sec-design.md

BUSINESS POSTURE

This project, Facenet, provides a face recognition model and related tools. From a business perspective, integrating face recognition capabilities can address various needs, depending on the specific business goals and priorities.

Potential business priorities and goals for using Facenet could include:

• Enhancing security systems: Implementing face recognition for access control, surveillance, or identity verification.
• Improving user experience: Using face recognition for faster and more convenient login or personalization features in applications.
• Automating processes: Employing face recognition for tasks like attendance tracking, customer identification, or image tagging.
• Data analysis and insights: Analyzing facial features in images or videos for demographic studies, marketing research, or social behavior analysis (with ethical considerations).

Most important business risks associated with these priorities and goals include:

• Privacy violations: Misuse or unauthorized access to facial recognition data, leading to breaches of personal privacy and regulatory non-compliance (e.g., GDPR, CCPA).
• Bias and discrimination: Facial recognition models can exhibit biases based on race, gender, or age, leading to unfair or discriminatory outcomes.
• Accuracy and reliability: The model's accuracy might be insufficient for critical applications, leading to false positives or false negatives with significant consequences.
• Security vulnerabilities: Exploitation of vulnerabilities in the face recognition system could compromise security and privacy.
• Reputational damage: Negative publicity due to privacy breaches, biased outcomes, or system failures can harm the business's reputation.
• Legal and regulatory risks: Failure to comply with evolving data privacy regulations and laws related to biometric data can result in fines and legal action.

SECURITY POSTURE

Based on the nature of an open-source project like Facenet and typical software development practices, we can infer the existing and recommended security posture.

Existing security controls:

• security control: Code repository hosted on GitHub, providing version control and transparency. (Implemented: GitHub)
• security control: Open-source licensing, allowing community review and contribution. (Implemented: LICENSE file in repository)
• security control: Basic input validation might be present in the code to handle image processing and model inputs. (Implemented: Within the Python code, needs review)

Accepted risks:

• accepted risk: Lack of formal security audits or penetration testing.
• accepted risk: Potential vulnerabilities in dependencies (Python libraries, TensorFlow/PyTorch).
• accepted risk: Limited security focus in the development process, as it's primarily a research-oriented project.
• accepted risk: No dedicated security team or incident response plan for the project itself.
• accepted risk: Reliance on community contributions for security fixes.

Recommended security controls (high priority):

• security control: Implement automated security scanning (SAST/DAST) in the CI/CD pipeline to detect vulnerabilities in code and dependencies.
• security control: Conduct regular vulnerability assessments and penetration testing of deployed systems using Facenet.
• security control: Establish a process for secure handling of facial recognition data, including encryption at rest and in transit.
• security control: Implement robust input validation and sanitization to prevent injection attacks and data manipulation.
• security control: Apply the principle of least privilege for access control to data and system resources.
• security control: Implement logging and monitoring to detect and respond to security incidents.
• security control: Develop and enforce secure coding practices throughout the development lifecycle.
• security control: Implement a process for managing and patching dependencies to address known vulnerabilities.

Security requirements:

• Authentication:
  • Requirement: Securely authenticate users or systems accessing the Facenet API or services.
  • Considerations: API keys, OAuth 2.0, mutual TLS, depending on the deployment context.
• Authorization:
  • Requirement: Implement fine-grained authorization to control access to specific functionalities and data based on roles or permissions.
  • Considerations: Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC).
• Input Validation:
  • Requirement: Thoroughly validate all inputs, including images, API requests, and configuration parameters, to prevent injection attacks and data corruption.
  • Considerations: Input type validation, format validation, range checks, sanitization of special characters.
• Cryptography:
  • Requirement: Encrypt sensitive data at rest (e.g., facial embeddings, user data) and in transit (e.g., API communication, data transfer).
  • Considerations: AES-256 encryption for data at rest, TLS 1.3 for data in transit, secure key management practices.
  • Requirement: Use cryptographic hashing for integrity checks of models and code.

DESIGN

C4 CONTEXT

graph LR
    subgraph "Organization System"
        Facenet["Facenet Project"]
    end
    User[/"Application Developer"/]
    ImageDB[/"Image Database"/]
    App[/"Face Recognition Application"/]
    UserManagement[/"User Management System"/]

    User --> Facenet: Integrates Facenet model
    Facenet --> ImageDB: Accesses training data
    Facenet --> App: Provides face embeddings
    App --> Facenet: Sends face images for embedding
    App --> UserManagement: Authenticates users
    User --> App: Uses face recognition features

    linkStyle 0,1,2,3,4,5 stroke:#333,stroke-width:2px;

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Context Diagram Elements:

• Element:

  • Name: Facenet Project
  • Type: Software System
  • Description: The Facenet project itself, encompassing the face recognition model, training scripts, and related utilities. It's the core component providing face embedding capabilities.
  • Responsibilities:
    • Training face recognition models.
    • Providing pre-trained face recognition models.
    • Offering tools and scripts for face detection and embedding generation.
  • Security controls:
    • security control: Code repository access control (GitHub permissions).
    • security control: Vulnerability scanning of dependencies (recommended).
    • security control: Code review process for contributions (recommended).

