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Security and Compliance Engineering Platform for Policy-Based Governance of Operational Analytics Applications

Description

This project is about automating security and compliance checks for an Operational Analytics Application before and during deployment. The platform validates application configurations, container images, Kubernetes resources, and deployment configurations against predefined security and compliance policies. Applications that do not meet the required policies can be blocked from deployment, while compliant applications are allowed to proceed.

Aim

To design and implement a centralized security and compliance engineering platform using Kyverno, Trivy, Kubernetes, Docker, and GitLab CI/CD to enforce security policies, detect vulnerabilities, and ensure that Operational Analytics Applications follow organizational security and compliance requirements.

Objectives

01 Enforce standardized security and compliance policies.
02 Scan dependencies and container images for vulnerabilities.
03 Validate Kubernetes configurations against security policies.
04 Block non-compliant workloads from deployment.
05 Standardize security controls across environments.
06 Automate security checks in the delivery process.

Application Workflow

01

Stage 1. User Login & Authentication

Process

The user logs into the Operational Analytics Application using their registered credentials.

Tools
Keycloak Kubernetes
Implementation

The application sends the login request to Keycloak. Keycloak verifies the user's identity and provides access to the appropriate application functions.

02

Stage 2. Operational Data Entry & Collection

Process

The user or connected systems provide operational data required for analysis.

Tools
PostgreSQL Kubernetes
Implementation

The application validates the received operational data and stores it in PostgreSQL. The application services process the data through containers running on Kubernetes.

03

Stage 3. Data Processing & Analysis

Process

The application processes the collected operational data and performs the required analytics calculations.

Tools
Python PostgreSQL Kubernetes
Implementation

Python-based services retrieve the required data from PostgreSQL, process it, perform analytics calculations, and prepare the results.

04

Stage 4. Analytics Dashboard

Process

The user views the processed operational analytics through the application dashboard.

Tools
Python PostgreSQL Kubernetes
Implementation

The application retrieves the processed results and displays operational trends, performance indicators, summaries, and other analytics information.

05

Stage 5. Report Generation

Process

The user selects the required information and generates an operational analytics report.

Tools
SQL PostgreSQL Python
Implementation

SQL queries retrieve the required data from PostgreSQL. Python processes and formats the data and generates the required report.

06

Stage 6. Report Review & Export

Process

The user reviews the generated report and exports or downloads it for further use.

Tools
Python PostgreSQL Kubernetes
Implementation

The application retrieves the finalized report data, generates the required output format, and provides it to the user.

07

Stage 7. Logout

Process

The user completes the required activities and logs out of the application.

Tools
Keycloak Kubernetes
Implementation

The user's application session is terminated through Keycloak, and the user is logged out securely.

Cloud Infrastructure and Tools

Security Policy Enforcement Engine Kyverno

Validates Kubernetes resources against predefined security and compliance policies and can allow, modify, or block resources that do not meet the required policies.

Container Security Scanner Trivy

Scans application dependencies, filesystems, and container images for known security vulnerabilities.

CI/CD Platform GitLab CI/CD

Automates application build, testing, security scanning, and deployment stages and can stop the pipeline when security requirements are not satisfied.

Container Orchestration Platform Kubernetes

Runs and manages the containerized Operational Analytics Application and enforces Kyverno policies on Kubernetes resources.

Container Platform Docker

Packages the Operational Analytics Application and its dependencies into containers for consistent deployment.

Application Programming & Analytics Python

Processes operational data, performs analytics calculations, and supports report generation.

Database PostgreSQL

Stores operational application data, analytics-related information, and application records.

Database Query Language SQL

Retrieves and filters operational data from PostgreSQL for analytics and report generation.

Identity & Access Management Keycloak

Provides authentication and role-based access for users accessing the Operational Analytics Application.

Target Compute Infrastructure Cloud EC2 Instances

Provides the virtual servers used to run the self-hosted Kubernetes cluster and its applications.

