Threat-Oriented DevSecOps: Formal Architecture for Integrating SAST, DAST, SCA, and MITRE ATT&CK-Based Behavioral Telemetry in CI/CD Pipelines

1. Introduction

The intensified digital transformation over the last decade has led organizations to adopt distributed architectures, microservices, and continuous integration and delivery practices. In modern development environments, CI/CD pipelines automate build, testing, and deployment processes, enabling significantly shorter delivery cycles (KIM et al., 2016). Although this automation increases productivity and operational reliability, it also expands the attack surface when security controls are not incorporated natively into the pipeline.

Widely documented security incidents demonstrate the risks associated with the absence of integrated security in the development lifecycle. The CVE‑2021‑44228 vulnerability, known as Log4Shell, enabled remote code execution in widely distributed Java applications, affecting software supply chains on a global scale (NIST, 2021). Similarly, the SolarWinds supply chain compromise showed that adversaries can insert malicious code directly into the software build and distribution process (CISA, 2021).

Contemporary attacks often follow behavioral patterns systematized in knowledge bases such as MITRE ATT&CK, which describes techniques used by adversaries during different phases of intrusion (MITRE, 2023). Techniques such as T1195 (Supply Chain Compromise), T1190 (Exploit Public‑Facing Application), and T1059 (Command and Scripting Interpreter) are recurring in real-world incidents. However, traditional DevSecOps pipelines rarely correlate vulnerabilities detected during development with behavioral indicators observed at runtime.

In light of this scenario, we formulate the following research questions:

RQ1: How can the residual risk associated with vulnerabilities detected in CI/CD pipelines be formally modeled mathematically?

RQ2: How can the results of static and dynamic analysis tools be integrated with behavioral evidence observed at runtime?

RQ3: What is the quantitative impact of this integration on operational security metrics, such as detection and remediation time?

This work makes three contributions. First, we propose a formal model for residual risk calculation in CI/CD pipelines. Second, we present an adaptive prioritization algorithm that integrates vulnerability severity, operational context, and behavioral evidence. Third, we empirically evaluate the architecture in a real corporate environment, demonstrating measurable reductions in operational risk metrics.

2. Background and Related Work

DevSecOps represents the evolution of the DevOps paradigm with the systematic integration of security controls throughout the entire software lifecycle (MYRBATEN; COLPAERT; PRETSCHNER, 2020). In this model, security tests are continuously executed at different stages of the development pipeline.

Formally, we represent a CI/CD pipeline as an ordered set of stages:

$$ P = {s_1, s_2, …, s_n} $$

in which each stage $s_i$ executes a transformation function $f_i$ over software artifacts $a_j$.

Static application security testing (SAST) tools, such as SonarQube and Semgrep, identify syntactic patterns associated with vulnerabilities. Dynamic analysis tools (DAST), such as OWASP ZAP, assess running applications by simulating external interactions. Software composition analysis tools (SCA), such as Trivy or OWASP Dependency‑Check, identify vulnerabilities in third-party dependencies (OWASP, 2021).

Despite the widespread adoption of these tools, empirical studies indicate significant challenges related to false positives and inadequate vulnerability prioritization (RAHMAN et al., 2019). In many cases, prioritization is based exclusively on the CVSS score, which measures technical severity but does not necessarily reflect the contextual risk of the application (FIRST, 2019).

The risk associated with a set of vulnerabilities can be modeled as:

$$ R = \sum_{v \in V} P(v) \cdot I(v) $$

where $V$ represents the set of identified vulnerabilities, $P(v)$ the probability of exploitation, and $I(v)$ the potential impact.

However, traditional models rarely incorporate operational telemetry, evidence of active exploitation, or deployment context. Our proposal integrates these factors through correlation between security scanners and ATT&CK-based behavioral monitoring.

3. Threat Model and Problem Formalization

We consider an adversary $A$ with the following capabilities:

• exploitation of known vulnerabilities published in CVE databases
• insertion of malicious code into software dependencies (supply chain attack)
• remote command execution after initial compromise

The adversary has external access to the application and may perform automated scanning using widely available tools such as Nmap or Masscan.

To capture contextual risk, we define two additional parameters:

• $E(v)$: observable evidence of exploitation associated with the vulnerability
• $C(v)$: contextual criticality based on operational factors

Thus, we define an adjusted exploitation probability:

$$ P’(v) = \alpha \cdot S(v) + \beta \cdot E(v) + \gamma \cdot C(v) $$

where:

• $S(v)$ represents the technical severity derived from CVSS
• $\alpha + \beta + \gamma = 1$

The residual risk of the system is then defined as:

$$ R’ = \sum_{v \in V} P’(v) \cdot I(v) $$

Our objective is to minimize $R’$ under operational constraints of response time $T$ and available resources $K$.

