch availability analysis, and risk scoring. Your platform should provide autonomous guidance on the minimum safe version that addresses vulnerabilities. This workflow operates continuously, monitoring components already in production and alerting you when new vulnerabilities are disclosed.

These scanning and matching capabilities form the technical foundation. The tools built on top of them deliver the operational value security teams rely on daily.

Core Capabilities of SCA Tools

SCA tools translate raw scanning data into operational security workflows your team can act on. Five capabilities separate effective SCA tools from basic dependency scanners.

• Continuous monitoring and alerting tracks your deployed components against newly disclosed vulnerabilities in real time. A component that passed every check at build time can become a critical risk the moment a researcher publishes a new CVE. Your SCA tool should alert you within hours, not wait for the next scheduled scan.
• CI/CD pipeline enforcement gives you policy gates that fail builds automatically when critical vulnerabilities or unapproved licenses are found. This shifts remediation to the point of introduction rather than post-deployment discovery, when fixes cost a fraction of production hotfixes.
• Reachability analysis determines whether your application actually invokes the vulnerable code paths within a flagged dependency. A library may contain a known CVE, but if your application never calls the affected function, the practical risk drops significantly. This analysis cuts through alert noise and focuses your remediation effort where exploitability is real.
• Automated remediation guidance provides actionable upgrade paths, including minimum safe versions, patch availability, and breaking-change warnings for each finding. Instead of dumping a vulnerability list on your developers, effective SCA tools surface the specific fix and its downstream impact on your dependency tree.
• SBOM export and compliance reporting generates machine-readable inventories in CycloneDX or SPDX formats, supporting audit trails and regulatory requirements. These reports map every component, version, license, and relationship in your application portfolio, ready for internal audits, customer requests, or federal compliance submissions.

These operational capabilities deliver measurable security and compliance outcomes across your application portfolio.

Key Benefits of Software Composition Analysis

When implemented effectively, SCA delivers three measurable outcomes that justify adoption across security, compliance, and development teams.

• Vulnerability visibility across supply chains: SCA gives you fundamental visibility into dependencies, identifying vulnerabilities, licenses, and component sources. The volume of malicious packages published to open source registries has grown sharply year over year, with attackers increasingly targeting npm, PyPI, and other repositories to distribute malware through the software supply chain. NIST has acknowledged a growing NVD backlog that leaves many new CVEs without severity scores, limiting the ability of existing security tools to properly assess risk. SCA fills this gap through proprietary vulnerability intelligence and reachability analysis.
• Regulatory compliance enablement: Federal frameworks now require SBOM and vulnerability monitoring capabilities that SCA tools provide. NIST's Secure Software Development Framework requires organizations to autonomously generate SBOMs and SLSA provenance documents during development. Executive Order 14028 elevated software supply chain security from optional best practice to compliance requirement affecting federal contracting eligibility. The EU Cyber Resilience Act requires products with digital elements to be developed using secure-by-design practices.
• AI-amplified risk prevention: AI-assisted development is increasing the speed of dependency changes while introducing new error patterns. A peer-reviewed study published through USENIX found that code-generating LLMs exhibited package hallucination rates averaging 19.6%, recommending software packages that do not exist. AI agents can amplify risk because they fail to check provenance, policy, or known-malicious indicators. SCA provides the governance layer that stops AI coding assistants from introducing vulnerable or malicious components at machine speed.

Even with these benefits, license compliance remains one of the most underestimated risks that SCA addresses.

Software Composition Analysis and Open Source License Compliance

Open source license violations carry real legal and financial consequences. According to CISA's guidance on SBOM consumption, violating an open source license can result in sale suspension or recall of software, fines, and reputational damage. These consequences can cascade to downstream organizations through unplanned costs or abrupt loss of support.

Every open source component carries a license, and licenses fall into two broad categories:

• Permissive licenses like MIT, Apache 2.0, and BSD allow you to use, modify, and distribute code in proprietary applications with minimal obligations, typically just attribution.
• Copyleft licenses like the GNU General Public License (GPL) impose stricter requirements: if your proprietary code becomes a derivative work of GPL-licensed components and you distribute the combined work, that derivative work must also be released under the GPL. This "viral" effect can force disclosure of proprietary source code you never intended to open source.

The risk multiplies through transitive dependencies. Installing a single package can pull in dozens of additional dependencies, each carrying its own license. Your application may contain hundreds of license obligations spread across multiple layers of the dependency tree, and a single copyleft component buried three levels deep can trigger compliance requirements that affect your entire product. Manual tracking at this scale is not feasible.

