Skip to content

Latest commit

 

History

History
345 lines (321 loc) · 21.8 KB

sec-design.md

File metadata and controls

345 lines (321 loc) · 21.8 KB

BUSINESS POSTURE

  • Business Priorities and Goals:
    • Apache Spark is designed to provide a unified analytics engine for large-scale data processing.
    • Key business priorities include:
      • Performance: Process large datasets quickly and efficiently.
      • Scalability: Handle increasing data volumes and user demands.
      • Reliability: Ensure stable and fault-tolerant data processing.
      • Ease of Use: Provide user-friendly APIs for various data processing tasks.
      • Versatility: Support diverse workloads including batch processing, stream processing, machine learning, and graph processing.
      • Open Source Community: Foster a vibrant and active community for continuous improvement and innovation.
  • Business Risks:
    • Data breaches and unauthorized access to sensitive data processed by Spark.
    • Service disruptions due to vulnerabilities or misconfigurations in Spark deployments.
    • Performance degradation impacting business operations relying on timely data processing.
    • Complexity in managing and securing large-scale Spark deployments.
    • Supply chain risks associated with dependencies and third-party libraries used by Spark.
    • Compliance risks related to data privacy regulations (e.g., GDPR, HIPAA) when processing regulated data.

SECURITY POSTURE

  • Existing Security Controls:
    • security control: Access Control Lists (ACLs) for data access authorization within Spark clusters. Implemented in Spark SQL and DataFrame APIs.
    • security control: Authentication mechanisms like Kerberos and Pluggable Authentication Modules (PAM) for cluster access. Configured during Spark cluster setup.
    • security control: Encryption in transit using TLS/SSL for communication between Spark components and external clients. Configured in Spark configuration settings.
    • security control: Encryption at rest for data stored in persistent storage used by Spark (e.g., HDFS, cloud storage). Implemented at the storage layer, outside of Spark itself.
    • security control: Security auditing and logging of Spark operations and user activities. Configured in Spark and integrated with external logging systems.
    • security control: Role-Based Access Control (RBAC) for managing permissions within Spark clusters. Implemented in Spark SQL and DataFrame APIs.
    • security control: Secure software development lifecycle practices within the Apache Spark project, including code reviews and vulnerability scanning. Described in Apache Software Foundation security guidelines and project development processes.
  • Accepted Risks:
    • accepted risk: Potential vulnerabilities in third-party libraries used by Spark, which require ongoing monitoring and patching.
    • accepted risk: Misconfigurations of Spark clusters by users, leading to security weaknesses.
    • accepted risk: Performance overhead associated with enabling strong security features like encryption.
  • Recommended Security Controls:
    • security control: Implement automated security vulnerability scanning as part of the Spark build and release process.
    • security control: Provide more comprehensive and user-friendly documentation and tools for secure Spark cluster configuration and management.
    • security control: Enhance input validation and sanitization within Spark core components to prevent injection attacks.
    • security control: Explore and implement data masking and anonymization techniques within Spark to protect sensitive data during processing.
    • security control: Integrate with centralized security information and event management (SIEM) systems for enhanced security monitoring and incident response.
  • Security Requirements:
    • Authentication:
      • Requirement: Securely authenticate users and services accessing Spark clusters and data.
      • Requirement: Support multiple authentication mechanisms to integrate with existing enterprise identity providers.
    • Authorization:
      • Requirement: Implement fine-grained authorization controls to manage access to data and Spark resources based on user roles and permissions.
      • Requirement: Enforce least privilege principle for all users and services interacting with Spark.
    • Input Validation:
      • Requirement: Validate and sanitize all external inputs to Spark applications to prevent injection attacks (e.g., SQL injection, command injection).
      • Requirement: Implement robust error handling for invalid inputs to avoid exposing sensitive information or causing unexpected behavior.
    • Cryptography:
      • Requirement: Use strong encryption algorithms and protocols for data in transit and at rest to protect confidentiality and integrity.
      • Requirement: Securely manage cryptographic keys used for encryption and authentication.

