A Quantitative and Operational Architecture for Application Security Based on the Integration of SAST, DAST, SCA, and Threat Intelligence in the DevSecOps Lifecycle

1. Introduction

Application Security (AppSec) has emerged as one of the principal risk vectors in modern enterprise environments. Accelerated digital transformation, combined with the adoption of microservice-based architectures, containers, and exposed APIs, has significantly expanded organizations’ attack surfaces. In this context, web applications and distributed services have become the primary point of interaction between legitimate users and potential adversaries.

Empirical reports underscore the centrality of this vector. The Verizon Data Breach Investigations Report shows that vulnerabilities in web applications continue to be among the most common causes of security incidents (VERIZON, 2023). In addition, attacks exploiting known vulnerabilities in Internet-facing applications remain one of the dominant intrusion techniques (MITRE, 2023).

Recent events reinforce the criticality of the software dependency chain. The Log4Shell vulnerability (CVE‑2021‑44228), for example, demonstrated how widely used libraries can introduce systemic risks at scale (NIST, 2023). Exploitation of this flaw enabled remote code execution via JNDI injection and affected millions of systems across different sectors of the economy.

Despite the widespread adoption of automated security analysis tools, many organizations still face difficulties in effectively prioritizing vulnerabilities. Static analysis tools often generate a large volume of findings with a high false-positive rate, while approaches based exclusively on dynamic analysis may miss dormant vulnerabilities in source code.

Against this backdrop, we investigated the following research problem: how to build an AppSec program that effectively reduces actual exploitable risk, rather than merely the nominal number of identified vulnerabilities.

We formulated the following research questions:

RQ1: How can SAST, DAST, and SCA results be correlated to reduce false positives while preserving detection coverage?
RQ2: How can the exploitable risk of vulnerabilities be modeled quantitatively while considering active threat intelligence?
RQ3: What is the operational impact of this integration on reducing mean time to remediation (MTTR)?

The main contributions of this work are:

• A formal risk function for vulnerability prioritization.
• An integrated, risk-driven AppSec architecture in a DevSecOps context.
• An experimental evaluation based on real applications in a production environment.

2. Background and Related Work

Static Application Security Testing (SAST) tools analyze source code without executing the program, using data-flow and control-flow analysis to identify vulnerability patterns (SCANDARIATO et al., 2013). Widely used tools include SonarQube, Semgrep, and Checkmarx.

Dynamic Application Security Testing (DAST) evaluates applications at runtime, simulating offensive interactions to identify exploitable vulnerabilities during execution. Tools such as OWASP ZAP and Burp Suite are widely used in this context (OWASP, 2023).

Software Composition Analysis (SCA) focuses on identifying vulnerabilities in open-source dependencies. These tools map library versions to vulnerability databases, such as the National Vulnerability Database (NVD) (NIST, 2023). Examples include Trivy, Snyk, and OWASP Dependency-Check.

Empirical studies show significant limitations of static analysis tools in real-world environments. Johnson et al. (2013) demonstrate that developers often ignore alerts due to the tools’ low precision. Additional studies indicate that only a small fraction of published vulnerabilities is actually exploited in real attacks (SHARMA; SHARMA, 2020).

Mature frameworks such as OWASP SAMM and the NIST Secure Software Development Framework (SSDF) provide structured guidance for AppSec programs, but do not define quantitative formalizations for vulnerability prioritization (OWASP, 2023; NIST, 2022).

In recent years, combining threat intelligence with vulnerability management has become a recommended practice. Repositories such as Exploit‑DB, CISA Known Exploited Vulnerabilities (KEV), and threat intelligence feeds make it possible to identify vulnerabilities with active exploitation (CISA, 2024).

3. Threat Model and Problem Formalization

We consider an adversary with the following operational capabilities:

• Performing automated scans of Internet-facing services.
• Exploiting known vulnerabilities using publicly available exploits.
• Combining multiple vulnerabilities into attack chains.

In the context of MITRE ATT&CK, such behaviors correspond to techniques such as Exploit Public‑Facing Application (T1190) and Active Scanning (T1595) (MITRE, 2023).

We define the set of identified vulnerabilities as follows:

$V = {v_1, v_2, …, v_n}$

For each vulnerability $v$, we consider the following attributes:

• $C(v)$ — normalized CVSS score in the range $[0,1]$
• $E(v)$ — indicator of the existence of a publicly available exploit
• $X(v)$ — application exposure factor
• $T(v)$ — criticality associated with the MITRE ATT&CK technique

The risk function is defined as:

$$ R(v) = \alpha C(v) + \beta E(v) + \gamma X(v) + \delta T(v) $$

subject to the constraint:

$$ \alpha + \beta + \gamma + \delta = 1 $$

The prioritization problem consists of ordering the set $V$ so as to maximize cumulative risk reduction after remediation:

$$ \max \sum_{v \in V_{remediadas}} R(v) $$

This model makes it possible to incorporate contextual information beyond traditional severity based solely on CVSS.