SCA tools address this by autonomously scanning your codebase, identifying every license across direct and transitive dependencies, and flagging conflicts before they reach production. Your SCA platform should enforce license policies in the CI/CD pipeline, blocking builds that introduce unapproved license types and alerting legal teams when copyleft components appear in proprietary codebases. This level of governance is essential for organizations managing supply chain risk at enterprise scale, and especially critical during mergers and acquisitions, where undisclosed GPL usage discovered in due diligence has collapsed deals or triggered significant valuation reductions.

Despite these capabilities, SCA adoption comes with real challenges you need to plan for.

Challenges and Limitations of Software Composition Analysis

SCA is not a deploy-and-forget solution. Organizations encounter four recurring challenges that can undermine even well-funded implementations.

1. False positives and data accuracy issues: Poor vulnerability intelligence and imprecise matching undermine SCA effectiveness. Alert fatigue sets in when tools generate thousands of findings without distinguishing exploitable vulnerabilities from theoretical risks. The severity scoring gap, where many new vulnerabilities lack NVD scores, makes prioritization across enterprise application portfolios even harder.
2. Remediation prioritization complexity: You need to distinguish which of your many dependencies per application actually pose exploitable risk. CVSS scores alone are insufficient. You need EPSS (Exploit Prediction Scoring System) data, reachability analysis showing whether vulnerable code paths are invoked, and business context about application criticality. Most SCA implementations lack this kind of prioritization.
3. Transitive dependency complexity: The dependencies of your dependencies create cascading remediation challenges. Upgrading one component may introduce new vulnerabilities or break application functionality. According to the OWASP dependency guidance, development teams must fully understand the complete relationship chains from first-level dependencies through multiple layers to the vulnerable component. This requires application security skills many development teams lack.
4. Developer workflow integration friction: Security scanning that slows development velocity gets bypassed. When SCA tools generate reports developers must manually review across separate platforms, remediation stalls. You need IDE integration, pull request scanning with inline feedback, and risk prioritization using reachability analysis to reduce alert fatigue. Critical vulnerabilities often take extended periods to remediate despite autonomous identification, often because poor developer experience creates operational barriers.

These challenges are real, but most stem from implementation decisions rather than fundamental SCA limitations. Knowing where deployments fail helps you avoid the most common pitfalls during your own rollout.

Common Software Composition Analysis Implementation Mistakes

Enterprise security teams consistently make five mistakes when implementing SCA. Avoiding them significantly improves your return on SCA investment.

• Ignoring transitive dependencies: The majority of vulnerabilities exist in transitive dependencies, yet teams focus on direct dependencies. Attackers specifically target these hidden layers because they are less visible and outside direct developer control.
• Failing to prioritize by exploitability: Treating all CVEs as equally important creates overwhelming alert fatigue. Even if vulnerable code is reachable, what matters is exploitability. Yet many teams rely solely on CVSS scores without considering whether vulnerabilities are actually exploitable in their specific context. Imprecise vulnerability matching wastes remediation resources while actually exploitable vulnerabilities remain unaddressed.
• Neglecting license compliance: Many teams implement SCA tools focusing exclusively on vulnerability identification while ignoring license compliance risks. Commercial software audits routinely reveal license conflicts, including open source components distributed with no license or customized licenses that create ambiguous legal obligations. These risks can derail M&A transactions or trigger intellectual property litigation.
• Poor CI/CD integration: Running scans too late in the development lifecycle drives up remediation costs. Generating vulnerability reports without enforcing action by failing builds when critical vulnerabilities are found leaves gaps. Neglecting IDE integration removes findings from where developers work. Treating SCA as point-in-time scanning rather than continuous monitoring of components already in production misses newly disclosed threats.
• Lack of developer training: Without proper training, developers view security findings as obstacles rather than actionable intelligence. They don't understand transitive dependency risks, lack knowledge about different remediation strategies, and can't interpret vulnerability reports to determine actual risk. According to the OWASP dependency guidance, development teams require application security skills to analyze vulnerability impact and understand complex transitive relationships.

Avoiding these five mistakes puts you ahead of most enterprise SCA deployments. A set of proven best practices will strengthen your program further.

Software Composition Analysis Best Practices

The difference between SCA as a checkbox exercise and SCA as an effective security control comes down to five operational practices.

Implement risk-based vulnerability prioritization: Move beyond simple vulnerability counts to sophisticated risk assessment. Deploy tools that statically model call paths through transitive dependencies to determine whether there's a likely path from your code to the vulnerable component. Prioritize using multiple signals:

• CVSS scores for baseline severity
• EPSS scores for actual exploit likelihood
• Reachability analysis specific to your codebase
• Business context including criticality of affected applications

Complete SBOM management: Generate SBOMs in standard formats (CycloneDX or SPDX) with each build for interoperability and regulatory compliance. Enrich SBOMs with security-relevant metadata including component provenance, download locations, and cryptographic hashes to improve vulnerability management and track licensing obligations.

