Surveilr Compliance Explorer Pattern – Streamlining Compliance with SQL Scripts

Surveilr Compliance Explorer Pattern – Streamlining Compliance with SQL Scripts

The Surveilr Compliance Explorer Pattern is an innovative solution for organizations managing information controls, which are essential security, compliance, and operational measures aimed at reducing risk in information systems and data handling. Designed with flexibility in mind, this pattern leverages SQL scripts to help users extract, present, and cache compliance data effectively. Here’s how it works and how you can get started.

Key Components of the Surveilr Compliance Explorer Pattern

1. Stateless SQL Script (stateless.sql): This script is designed to create database views that define how control data is extracted from structured files like uniform_resource.csv. These views make it easy to retrieve and display specific compliance data on demand without needing to re-ingest the data each time.

2. Stateful SQL Script (stateful.sql): For users who need to cache data for quicker access, the stateful.sql script creates tables that store data extracted by the views. This approach allows compliance and audit teams to access the latest data without running the same extractions repeatedly.

3. Package SQL Script (package.sql.ts): Acting as the primary entry point, this script loads necessary database objects and Web UI content, providing users with a simplified experience in managing and visualizing data. This central script is crucial for setting up the user interface and accessing the Surveilr database.

How to Use the Compliance Explorer Pattern

Surveilr’s flexibility extends to a variety of platforms, so you can run this pattern on almost any device without needing the Surveilr repository itself. Here’s a step-by-step guide to getting started:

1. Prepare a Working Directory: First, set up a folder for your compliance data:

   mkdir -p /tmp/compliance-explorer
   cd /tmp/compliance-explorer

2. Prepare Control Files: Place control data CSV files (e.g., SCF_2024.2.csv) in an ingest folder inside your directory:

   compliance-explorer
   ├── ingest
   │   ├── SCF_2024.2.csv
   ├── package.sql.ts
   └── stateless.sql

3. Download and Ingest Data with Surveilr: Download the Surveilr binary to your working directory and use it to ingest the control files, creating an SQLite database (resource-surveillance.sqlite.db) that serves as your compliance repository:

   ./surveilr ingest files -r ingest/

   After ingestion, your folder will look like this:

   compliance-explorer
   ├── ingest
   │   ├── SCF_2024.2.csv
   ├── package.sql.ts
   └── stateless.sql
   └── resource-surveillance.sqlite.db            # SQLite database

4. Query and Visualize Data: Once data is ingested, Surveilr is no longer required. Use standard tools like sqlite3, DBeaver, or DataGrip to access your compliance data from the resource-surveillance.sqlite.db database. You can also load the Surveilr Web UI for a richer interface:

   deno run -A ./package.sql.ts | surveilr shell

5. Accessing the Web UI: Start the Surveilr web UI to view compliance data:

   ../../std/surveilrctl.ts dev

   Visit http://localhost:9000/ to browse the UI or access the specific schema for data management services (DMS) at http://localhost:9000/dms/info-schema.sql.

Compliance Explorer’s Advantage

The Surveilr Compliance Explorer Pattern streamlines compliance workflows with reusable SQL scripts for data extraction and caching, enabling organizations to:

• Centralize Compliance Data: Store and organize compliance information in a single, easily accessible SQLite database.
• Simplify Data Management: Run one-time ingestions and visualize data in real time without needing to repeat complex extraction processes.
• Enhance Flexibility: Use SQL queries to generate views or reports on-demand, facilitating quick compliance checks, audits, and updates.

This pattern empowers teams to effectively manage compliance requirements and reduces the complexity of regulatory data handling, offering a seamless, accessible solution across varied operational environments.