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building-devsecops-pipeline-with-gitlab-ci

Configure a GitLab CI/CD pipeline that embeds SAST (Semgrep, SpotBugs, Gosec, Bandit, NodeJsScan), DAST, container scanning, dependency scanning, and secret detection via GitLab

What is building-devsecops-pipeline-with-gitlab-ci?

building-devsecops-pipeline-with-gitlab-ci is a Claude Code agent skill that configure a GitLab CI/CD pipeline that embeds SAST (Semgrep, SpotBugs, Gosec, Bandit, NodeJsScan), DAST, container scanning, dependency scanning, and secret detection via GitLab.

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Documentation

Building DevSecOps Pipeline with GitLab CI

Overview

GitLab provides an integrated DevSecOps platform that embeds security testing directly into the CI/CD pipeline. By leveraging GitLab's built-in security scanners---SAST, DAST, container scanning, dependency scanning, secret detection, and license compliance---teams can shift security left, catching vulnerabilities during development rather than post-deployment. GitLab Duo AI assists with false positive detection for SAST vulnerabilities, helping security teams focus on genuine issues.

When to Use

  • When deploying or configuring building devsecops pipeline with gitlab ci capabilities in your environment
  • When establishing security controls aligned to compliance requirements
  • When building or improving security architecture for this domain
  • When conducting security assessments that require this implementation

Prerequisites

  • GitLab Ultimate license (required for full security scanner suite)
  • GitLab Runner configured (shared or self-hosted)
  • .gitlab-ci.yml pipeline configuration familiarity
  • Docker-in-Docker (DinD) or Kaniko for container builds
  • Application deployed to a staging environment for DAST scanning

Core Security Scanning Stages

Static Application Security Testing (SAST)

SAST analyzes source code for vulnerabilities before compilation. GitLab supports 14+ languages using analyzers such as Semgrep, SpotBugs, Gosec, Bandit, and NodeJsScan. The simplest inclusion uses GitLab's managed templates.

Dynamic Application Security Testing (DAST)

DAST tests running applications by simulating attack payloads against HTTP endpoints. It detects XSS, SQLi, CSRF, and other runtime vulnerabilities that static analysis cannot find. DAST requires a deployed, accessible target URL.

Container Scanning

Uses Trivy to scan Docker images for known CVEs in OS packages and application dependencies. Runs after the Docker build stage to gate images before they reach a registry.

Dependency Scanning

Inspects dependency manifests (package.json, requirements.txt, pom.xml, Gemfile.lock) for known vulnerable versions. Operates at the source code level, complementing container scanning.

Secret Detection

Scans commits for accidentally committed credentials, API keys, tokens, and private keys using pattern matching and entropy analysis. Runs on every commit to prevent secrets from reaching the repository.

Implementation

Complete Pipeline Configuration

# .gitlab-ci.yml

stages:
  - build
  - test
  - security
  - deploy-staging
  - dast
  - deploy-production

variables:
  DOCKER_IMAGE: $CI_REGISTRY_IMAGE:$CI_COMMIT_SHORT_SHA
  SECURE_LOG_LEVEL: "info"

# Include GitLab managed security templates
include:
  - template: Security/SAST.gitlab-ci.yml
  - template: Security/Secret-Detection.gitlab-ci.yml
  - template: Security/Dependency-Scanning.gitlab-ci.yml
  - template: Security/Container-Scanning.gitlab-ci.yml
  - template: DAST.gitlab-ci.yml
  - template: Security/License-Scanning.gitlab-ci.yml

build:
  stage: build
  image: docker:24.0
  services:
    - docker:24.0-dind
  variables:
    DOCKER_TLS_CERTDIR: "/certs"
  script:
    - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY
    - docker build -t $DOCKER_IMAGE .
    - docker push $DOCKER_IMAGE
  rules:
    - if: $CI_COMMIT_BRANCH

unit-tests:
  stage: test
  image: $DOCKER_IMAGE
  script:
    - npm ci
    - npm run test:coverage
  coverage: '/Lines\s*:\s*(\d+\.?\d*)%/'
  artifacts:
    reports:
      junit: junit-report.xml
      coverage_report:
        coverage_format: cobertura
        path: coverage/cobertura-coverage.xml