DESIGN

C4 CONTEXT

flowchart LR
    subgraph "Organization"
        A["Data Scientist"]
        B["Data Engineer"]
        C["Business Analyst"]
    end
    D["Apache Spark Project"]
    E["Data Sources"]
    F["Data Storage"]
    G["Orchestration Tools"]
    H["Monitoring Systems"]

    A --> D
    B --> D
    C --> D
    D --> E
    D --> F
    D --> G
    D --> H
Loading
  • Context Diagram Elements:
      • Name: Data Scientist
      • Type: User
      • Description: Professionals who use Spark to analyze data, build machine learning models, and derive insights.
      • Responsibilities: Develop and execute data analysis and machine learning workloads using Spark APIs.
      • Security controls: Authentication to access Spark clusters, authorization to access specific datasets and resources.
      • Name: Data Engineer
      • Type: User
      • Description: Professionals responsible for building and maintaining data pipelines and infrastructure, including Spark clusters.
      • Responsibilities: Deploy, configure, and manage Spark clusters; develop data ingestion and processing pipelines.
      • Security controls: Authentication and authorization to manage Spark infrastructure, access to configuration and monitoring tools.
      • Name: Business Analyst
      • Type: User
      • Description: Professionals who use Spark-powered dashboards and reports to monitor business performance and trends.
      • Responsibilities: Consume data insights generated by Spark applications, often through reporting and visualization tools.
      • Security controls: Authentication to access reporting systems that consume Spark data, authorization to view specific reports and dashboards.
      • Name: Apache Spark Project
      • Type: System
      • Description: Unified analytics engine for large-scale data processing, providing APIs for batch, streaming, machine learning, and graph processing.
      • Responsibilities: Process and analyze large datasets, provide data transformation and computation capabilities, manage distributed computing resources.
      • Security controls: Authentication, authorization, input validation, encryption, auditing, secure configuration management.
      • Name: Data Sources
      • Type: External System
      • Description: Various systems that provide data to be processed by Spark, such as databases, message queues, cloud storage, and APIs.
      • Responsibilities: Provide data to Spark for processing, ensure data availability and integrity.
      • Security controls: Authentication and authorization for Spark to access data sources, data encryption in transit.
      • Name: Data Storage
      • Type: External System
      • Description: Systems used to store data processed by Spark, including distributed file systems (e.g., HDFS), cloud object storage (e.g., S3, Azure Blob Storage), and databases.
      • Responsibilities: Persist processed data, provide data storage and retrieval capabilities.
      • Security controls: Access control lists, encryption at rest, data backup and recovery.
      • Name: Orchestration Tools
      • Type: External System
      • Description: Tools used to schedule and manage Spark workflows and data pipelines, such as Apache Airflow, Apache NiFi, and cloud-based workflow services.
      • Responsibilities: Orchestrate Spark jobs, manage dependencies, schedule execution, monitor workflow status.
      • Security controls: Authentication and authorization for Spark to interact with orchestration tools, secure job submission and management.
      • Name: Monitoring Systems
      • Type: External System
      • Description: Systems used to monitor the health, performance, and security of Spark clusters and applications, such as Prometheus, Grafana, and cloud monitoring services.
      • Responsibilities: Collect and visualize metrics, logs, and events from Spark clusters, provide alerts and dashboards for monitoring.
      • Security controls: Secure access to monitoring dashboards and data, secure data transmission from Spark to monitoring systems.