Server Operating System Ubuntu Server 24.04 LTS

Provides the Linux operating system for the EC2 servers hosting the Kubernetes cluster.

Cloud Networking Cloud VPC

Provides the private network for the Kubernetes infrastructure and separates public and private application traffic.

Security Controls Cloud Security Groups + Network ACLs

Control network access and protect the Kubernetes servers and application workloads from unauthorized traffic.

Cloud Storage Cloud EBS

Provides persistent block storage for the EC2 servers, Kubernetes workloads, application data, and required security-related data.

Implementation Process

01
Step 1 – Analyze Application and Security Requirements
  • Review the Operational Analytics Application architecture, dependencies, data flows, and resource requirements.
  • Identify security and compliance requirements.
  • Define Kubernetes security policies and deployment rules.
  • Define vulnerability severity thresholds for application and container scanning.
  • Define authentication, networking, storage, CPU, and memory requirements.
02
Step 2 – Create Cloud and Kubernetes Infrastructure
  • Create an Cloud VPC with required public and private subnets.
  • Create Cloud EC2 servers using Ubuntu Server 24.04 LTS.
  • Configure Cloud Security Groups, Network ACLs, and Cloud EBS storage.
  • Install and configure the self-hosted Kubernetes cluster.
  • Create application namespaces and configure networking, resource limits, and health checks.
03
Step 3 – Deploy the Operational Analytics Application
  • Package the application using Docker.
  • Create Kubernetes Deployment and Service configurations.
  • Deploy the application containers and PostgreSQL database.
  • Configure Python analytics services and Keycloak authentication.
  • Verify that the application operates correctly in Kubernetes.
04
Step 4 – Implement Security and Compliance Controls
  • Install and configure Trivy to scan dependencies and Docker images.
  • Install Kyverno and define security and compliance policies.
  • Configure policies for container images, resource limits, namespaces, and secure configurations.
  • Integrate Trivy security scanning with GitLab CI/CD.
  • Configure security gates to block applications with unacceptable vulnerabilities or policy violations.
05
Step 5 – Test and Operate the Platform
  • Test compliant and non-compliant Kubernetes configurations.
  • Test vulnerable dependencies and container images using Trivy.
  • Verify that Kyverno and GitLab CI/CD block security violations.
  • Deploy approved configurations to the production environment.
  • Continuously monitor security findings and update policies and scanning configurations.

Proposed Solution

The proposed solution provides a centralized Security and Compliance Engineering Platform for Operational Analytics Applications using Kyverno, Trivy, GitLab CI/CD, and Kubernetes. Trivy scans dependencies and container images, while Kyverno enforces security and compliance policies. GitLab CI/CD automates security checks and blocks applications that fail defined security requirements. The platform runs on Kubernetes hosted on Cloud EC2 inside an Cloud VPC, with Cloud security controls and Cloud EBS providing network protection and persistent storage. This ensures applications are secure, compliant, and properly governed before and during deployment.

Benefits

Automated Security: Security checks run automatically during delivery.
Policy Governance: Kyverno enforces predefined security policies.
Vulnerability Detection: Trivy detects vulnerabilities in dependencies and images.
Security Gates: GitLab CI/CD blocks non-compliant releases.
Less Manual Work: Security validation is automated.
Consistent Standards: Same policies apply across environments.
Better Security: Issues are detected before production.
Centralized Governance: Security requirements are managed through standard policies.

Challenges

Policy Management: Updating Kyverno policies as requirements change.
False Positives: Scanners may report issues requiring manual review.
Pipeline Performance: Security scans can increase CI/CD execution time.
Vulnerability Updates: Trivy requires regularly updated vulnerability data.
Finding Management: Large applications can generate many security findings.
Policy Compatibility: Strict policies may block valid configurations.
Multi-Team Governance: Teams may have different requirements while following common standards.
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