4. Proposed Methodology

The proposed architecture consists of five operational layers integrated into a DevSecOps pipeline:

Layer	Function
Ingestion	Collecting results from SAST, DAST, and SCA
Normalization	Standardizing vulnerabilities in a central platform
Enrichment	Integrating threat intelligence feeds
Prioritization	Computing quantitative risk
Orchestration	Automating remediation via the CI/CD pipeline

The DefectDojo platform is used as a mechanism for aggregation and de-duplication of findings.

The prioritization algorithm is defined as follows:

Algorithm RiskScore(V)

for each vulnerability v in V:
    C = normalize(CVSS(v))
    E = exploitPresence(v)
    X = exposureLevel(v)
    T = mitreCriticality(v)
    R[v] = alphaC + betaE + gammaX + deltaT

return sortDescending(R)

The algorithm’s computational complexity is $O(n \log n)$ due to the sorting step.

We integrated scanners directly into the CI/CD pipeline. An example of a Semgrep run is shown below:

semgrep --config auto --json --output semgrep.json

Dynamic analysis can be executed with OWASP ZAP in automated mode:

zap-baseline.py -t https://app.test -J zap-report.json

5. Implementation

The architecture was implemented in a Kubernetes cluster version 1.28 consisting of six nodes with 32 vCPUs and 128 GB of memory each.

The toolset used is shown below:

Category	Tool
SAST	Semgrep 1.45, SonarQube 10
DAST	OWASP ZAP 2.14
SCA	Trivy 0.48
Orchestration	GitLab CI
Management	DefectDojo 2.25

An example of a custom Semgrep rule for detecting insecure deserialization in Python is shown below:

rules:
- id: insecure-deserialization
  pattern: pickle.loads(...)
  message: Possible insecure deserialization
  languages: [python]
  severity: ERROR

6. Experimental Evaluation

The experiment was conducted on a set of 42 enterprise applications.

Language	Number of applications
Java	18
Python	14
Node.js	10

Initially, 12,842 vulnerabilities were identified using the combined output of the three scanners.

After de-duplication and cross-tool correlation, the total number decreased to 7,103 unique findings, representing a reduction of 44.7%.

The false-positive rate was estimated based on manual analysis of a sample of 600 vulnerabilities.

Approach	False-positive rate
Isolated SAST	38% ± 4.2
Proposed architecture	20% ± 3.1

A significant reduction in mean remediation time was also observed.

Metric	Baseline	Proposed architecture
MTTR	18.4 days	6.8 days

In addition, vulnerabilities in dependencies with an active public exploit decreased significantly after risk-based prioritization.

Metric	Before	After 60 days
Critical exploitable dependencies	312	87

7. Case Study

We conducted a case study on a publicly exposed financial application.

During the analysis, SAST identified use of the vulnerable Jackson Databind library related to CVE‑2020‑36518. The SCA tool confirmed the presence of the dependency at runtime.

Integration with intelligence feeds indicated the existence of a public proof-of-concept. The risk calculation yielded:

$$ R(v) = 0.87 $$

This value placed the vulnerability among the three most critical in the system, leading to patch application in less than 48 hours.

Without risk-driven prioritization, the vulnerability would have been classified only as “high” among more than 240 findings.

8. Discussion

The experimental results indicate that combining multiple analysis techniques with threat intelligence significantly improves vulnerability prioritization.

The main operational benefit is the reduction of noise generated by automated scanners. By incorporating exploitation context and application exposure, teams can focus their efforts on vulnerabilities with a higher likelihood of exploitation.

However, the approach has limitations. Prioritization quality depends on the availability and timeliness of threat intelligence feeds. In addition, zero-day vulnerabilities that do not appear in public repositories may not receive appropriate prioritization.

Integration with eBPF-based runtime monitoring and behavioral detection tools can complement the approach by providing visibility into active exploitation in production environments (LIGUORI et al., 2023).

9. Conclusion

We presented a quantitative, risk-driven Application Security architecture based on the integration of SAST, DAST, SCA, and threat intelligence within a DevSecOps lifecycle.

The formalization of the risk function made it possible to prioritize vulnerabilities by considering not only technical severity, but also evidence of exploitation and operational context. The experimental evaluation showed a significant reduction in false positives, improvement in mean remediation time, and a decrease in the number of exploitable vulnerable dependencies.

The results indicate that risk-driven AppSec programs can substantially increase the operational efficiency of security teams and reduce actual exposure to attacks.

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