Shift-left security integration: According to NIST SP 800-204D guidance, organizations should run autonomous checks on all artifacts covered in pull requests, including security scanning. Place SCA early in your development lifecycle through scanning integrated into CI/CD pipelines before code reaches production. Configure tools to fail builds when critical vulnerabilities are found rather than generating reports developers might ignore.

Effective remediation workflows: Follow OWASP guidance requiring you to fully understand complete dependency relationships before attempting remediation. For open source dependencies, consider creating patches and pull requests that protect your organization while helping others secure their applications. Implement continuous monitoring where your SCA platform watches for new vulnerabilities disclosed for components already in production.

Developer training: Foster education around open source risks so teams integrate SCA practices effectively. Train developers that transitive dependencies often introduce vulnerabilities without their awareness. Provide frameworks combining CVSS, EPSS, and reachability analysis rather than treating all vulnerabilities with equal urgency. Cover legal implications of different open source licenses and organizational policies for acceptable license types.

With these practices in place, the right platform turns SCA from a manual burden into an autonomous defense layer.

Strengthen Software Composition Analysis with SentinelOne

Singularity Cloud Security strengthens your vulnerability management by combining agentless scanning with its Offensive Security Engine™. The platform produces Verified Exploit Paths™ that simulate real attacker behavior to determine which vulnerabilities are truly reachable and exploitable in your environment, distinguishing between theoretical risks and actively exploitable weaknesses. This approach lets your security team focus remediation on the exposures that matter rather than chasing low-risk findings.

Shift-left with CI/CD

Integrate CNS into CI/CD pipelines so IaC templates, container images, and registries are scanned on every build for misconfigurations, vulnerabilities, and leaked secrets. Policy controls can block only the builds that introduce exploitable risk, keeping healthy releases moving and reducing the need for risky hotfixes and rollbacks.

Scan Code Repos, IaC, and Secure SaaS apps

Continuously scan repositories and pipelines configured with CNS scanning for more than 750 types of secrets across cloud platforms, payment providers, AI/LLM services, SaaS applications, and developer tools. Catching these issues before deployment reduces the risk of costly data leaks, service disruptions, and incident response efforts driven by compromised credentials.

Scan policies across popular platforms like AWS CloudFormation, Terraform, and Kubernetes (K8s) manifests, and Helm charts. SentinelOne includes over 1,000+ pre-built rules for out-of-the-box security checks. It is based on compliance frameworks like CIS, NIST, GPDR, and PCI-DSS. A custom policy engine lets your security team create and enforce custom rules using OPA/Rego scripts to meet business standards. You can also automatically detect API keys, cloud credentials, and authentication tokens within IaC files and repos.

Actively Validate Your Secrets

Traditional secret-scanning tools surface long lists of credentials, many of which are already rotated, revoked, or tied to decommissioned test services. CNS validates whether exposed secrets are still active and where they are in use, so teams avoid wasting time on old/non-redundant findings and low-impact keys. Remediation efforts are concentrated on a much smaller set of live, high-value credentials whose compromise could realistically result in data loss, fraud, or service disruption.

Get Code-to-cloud Exploit-path Context

Show developers exactly how a risk in code or CI/CD e.g. a misconfiguration, vulnerable base image, or leaked secret—can be used to reach specific cloud resources or sensitive data. CNS attaches exploit-path details and affected assets to each finding, turning generic alerts into fix-ready tickets that are easier to understand, act on, and prioritize.

Hyperautomation-driven remediation workflows

Use Hyperautomation to route prioritized, exploit-backed findings into Jira and other workflow tools with owners, severity, and context already attached. Security and engineering work from a single backlog.

Purple AI can use agentic reasoning to autonomously investigate alerts and prompt analysts to convert manual investigations into permanent hyperautomation workflows. SentinelOne can automate security actions across third-party tools like Jira, Okta, Slack, and ServiceNow with its 100+ pre-built integrations via the Singularity Marketplace.

The platform generates SBOMs that catalog components, libraries, and dependencies across your containerized and cloud workloads, supporting software supply chain transparency requirements under NIST SSDF and Executive Order 14028. Singularity Cloud Native Security scans code repositories, infrastructure-as-code templates, and container registries to identify vulnerabilities before they reach production, while its secrets scanning engine detects over 750 types of hardcoded credentials. This enables your security team to gain visibility across your cloud estate, identifying which applications contain vulnerable components and which require immediate remediation.

Request a SentinelOne demo to see how the Singularity Platform protects your software supply chain.

Key Takeaways

Software Composition Analysis has evolved from optional tooling into a federally mandated security practice backed by NIST's Secure Software Development Framework and Executive Order 14028. With enterprise codebases predominantly containing open source components with hundreds of dependencies per application, and industry research showing that a significant majority contain vulnerable components, SCA provides essential visibility. 

