Code Monkey home page Code Monkey logo

ddc-opencontrol's Introduction

Docker Datacenter Compliance Controls CircleCI codecov

Contained within this repository is compliance information for Docker Datacenter as it pertains to NIST-800-53 Rev 4 security controls and the FedRAMP Moderate baseline. This data adheres to the OpenControl schema for building compliance documentation and can be used as part of your own ATO review process. The documentation generated from this content can be used to ATO Docker Datacenter in both on-premises/private cloud infrastructure and in public cloud providers.

This content is provided for informational purposes only and has not been vetted by any third-party security assessors. You are solely responsible for developing, implementing, and managing your applications and/or subscriptions running on your own platform in compliance with applicable laws, regulations, and contractual obligations. The documentation is provided "as-is" and without any warranty of any kind, whether express, implied or statutory, and Docker, Inc. expressly disclaims all warranties for non-infringement, merchantability or fitness for a particular purpose.

System Security Plan (SSP) Templates for Docker Datacenter that also contain this content as it applies to a specific cloud provider can be obtained by contacting [email protected]. Docker Datacenter SSP template guidance availability is contained in the following table:

Provider Format Baselines Status
Microsoft Azure Government Azure Blueprint (.docx) Moderate Available
AWS GovCloud TBD Moderate Coming soon

Usage

The control guidance for Docker Datacenter is separated in to the following components:

Component Name Folder Version
Commercially Supported (CS) Docker Engine CSEngine/ 1.12.3-cs4
Docker Trusted Registry (DTR) DTR/ 2.1.1
Universal Control Plane (UCP) UCP/ 2.0.1
Universal Control Plane Authentication and Authorization Service UCPAuthNAuthZService/ 2.0.1

Both the UCP and DTR components leverage the UCP Authentication and Authorization Service component for authentication and authorization across an entire Docker Datacenter cluster.

A component.yaml file resides in each component's subdirectory. Updates to the security narratives and content are made to these component.yaml files.

In order to generate the documentation appropriate to your system, refer to the Compliance Masonry usage instructions. The examples/ddc-compliance directory contains an example use of these components.

Developing

Refer to the Contributing Guide for instructions on contributing to this project.

Component Validation

The OpenControl schema is defined by the Kwalify schema validator and YAML parser. Each Docker Datacenter component definition is tested against this schema using the PyKwalify Python port of the Kwalify specification. This repository contains a Dockerfile for running the component tests within a container.

docker build -t docker/ddc-opencontrol .
docker run docker/ddc-opencontrol

Natural Language Processing [Experimental]

Thorough documentation of the relevant security controls for each of the DDC components is a critical aspect of this project. It's imperative that not only is each control satisfied, but that the contents of the actual narratives match that which is defined by NIST 800-53. As such, this project includes experimental support for key phrase text analysis backed by Microsoft Cognitive Services.

The nlp/ directory contains a command-line service written in Go that parses each component control's narratives and submits them to the Text Analytics API for detection of key phrases (e.g. "access control", "authentication", etc). The key phrases are then checked against the key phrases that represent each of the NIST 800-53 definitions to ensure that the content indeed matches. The match process is currently quite basic. A successful match occurs when a component's narrative includes one or more of the key phrases that are also in the list of key phrases representative of the NIST definition itself. You can think of this as a form of automated proofreading.

Ultimately, this functionality is best served as a compliance-masonry plugin developed in a separate repository instead of a standalone tool. Contributions welcome!

The potential use cases for natural language processing in documentation efforts are pretty wide-ranging. As such, our goal with this example is to open the door to new and exciting ways to build security and compliance documentation.

ddc-opencontrol's People

Contributors

anweiss avatar clemenko avatar

Watchers

James Cloos avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.