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

1. Introduction

Application security (Application Security – AppSec) has established itself as one of the main risk vectors in modern corporate environments. Accelerated digital transformation, combined with the adoption of microservice-based architectures, containers, and exposed APIs, has substantially increased organizations’ attack surface. In this context, web applications and distributed services have become the main point of interaction between legitimate users and potential adversaries.

Empirical reports indicate the centrality of this vector. The Verizon Data Breach Investigations Report shows that vulnerabilities in web applications remain among the most recurring causes of security incidents (VERIZON, 2023). Additionally, attacks that exploit known vulnerabilities in internet-exposed applications continue to be one of the predominant intrusion techniques (MITRE, 2023).

Recent incidents 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 through JNDI injection, affecting millions of systems across different economic sectors.

Despite the widespread adoption of automated security analysis tools, many organizations face difficulties in the effective prioritization of 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 overlook latent vulnerabilities in the source code.

Given this scenario, we investigate the following research problem: how to structure an AppSec program that effectively reduces real exploit risk, and not merely the nominal number of identified vulnerabilities.

We formulate the following research questions:

RQ1: How to correlate SAST, DAST, and SCA results to reduce false positives while maintaining detection coverage?
RQ2: How to quantitatively model the exploit risk of vulnerabilities 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:

• Proposal of a formal risk function for vulnerability prioritization.
• Risk-oriented integrated AppSec architecture in the DevSecOps context.
• Experimental evaluation based on real production applications.

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 running applications, simulating adversarial interactions to identify runtime-exploitable vulnerabilities. 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 correlate library versions with 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 environments. Johnson et al. (2013) demonstrate that developers often ignore alerts due to the low precision of the tools. Additional studies indicate that only a fraction of published vulnerabilities is effectively exploited in real attacks (SHARMA; SHARMA, 2020).

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

In recent years, the integration of threat intelligence with vulnerability management has become a recommended practice. Sources 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:

• Perform automated scanning of internet-exposed services.
• Exploit known vulnerabilities with publicly available exploits.
• Combine 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 detected vulnerabilities as:

$V = {v_1, v_2, …, v_n}$

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

• $C(v)$ — normalized CVSS score in the interval $[0,1]$
• $E(v)$ — indicator of 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 the reduction in aggregate risk 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 is composed of five operational layers integrated into the DevSecOps pipeline:

Layer	Function
Ingestion	Collection of SAST, DAST, and SCA results
Normalization	Standardization of vulnerabilities in a central platform
Enrichment	Integration with threat intelligence feeds
Prioritization	Calculation of quantitative risk
Orchestration	Automation of fixes via CI/CD pipeline

The DefectDojo platform is used as the aggregation and deduplication mechanism for 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 computational complexity of the algorithm is $O(n \log n)$ due to the sorting step.

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

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

Dynamic analysis with OWASP ZAP can be executed 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 composed of six nodes with 32 vCPUs and 128 GB of memory each.

The set of tools used is presented 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 identifying 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 corporate applications.

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

Initially, 12,842 vulnerabilities were identified by combining the three scanners.

After deduplication and correlation across tools, the total number was reduced to 7,103 unique findings, representing a 44.7% reduction.

The false-positive rate was estimated from the 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 active public exploits decreased significantly after risk-based prioritization.

Metric	Before	After 60 days
Exploitable critical 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 Jackson Databind library vulnerable to CVE‑2020‑36518. The SCA tool confirmed the presence of the dependency at runtime.

Integration with intelligence feeds indicated the presence 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, resulting in patch application in less than 48 hours.

Without risk-oriented prioritization, the vulnerability was classified only as “High” among more than 240 findings.

8. Discussion

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

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

However, the approach has limitations. The quality of prioritization depends on the availability and updating of threat intelligence feeds. In addition, zero-day vulnerabilities not present in public databases may not receive adequate prioritization.

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

9. Conclusion

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

The formalization of a risk function made it possible to prioritize vulnerabilities considering not only technical severity, but also evidence of exploitation and operational context. The experimental evaluation demonstrated 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 significantly increase the operational efficiency of security teams and reduce real exposure to attacks.

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