Effective implementation requires risk-based prioritization using reachability analysis, SBOM management in standard formats, shift-left integration with pre-commit scanning, and developer training on supply chain risks.

FAQs

What is SCA (Software Composition Analysis)?

Software Composition Analysis (SCA) is the process of scanning your software to identify, assess, and manage risks in open source and third-party components. SCA tools inventory every component in your codebase, including transitive dependencies, and compare them against vulnerability databases such as the NVD, MITRE CVE, and GitHub Security Advisories. 

The output includes vulnerability findings, license compliance checks, and actionable remediation guidance. When integrated into CI/CD pipelines, SCA stops vulnerable components before they reach production.

How Does Software Composition Analysis Relate to Cybersecurity?

SCA is a core cybersecurity control because open source components are the primary attack surface in modern applications. Attackers exploit known vulnerabilities in outdated libraries, inject malicious packages into public registries, and target transitive dependencies that developers never directly manage. 

SCA integrates into your security operations as continuous monitoring, alerting you when new vulnerabilities affect production components, blocking builds containing known CVEs, and comparing component hashes against known-good signatures to find supply chain tampering.

Why Is SCA Important in DevSecOps?

SCA is essential to DevSecOps because it automates security checks at the speed of modern development. When your team pushes code multiple times a day, manual dependency reviews cannot keep pace. SCA tools embedded in CI/CD pipelines scan every pull request and build, failing deployments when critical vulnerabilities or unapproved licenses are found. 

This shifts remediation to the point of code introduction rather than post-production discovery, reducing fix costs and keeping security enforcement continuous without slowing release velocity.

What Types of Vulnerabilities Does SCA Detect?

SCA detects known vulnerabilities cataloged in databases like the NVD, MITRE CVE, and GitHub Security Advisories, including remote code execution, injection flaws, authentication bypasses, and denial-of-service weaknesses in open source components. 

It also identifies malicious packages planted in public registries, components with tampered signatures indicating supply chain compromise, and license violations that create legal risk. Advanced SCA tools use reachability analysis to determine which of these vulnerabilities your application actually invokes.

Is SCA Required for Regulatory Compliance?

Federal and international frameworks increasingly mandate the capabilities SCA provides. NIST's Secure Software Development Framework requires organizations to generate SBOMs and SLSA provenance documents during development. Executive Order 14028 elevated software supply chain security to a compliance requirement affecting federal contracting eligibility. 

The EU Cyber Resilience Act requires secure-by-design development practices for products with digital elements. While regulations may not name SCA by tool category, SBOM generation, vulnerability monitoring, and supply chain risk management are all requirements SCA tools directly fulfill.

Does SCA Detect Transitive Dependencies?

Yes. Transitive dependency detection is a core SCA function. When you install a single package, it can pull in dozens of additional dependencies, each carrying its own vulnerabilities and licenses. 

SCA tools build complete dependency graphs spanning multiple layers, mapping every direct and indirect component in your application. This visibility is critical because the majority of vulnerabilities exist in transitive dependencies that developers never explicitly add and rarely monitor.

What is the difference between SCA and SAST?

Software Composition Analysis examines third-party open source components and libraries in your applications, identifying vulnerabilities in code you didn't write. Static Application Security Testing analyzes your proprietary source code for security flaws in code you did write. 

Enterprise codebases predominantly contain open source components with hundreds of dependencies per application, so you need both. They are complementary capabilities that should be integrated early in your CI/CD pipeline, with SCA executed before SAST to catch vulnerable dependencies first.

What are the most common SCA integration points?

You integrate SCA at multiple points throughout your development lifecycle: manifest and package file analysis that identifies declared dependencies, source code scanning that finds imported libraries, pre-commit hooks that block vulnerable components before they reach version control, pull request automation that provides inline feedback during code review, CI/CD pipeline stages that fail builds containing critical vulnerabilities, and continuous production monitoring that alerts you when new vulnerabilities are disclosed for deployed components.

How does SCA handle false positives?

Advanced SCA implementations use reachability analysis to determine whether vulnerable code paths are actually invoked by your application runtime. This technique maps call trees from your code through dependencies to vulnerable functions, identifying which vulnerabilities in unused code pose minimal actual risk. 

You combine reachability analysis with EPSS scores showing exploit likelihood and business context about application criticality to prioritize remediation efforts.

How often should you run SCA scans?

You run SCA continuously, not on fixed schedules. Scans execute autonomously on every pull request, commit, and build to catch vulnerabilities before code integration. 

Nightly scans on your complete application portfolio find newly disclosed vulnerabilities in components that were safe the day before. Your SCA platform monitors production environments continuously, alerting you within hours when researchers disclose new CVEs affecting deployed applications.