# Override SAST to run in security stage
sast:
  stage: security
  variables:
    SAST_EXCLUDED_PATHS: "spec,test,tests,tmp,node_modules"
    SEARCH_MAX_DEPTH: 10

# Override container scanning
container_scanning:
  stage: security
  variables:
    CS_IMAGE: $DOCKER_IMAGE
    CS_SEVERITY_THRESHOLD: "HIGH"

# Override dependency scanning
dependency_scanning:
  stage: security

# Override secret detection
secret_detection:
  stage: security

# License compliance scanning
license_scanning:
  stage: security

deploy-staging:
  stage: deploy-staging
  image: bitnami/kubectl:latest
  script:
    - kubectl set image deployment/app app=$DOCKER_IMAGE -n staging
    - kubectl rollout status deployment/app -n staging --timeout=300s
  environment:
    name: staging
    url: https://staging.example.com
  rules:
    - if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH

# DAST runs against deployed staging
dast:
  stage: dast
  variables:
    DAST_WEBSITE: https://staging.example.com
    DAST_FULL_SCAN_ENABLED: "true"
    DAST_BROWSER_SCAN: "true"
  needs:
    - deploy-staging
  rules:
    - if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH

deploy-production:
  stage: deploy-production
  image: bitnami/kubectl:latest
  script:
    - kubectl set image deployment/app app=$DOCKER_IMAGE -n production
    - kubectl rollout status deployment/app -n production --timeout=300s
  environment:
    name: production
    url: https://app.example.com
  when: manual
  rules:
    - if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH

Security Approval Policies

Configure scan execution policies to enforce mandatory security scans:

  1. Navigate to Security & Compliance > Policies
  2. Create a "Scan Execution Policy" requiring SAST and secret detection on all branches
  3. Create a "Merge Request Approval Policy" requiring security team approval when critical vulnerabilities are detected

Custom SAST Ruleset Configuration

Create .gitlab/sast-ruleset.toml to customize analyzer behavior:

[semgrep]
  [[semgrep.ruleset]]
    dirs = ["src"]

  [[semgrep.passthrough]]
    type = "url"
    target = "/sgrep-rules/custom-rules.yml"
    value = "https://semgrep.dev/p/owasp-top-ten"

  [[semgrep.passthrough]]
    type = "url"
    target = "/sgrep-rules/java-rules.yml"
    value = "https://semgrep.dev/p/java"

Security Dashboard and Vulnerability Management

Vulnerability Report

GitLab consolidates all scanner findings into a single Vulnerability Report accessible at Security & Compliance > Vulnerability Report. Each vulnerability includes:

  • Severity rating (Critical, High, Medium, Low, Info)
  • Scanner source (SAST, DAST, Container, Dependency, Secret)
  • Location in source code or image layer
  • Remediation guidance and suggested fixes
  • Status tracking (Detected, Confirmed, Dismissed, Resolved)

Merge Request Security Widget

Every merge request displays a security scanning widget showing:

  • New vulnerabilities introduced by the MR
  • Fixed vulnerabilities resolved by the MR
  • Comparison against the target branch baseline

Pipeline Optimization

  • Parallel execution: Security scanners run concurrently in the security stage
  • Caching: Use CI cache for dependency downloads to speed up scanning
  • Incremental scanning: SAST can scan only changed files using SAST_INCREMENTAL: "true"
  • Fail conditions: Set allow_failure: false on critical scanners to enforce quality gates

Monitoring and Metrics

MetricDescriptionTarget
Pipeline security coveragePercentage of projects with all scanners enabled> 95%
Critical vulnerability MTTRTime from detection to resolution for critical findings< 48 hours
False positive ratePercentage of dismissed-as-false-positive findings< 15%
Secret detection block ratePercentage of secret commits blocked by push rules> 99%

