PacBot
PacBot (Policy as Code Bot)
| Category | Compliance & Governance |
|---|---|
| Community Stars | 1290 |
| Last Commit | 4 years ago |
| Last page update | 19 days ago |
| Pricing Details | Free and open-source. |
| Target Audience | Cloud security professionals, DevOps teams, compliance officers. |
PacBot manages continuous compliance monitoring and security automation in cloud environments, particularly within AWS. The technical architecture of PacBot is built around several key AWS managed services.
At its core, PacBot consists of three major components: the Rule Engine, the Web Application, and the Inventory Collector. The Rule Engine leverages CloudWatch Rules, Lambda functions, AWS Batch, and Elasticsearch to evaluate and enforce compliance policies. The Web Application, powered by AWS ECS, Fargate, Elasticsearch, and RDS, provides a user interface and APIs for managing and viewing compliance data. The Inventory Collector uses CloudWatch Rules, AWS Batch, and Elasticsearch to gather and update the inventory of cloud resources.
Operationally, PacBot relies on a Python and Terraform-based installer script to provision and configure the necessary AWS resources, including IAM roles, S3 buckets, RDS instances, Elasticsearch services, and ECS clusters. This script also handles the deployment of the application from source code, ensuring that all components are correctly set up and integrated.
Key operational considerations include the need for specific software dependencies such as Python 3.4 or higher, Terraform, Docker, and Node.js. The installation process can be time-consuming due to the extensive resource provisioning involved. Additionally, PacBot's scalability and performance may be impacted by the volume of data and the complexity of compliance rules being evaluated, particularly in large-scale AWS deployments.
From a technical standpoint, PacBot utilizes Elasticsearch version 5.5 for data storage and analysis, and it supports MySQL 5.6.x for relational database needs. The use of AWS Batch and Lambda functions ensures that tasks are executed efficiently and on-demand, reducing the overhead of continuous monitoring. However, this architecture also introduces limitations such as potential query performance degradation in Elasticsearch and increased costs associated with data retention and resource usage in multi-account AWS setups.