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

1. Introduction

The accelerated digital transformation of the past decade has led organizations to adopt distributed architectures, microservices, and continuous integration and continuous delivery practices. In modern development environments, CI/CD pipelines automate build, test, and deployment processes, significantly reducing delivery cycles (KIM et al., 2016). Although such automation improves productivity and operational reliability, it also expands the attack surface when security controls are not natively embedded in the pipeline.

A large body of well-documented security incidents shows that the absence of integrated security throughout the development lifecycle introduces risk. The vulnerability CVE‑2021‑44228, better known as Log4Shell, enabled remote code execution in widely deployed Java applications and affected software supply chains at a global scale (NIST, 2021). Similarly, the compromise of the SolarWinds supply chain demonstrated that adversaries can directly insert malicious code into software build and distribution processes (CISA, 2021).

Contemporary attacks often follow systematic behavioral patterns, which are cataloged in knowledge bases such as MITRE ATT&CK to describe the techniques adversaries use across different stages of intrusion (MITRE, 2023). Techniques such as T1195 (Supply Chain Compromise), T1190 (Exploit Public‑Facing Application), and T1059 (Command and Scripting Interpreter) repeatedly appear in real-world incidents. However, traditional DevSecOps pipelines rarely correlate vulnerabilities detected during development with behavioral indicators observed at runtime.

Based on this context, we pose the following research questions:

RQ1: How can the residual risk associated with vulnerabilities detected in a CI/CD pipeline 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 security operations metrics such as detection time and remediation time?

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

2. Background and Related Work

DevSecOps represents an evolution of the DevOps paradigm, in which security controls are systematically integrated throughout the software lifecycle (MYRBATEN; COLPAERT; PRETSCHNER, 2020). In this model, security testing is continuously performed at different stages of the development pipeline.

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

$$ P = {s_1, s_2, …, s_n} $$

where each stage $s_i$ applies a transformation function $f_i$ to a software artifact $a_j$.

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

Although these tools are widely adopted, empirical studies show that significant challenges remain with false positives and poor vulnerability prioritization (RAHMAN et al., 2019). In many cases, prioritization is based solely on CVSS scores, which measure technical severity but do not necessarily reflect contextual application risk (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 $V$ denotes the set of identified vulnerabilities, $P(v)$ denotes the probability of exploitation, and $I(v)$ denotes the potential impact.

However, traditional models rarely incorporate runtime telemetry, active exploitation evidence, or deployment context. Our approach integrates these factors by correlating security scanners with ATT&CK-based behavioral monitoring.

3. Threat Model and Problem Formalization

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

• Exploiting known vulnerabilities publicly disclosed in CVE databases
• Injecting malicious code into software dependencies (supply chain attacks)
• Executing remote commands after initial compromise

The adversary can access the application externally and can perform automated scanning using widely available tools such as Nmap or Masscan.

To characterize contextual risk, we define two additional parameters:

• $E(v)$: observable exploitation evidence associated with the vulnerability
• $C(v)$: context criticality based on runtime factors

We therefore define the adjusted exploitation probability as:

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

where:

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

The residual risk of the system is defined as:

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

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

4. Proposed Method

We propose a threat-driven 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	Analysis engine

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

Prioritization algorithm:

Input: conjunto de vulnerabilidades V
Output: lista priorizada 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)

The 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 used to run SAST analysis:

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

Example Falco rule used to detect shell execution in containers:

- rule: Shell in Container
  desc: Detecta execução de shell em contêiner
  condition: container.id != host and proc.name = bash
  output: "Shell detectado (user=%user.name container=%container.id)"

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

6. Experimental Evaluation

The evaluation was conducted over six months in an enterprise environment comprising 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 method
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 an average reduction of 64% in detection time and 41% in remediation time.

7. Case Study

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

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

The event sequence was as follows:

1. Trivy identified the vulnerable dependency during the SCA stage.
2. Suricata detected suspicious traffic related to LDAP exploitation attempts.
3. The correlation mechanism mapped the event to ATT&CK technique T1190.
4. The prioritization algorithm automatically elevated the criticality of the vulnerability.

The vulnerability was remediated approximately six hours after initial detection, preventing successful exploitation.

8. Discussion

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

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

Another limitation is related to appropriate instrumentation in the runtime environment. eBPF-based observability systems and runtime sensors are essential for reliably capturing behavioral evidence.

9. Conclusion

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

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

The proposed architecture is compatible with contemporary software supply chain security practices, including SBOM generation and build integrity frameworks such as SLSA, thereby improving traceability and trust in continuous delivery processes.

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