References

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

abusing-dpapi-for-credential-access

Extract and decrypt Windows DPAPI-protected secrets (Credential Manager, browser logins/cookies, Wi-Fi credentials, KeePass keys) online or offline using SharpDPAPI, SharpChrome, Mimikatz, or Impacket

abusing-shadow-credentials-for-privesc

Take over Active Directory accounts by writing attacker-controlled public keys to msDS-KeyCredentialLink (Shadow Credentials) with pyWhisker, Whisker, or Certipy, then authenticate via PKINIT to recover the target

achieving-cmmc-level-2-compliance

>-

acquiring-disk-image-with-dd-and-dcfldd

Create forensically sound bit-for-bit disk images with dd or dcfldd on a Linux forensic workstation, preserving evidence integrity through hash verification (MD5/SHA) during acquisition. Use when imaging a suspect drive, USB device, or memory card for investigation, preserving volatile disk evidence during incident response, or producing a verified copy for legal or law-enforcement proceedings before any destructive analysis.

analyzing-active-directory-acl-abuse

Detect dangerous ACL misconfigurations in Active Directory using ldap3

analyzing-android-malware-with-apktool

Perform static analysis of Android APK malware using apktool for resource decompilation, jadx for Java source recovery, and androguard for manifest inspection, dangerous permission-combination detection, and identification of obfuscated code, dynamic code loading, and reflection-based API calls. Use to statically triage a suspicious APK without executing it or to build mobile malware detection rules.

analyzing-api-gateway-access-logs

Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect

analyzing-apt-group-with-mitre-navigator

Query ATT&CK data with attackcti, mitreattack-python, and stix2, then build MITRE ATT&CK Navigator layers and multi-layer heatmap overlays mapping one or more APT groups

analyzing-azure-activity-logs-for-threats

Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query

analyzing-bootkit-and-rootkit-samples

Analyzes bootkit and advanced rootkit malware infecting the Master

analyzing-browser-forensics-with-hindsight

Parse Chromium-based browser databases with Hindsight to extract and correlate browsing history, downloads, cookies, cached content, autofill data, saved passwords, and extensions from Chrome, Edge, Brave, Opera, and Vivaldi into a unified timeline (XLSX, JSON, or SQLite output). Use during incident response, insider-threat investigations, or criminal cases when you need to reconstruct a user

analyzing-campaign-attribution-evidence

Systematically evaluate cyber-campaign evidence to attribute an operation to a threat actor, using the Diamond Model and Analysis of Competing Hypotheses (ACH) to weigh infrastructure overlaps, TTP consistency, malware code similarity, and timing/language artifacts into confidence-weighted attribution assessments. Use when an incident investigation needs a defensible attribution confidence level.

analyzing-certificate-transparency-for-phishing

Monitor Certificate Transparency logs using crt.sh and Certstream to

analyzing-cloud-storage-access-patterns

Detect abnormal access in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics for after-hours bulk downloads, new-IP access, and API-call spikes (e.g. GetObject) via statistical baselines and time-series anomaly detection. Use when investigating suspected cloud data exfiltration or building related detection rules.

analyzing-cobalt-strike-beacon-configuration

Extract and analyze Cobalt Strike beacon configuration from PE files

analyzing-cobaltstrike-malleable-c2-profiles

Parse and analyze Cobalt Strike Malleable C2 profiles with dissect.cobaltstrike (profiles and beacon-payload configs) and pyMalleableC2 (AST parsing) to extract HTTP/DNS transforms, URIs, headers, sleep/jitter, and injection behavior, then generate network detection signatures. Use when reverse-engineering a captured malleable profile or building detections against Cobalt Strike Beacon traffic.

analyzing-command-and-control-communication

Analyzes malware C2 communication over HTTP, HTTPS, DNS, and custom

analyzing-cyber-kill-chain

Analyzes intrusion activity against the Lockheed Martin Cyber Kill Chain

analyzing-disk-image-with-autopsy

Perform comprehensive forensic analysis of raw (dd), E01, or AFF disk images with Autopsy and The Sleuth Kit, recovering deleted files, examining metadata and embedded artifacts, keyword searching, and building investigation timelines with visual reports. Use for structured analysis of a forensic disk image or when stakeholders need visual reports from evidence.

analyzing-dns-logs-for-exfiltration

Analyzes DNS query logs to detect data exfiltration via DNS tunneling,

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