C4 CONTAINER

flowchart LR
    subgraph "Apache Spark Project"
        subgraph "Cluster Manager"
            CM_A["Cluster Manager"]
        end
        subgraph "Driver Program"
            DP_A["Driver Process"]
        end
        subgraph "Executor"
            EX_A["Executor Process"]
            EX_B["Executor Process"]
            EX_C["Executor Process"]
        end
        subgraph "User Interface"
            UI_A["Spark UI"]
        end
    end
    A["Data Scientist"] --> DP_A
    DP_A --> CM_A
    CM_A --> EX_A
    CM_A --> EX_B
    CM_A --> EX_C
    EX_A --> F["Data Storage"]
    EX_B --> F
    EX_C --> F
    DP_A --> UI_A
    UI_A --> A
Loading
  • Container Diagram Elements:
      • Name: Cluster Manager
      • Type: Container
      • Description: Component responsible for managing cluster resources and allocating tasks to executors. Examples include Standalone Cluster Manager, YARN, and Kubernetes.
      • Responsibilities: Resource management, task scheduling, executor lifecycle management, cluster monitoring.
      • Security controls: Authentication and authorization for cluster management operations, secure communication with executors and driver, resource isolation.
      • Name: Driver Process
      • Type: Container
      • Description: Runs the user application code, creates SparkContext, submits jobs to the Cluster Manager, and coordinates executors.
      • Responsibilities: Application execution, job scheduling, task distribution, result aggregation.
      • Security controls: Authentication and authorization for application submission, input validation for application code, secure communication with Cluster Manager and executors.
      • Name: Executor Process
      • Type: Container
      • Description: Worker processes that execute tasks assigned by the Driver, process data partitions, and store results in memory or disk.
      • Responsibilities: Task execution, data processing, data caching, data persistence.
      • Security controls: Resource isolation, secure data handling, access control to local resources, secure communication with Driver and Cluster Manager.
      • Name: Spark UI
      • Type: Container
      • Description: Web-based user interface for monitoring Spark applications, jobs, stages, tasks, and cluster resources.
      • Responsibilities: Application monitoring, job progress visualization, cluster status display, logging and metrics access.
      • Security controls: Authentication and authorization to access Spark UI, secure communication with Driver for monitoring data, protection against cross-site scripting (XSS) and other web vulnerabilities.

DEPLOYMENT

  • Deployment Options:

    • On-Premise Deployment: Deploying Spark on physical servers or virtual machines within an organization's own data center.
    • Cloud Deployment (IaaS): Deploying Spark on cloud infrastructure as a service (IaaS) like AWS EC2, Azure VMs, or Google Compute Engine.
    • Cloud Deployment (PaaS/Managed Services): Using managed Spark services provided by cloud platforms, such as AWS EMR, Azure HDInsight, or Google Dataproc.
    • Containerized Deployment (Kubernetes): Deploying Spark on container orchestration platforms like Kubernetes for scalability and portability.

  • Detailed Deployment (Cloud Deployment on Kubernetes):

flowchart LR
    subgraph "Kubernetes Cluster"
        subgraph "Nodes"
            subgraph "Worker Node 1"
                WN1_P["Spark Executor Pod"]
            end
            subgraph "Worker Node 2"
                WN2_P["Spark Executor Pod"]
            end
            subgraph "Master Node"
                MN_P["Spark Driver Pod"]
                MN_CM["Kubernetes Control Plane"]
            end
        end
        subgraph "Network"
            K8S_NET["Kubernetes Network"]
        end
    end
    CLOUD_LB["Cloud Load Balancer"]
    USER["User"]