4. Proposed Methodology

We propose a threat-oriented DevSecOps architecture composed of multiple stages integrated into the CI/CD pipeline.

Stage	Function	Tools
SAST	static code analysis	Semgrep, SonarQube
SCA	dependency analysis	Trivy
IaC	infrastructure-as-code analysis	Checkov
DAST	dynamic testing	OWASP ZAP
Runtime	behavioral detection	Falco, Suricata
Correlation	ATT&CK mapping	analytical engine

Vulnerability prioritization is performed by an adaptive algorithm that integrates severity, context, and behavioral evidence.

Prioritization algorithm:

Input: set of vulnerabilities V
Output: prioritized list L

for each v in V:
    severity = getCVSS(v)
    exploitEvidence = queryTelemetry(v)
    context = computeContext(v)

    P_prime = alphaseverity + betaexploitEvidence + gammacontext
    risk[v] = P_prime  impact(v)

return sortDescending(risk)

Computational complexity is $O(n \log n)$ due to the sorting step.

5. Implementation

The architecture was implemented using technologies widely adopted in cloud-native environments.

Component	Technology
CI/CD orchestration	GitLab CI
Containers	Docker
Orchestration	Kubernetes
SAST	SonarQube, Semgrep
SCA	Trivy
IaC scanning	Checkov
DAST	OWASP ZAP
Observability	Elastic Stack
Behavioral detection	Falco, Suricata

Example CI job for running SAST analysis:

sast:
  image: returntocorp/semgrep
  script:
    - semgrep --config=auto --json > report.json
  artifacts:
    paths:
      - report.json

Example Falco rule for detecting shell execution in a container:

- rule: Shell in Container
  desc: Detects shell execution in a container
  condition: container.id != host and proc.name = bash
  output: "Shell detected (user=%user.name container=%container.id)"

The experimental environment was run on a Kubernetes cluster with 12 nodes, totaling 96 vCPUs and 384 GB of memory.

6. Experimental Evaluation

The evaluation was conducted over six months in a corporate environment containing multiple microservices.

Parameter	Value
Pipelines analyzed	120
Microservices	48
Lines of code	3.2 million
Period	6 months

Comparison between the traditional approach and the proposed architecture:

Metric	Baseline	Proposed
MTTD	72 h	26 h
MTTR	9.8 days	5.8 days
Critical vulnerabilities in production	21	9
False positive rate	22%	13.6%

We observed a 64% reduction in mean time to detection and a 41% reduction in mean time to remediation.

7. Case Study

We conducted a case study in a financial-sector organization with approximately two million active users.

During the build process, the Trivy tool identified the presence of vulnerability CVE‑2021‑44228 in a Log4j dependency.

The sequence of events was as follows:

1. Trivy identified the vulnerable dependency in the SCA stage.
2. Suricata detected suspicious traffic associated with an LDAP exploitation attempt.
3. The correlation engine associated the event with ATT&CK technique T1190.
4. The prioritization algorithm automatically raised the vulnerability criticality.

The fix was applied approximately six hours after the initial detection, preventing successful exploitation.

8. Discussion

The results indicate that correlating vulnerability scanners with behavioral telemetry significantly increases the accuracy of risk prioritization. Integration with ATT&CK provides an operational taxonomy that facilitates the interpretation of security events.

However, the approach has limitations. Its effectiveness depends on the quality of the collected telemetry and the monitoring coverage of the environment. In addition, the continuous execution of multiple scanners may introduce additional computational costs in large-scale pipelines.

Another limitation concerns the dependence on adequate instrumentation in execution environments. Observability systems based on eBPF and runtime sensors are essential for reliably capturing behavioral evidence.

9. Conclusion

We presented a threat-oriented DevSecOps architecture that integrates static analysis, dynamic analysis, dependency security, and behavioral monitoring based on MITRE ATT&CK. We formally modeled residual risk calculation in CI/CD pipelines and introduced an adaptive prioritization algorithm that incorporates technical severity, operational context, and evidence of exploitation.

The empirical evaluation demonstrated significant reductions in operational security metrics, including detection time, remediation time, and the incidence of critical vulnerabilities in production. These results indicate that correlating runtime telemetry with vulnerability analysis provides a more effective approach to risk management in DevSecOps environments.

The presented architecture is compatible with contemporary software supply chain security practices, including SBOM generation and build integrity frameworks such as SLSA, enabling greater traceability and reliability in continuous delivery processes.

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