netanelcyber / netanelcyber/AdPentestAI-Python

Feature: Docker containerization and deployment

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deployment docker enhancement
Dominant language
Python
Stars
5
Forks
2
Avg merge
22h 25m
Merged PRs (30d)
7

Description

Feature: Docker & Container Support

Description

Add comprehensive Docker support for easy deployment and scaling of adpentest across multiple environments.

Proposed Components
1. Dockerfile
FROM python:3.11-slim
RUN apt-get update && apt-get install -y nmap masscan ldap-tools
COPY . /app
WORKDIR /app
RUN pip install -e .
ENTRYPOINT [\"adpentest\"]
2. Docker Compose
  • Multi-container setup (adpentest + logging + storage)
  • Volume mounts for results
  • Network configuration for AD scanning
3. Kubernetes Deployment
  • Helm charts
  • Resource limits/requests
  • Persistent volume claims for reports
  • Service exposure
4. Cloud Deployment
  • AWS Fargate configuration
  • Azure Container Instances
  • GCP Cloud Run setup
Benefits
  • ✅ Consistent environment across teams
  • ✅ Easy scaling for large environments
  • ✅ Isolation from host system
  • ✅ CI/CD integration ready
  • ✅ No dependency conflicts
Implementation Tasks
  • Create Dockerfile with all tools pre-installed
  • Add docker-compose.yml
  • Create Kubernetes manifests
  • Add cloud deployment scripts
  • Update documentation
  • Add GitHub Actions for image builds
  • Publish to Docker Hub/GHCR
Security Considerations
  • Minimal base image (python:3.11-slim)
  • Non-root user execution
  • Read-only filesystem where possible
  • Volume-based result storage
  • Network policies for scanning
Example Usage
# Build image
docker build -t adpentest:1.1.2a .

# Run scan
docker run --rm \
  -v results:/app/results \
  adpentest:1.1.2a \
  --target 10.0.0.1 \
  --mode dry-run \
  --scope-confirmed

# Kubernetes
kubectl apply -f adpentest-deployment.yaml
Related
  • Deployment documentation
  • CI/CD integration

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

No repository files or tests are named. Start by reviewing the existing Python packaging and deployment workflow, then split the proposal into the Dockerfile, Compose setup, Kubernetes/Helm resources, cloud configurations, documentation, and image-build automation. Done requires agreed scope and working, documented deployment paths with security settings and result storage addressed.

Written by the indexing model from the issue text.

Assessment

Tech stack
aws, azure, docker, docker-compose, gcp, github-actions, helm, kubernetes, python
Domain
build-system, ci-cd, cloud, devops, infrastructure
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Active
Clarity
Mostly clear
Newbie friendliness
25/100

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