    USER --> CLOUD_LB --> MN_P
    MN_P -- K8S_NET --> WN1_P
    MN_P -- K8S_NET --> WN2_P
    WN1_P --> CLOUD_STORAGE["Cloud Storage (e.g., S3)"]
    WN2_P --> CLOUD_STORAGE
    MN_CM -- K8S_NET --> MN_P
    MN_CM -- K8S_NET --> WN1_P
    MN_CM -- K8S_NET --> WN2_P
Loading
  • Deployment Diagram Elements:
      • Name: Kubernetes Cluster
      • Type: Environment
      • Description: Container orchestration platform used to deploy and manage Spark components.
      • Responsibilities: Container management, resource scheduling, service discovery, scaling, health monitoring.
      • Security controls: Kubernetes RBAC, network policies, pod security policies, secrets management, security audits.
      • Name: Worker Node 1, Worker Node 2
      • Type: Infrastructure (Compute)
      • Description: Virtual machines or physical servers in the Kubernetes cluster that run Spark Executor Pods.
      • Responsibilities: Provide compute resources for Spark executors, execute tasks, store intermediate data.
      • Security controls: Operating system hardening, security patching, network segmentation, access control, monitoring.
      • Name: Master Node
      • Type: Infrastructure (Control Plane)
      • Description: Virtual machine or physical server in the Kubernetes cluster that runs the Kubernetes Control Plane and Spark Driver Pod.
      • Responsibilities: Kubernetes control plane functions (API server, scheduler, controller manager, etcd), run Spark Driver process.
      • Security controls: Kubernetes security best practices, secure access to control plane components, hardening, monitoring.
      • Name: Spark Executor Pod
      • Type: Container (Spark Executor)
      • Description: Kubernetes Pod running a Spark Executor process.
      • Responsibilities: Execute Spark tasks, process data partitions, communicate with Driver.
      • Security controls: Container security context, resource limits, network policies, secure communication within the cluster.
      • Name: Spark Driver Pod
      • Type: Container (Spark Driver)
      • Description: Kubernetes Pod running the Spark Driver process.
      • Responsibilities: Run Spark application, coordinate executors, submit tasks to Kubernetes.
      • Security controls: Container security context, resource limits, network policies, secure communication within the cluster.
      • Name: Kubernetes Network
      • Type: Network
      • Description: Network infrastructure within the Kubernetes cluster enabling communication between pods and services.
      • Responsibilities: Network connectivity, service discovery, load balancing within the cluster.
      • Security controls: Network policies to control traffic flow, network segmentation, encryption in transit within the cluster (optional).
      • Name: Cloud Load Balancer
      • Type: Network (Load Balancer)
      • Description: Cloud provider's load balancer to distribute traffic to the Spark Driver Pod (Spark UI, application submission).
      • Responsibilities: Load balancing, external access point to Spark UI and application submission.
      • Security controls: TLS/SSL termination, access control lists, DDoS protection, security monitoring.
      • Name: Cloud Storage (e.g., S3)
      • Type: Data Storage
      • Description: Cloud-based object storage used by Spark for input and output data.
      • Responsibilities: Persistent data storage, data retrieval, data sharing.
      • Security controls: Access control lists (IAM policies), encryption at rest, data versioning, audit logging.

BUILD

flowchart LR
    DEV["Developer"] --> VCS["Version Control System (GitHub)"]
    VCS --> BUILD_SERVER["Build Server (GitHub Actions)"]
    BUILD_SERVER --> COMPILE["Compile & Test"]
    COMPILE --> SAST["SAST & Security Scan"]
    SAST --> DEPENDENCY_SCAN["Dependency Scan"]
    DEPENDENCY_SCAN --> CONTAINER_BUILD["Container Image Build (if applicable)"]
    CONTAINER_BUILD --> ARTIFACT_REPO["Artifact Repository (e.g., Maven Central, Docker Hub)"]
Loading
  • Build Process Elements:
      • Name: Developer
      • Type: Actor
      • Description: Software developers contributing code to the Apache Spark project.
      • Responsibilities: Write code, commit changes to version control, create pull requests.
      • Security controls: Secure development environment, code review process, authentication to version control system.
      • Name: Version Control System (GitHub)
      • Type: System
      • Description: Central repository for source code, managing code changes and collaboration.
      • Responsibilities: Source code management, version control, collaboration platform.
      • Security controls: Access control, branch protection, audit logging, vulnerability scanning of repository.
      • Name: Build Server (GitHub Actions)
      • Type: System
      • Description: Automated build and CI/CD system used to compile, test, and package Spark.
      • Responsibilities: Automated build process, continuous integration, testing, packaging, release management.
      • Security controls: Secure build environment, access control to build system, secrets management, build process isolation.
      • Name: Compile & Test
      • Type: Build Step
      • Description: Compilation of source code and execution of unit and integration tests.
      • Responsibilities: Code compilation, test execution, quality assurance.
      • Security controls: Secure compilation environment, test case coverage, static code analysis (linters).
      • Name: SAST & Security Scan
      • Type: Build Step
      • Description: Static Application Security Testing (SAST) tools to identify potential security vulnerabilities in the code.
      • Responsibilities: Static code analysis, vulnerability detection, security code review guidance.
      • Security controls: SAST tool configuration, vulnerability reporting, integration with build pipeline.
      • Name: Dependency Scan
      • Type: Build Step
      • Description: Scanning dependencies for known vulnerabilities and license compliance issues.
      • Responsibilities: Dependency vulnerability management, license compliance checks, supply chain security.
      • Security controls: Dependency scanning tool configuration, vulnerability database updates, reporting and alerting.
      • Name: Container Image Build (if applicable)
      • Type: Build Step
      • Description: Building container images for Spark components (e.g., Docker images for executors, driver).
      • Responsibilities: Container image creation, image optimization, security hardening of images.
      • Security controls: Base image selection, vulnerability scanning of container images, image signing, secure registry access.
      • Name: Artifact Repository (e.g., Maven Central, Docker Hub)
      • Type: System
      • Description: Repository for storing and distributing build artifacts (e.g., JAR files, container images).
      • Responsibilities: Artifact storage, versioning, distribution, access control.
      • Security controls: Access control to repository, artifact signing, vulnerability scanning of artifacts, secure repository configuration.

RISK ASSESSMENT

  • Critical Business Processes:
    • Data Analytics and Reporting: Providing timely and accurate data insights for business decision-making.
    • Machine Learning Model Training and Inference: Enabling development and deployment of ML models for various business applications.
    • Data Warehousing and ETL: Supporting data integration and transformation for data warehousing and business intelligence.
    • Real-time Stream Processing: Processing and analyzing streaming data for immediate insights and actions.
  • Data Sensitivity:
    • Data processed by Spark can vary widely in sensitivity depending on the use case.
    • Potential data types include:
      • Personally Identifiable Information (PII): Highly sensitive, requires strong protection to comply with privacy regulations.
      • Financial Data: Sensitive, requires protection against fraud and financial loss.
      • Healthcare Data (PHI): Highly sensitive, subject to strict regulations like HIPAA.
      • Business Confidential Data: Sensitive, requires protection to maintain competitive advantage.
      • Public Data: Less sensitive, but integrity and availability are still important.
    • Data sensitivity should be assessed on a per-application basis and appropriate security controls implemented accordingly.

QUESTIONS & ASSUMPTIONS

  • Questions:
    • What specific authentication and authorization mechanisms are currently enforced in Apache Spark deployments across different environments (on-premise, cloud, Kubernetes)?
    • Are there any specific security hardening guidelines or best practices documented and actively promoted for Spark cluster deployments?
    • What is the current process for vulnerability management and patching of Apache Spark and its dependencies?
    • Are there any ongoing initiatives to enhance data masking, anonymization, or differential privacy capabilities within Spark?
    • What are the typical network security configurations recommended for Spark clusters in different deployment scenarios?
  • Assumptions:
    • Assumption: Apache Spark project follows secure software development lifecycle practices, including code reviews and security testing.
    • Assumption: Organizations deploying Apache Spark are responsible for configuring and managing security controls within their own environments.
    • Assumption: Security features in Apache Spark are configurable and can be adapted to meet different security requirements and risk appetites.
    • Assumption: Data processed by Spark may contain sensitive information, and security is a critical consideration for deployments.
    • Assumption: The provided GitHub repository represents the core Apache Spark project and its functionalities.