• Element:

  • Name: Application Developer
  • Type: Person
  • Description: Software developers who integrate the Facenet model and tools into their applications or systems.
  • Responsibilities:
    • Integrating the Facenet model into applications.
    • Developing applications that utilize face recognition features.
    • Ensuring secure and responsible use of face recognition technology.
  • Security controls:
    • security control: Secure development practices.
    • security control: Following organization's security policies.

• Element:

  • Name: Image Database
  • Type: External System
  • Description: A database or storage system containing images used for training the Facenet model. This could be a publicly available dataset or a private dataset.
  • Responsibilities:
    • Storing and providing access to image data for model training.
    • Maintaining data integrity and availability.
  • Security controls:
    • security control: Access control to the database.
    • security control: Data encryption at rest (recommended).
    • security control: Data integrity checks.

• Element:

  • Name: Face Recognition Application
  • Type: Software System
  • Description: An application that utilizes the Facenet model to provide face recognition features to end-users. This could be a web application, mobile app, or desktop software.
  • Responsibilities:
    • Providing face recognition functionality to users.
    • Handling user authentication and authorization.
    • Managing user data and privacy.
  • Security controls:
    • security control: Authentication and authorization mechanisms.
    • security control: Input validation and sanitization.
    • security control: Secure data storage and transmission.
    • security control: Application-level security controls (e.g., session management, CSRF protection).

• Element:

  • Name: User Management System
  • Type: External System
  • Description: A system responsible for managing user accounts, authentication, and authorization within the application environment.
  • Responsibilities:
    • User registration and login.
    • Password management.
    • User role and permission management.
  • Security controls:
    • security control: Secure authentication protocols.
    • security control: Strong password policies.
    • security control: Multi-factor authentication (recommended).
    • security control: Account lockout mechanisms.

C4 CONTAINER

graph LR
    subgraph "Face Recognition Application"
        API[/"API Gateway"/]
        WebApp[/"Web Application"/]
    end
    subgraph "Facenet Project"
        ModelServer[/"Model Server"/]
        EmbeddingService[/"Embedding Service"/]
        ModelStorage[/"Model Storage"/]
    end
    ImageDB[/"Image Database"/]
    UserManagement[/"User Management System"/]
    User[/"User"/]

    User --> WebApp: Uses application
    WebApp --> API: API requests
    API --> EmbeddingService: Face embedding requests
    EmbeddingService --> ModelServer: Model inference
    ModelServer --> ModelStorage: Model loading
    EmbeddingService --> ImageDB: Accesses images (for training/verification)
    WebApp --> UserManagement: Authentication/Authorization

    linkStyle 0,1,2,3,4,5,6 stroke:#333,stroke-width:2px;

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Container Diagram Elements:

• Element:

  • Name: API Gateway
  • Type: Container - Web Server
  • Description: An API Gateway acts as a single entry point for all API requests to the Facenet services. It handles routing, authentication, authorization, and potentially rate limiting and request transformation.
  • Responsibilities:
    • API request routing.
    • Authentication and authorization of API requests.
    • Rate limiting and traffic management.
    • Request and response transformation.
  • Security controls:
    • security control: Authentication and authorization mechanisms (e.g., API keys, OAuth 2.0).
    • security control: Input validation and sanitization.
    • security control: TLS encryption for API communication.
    • security control: Web Application Firewall (WAF) (recommended).
    • security control: Rate limiting and DDoS protection.

• Element:

  • Name: Web Application
  • Type: Container - Web Application
  • Description: The front-end web application that provides user interface for interacting with face recognition features. It communicates with the API Gateway to access Facenet services.
  • Responsibilities:
    • User interface for face recognition features.
    • Handling user interactions and requests.
    • Displaying results and information to users.
  • Security controls:
    • security control: Secure coding practices (OWASP guidelines).
    • security control: Input validation and output encoding.
    • security control: Session management and CSRF protection.
    • security control: Content Security Policy (CSP).

• Element:

  • Name: Embedding Service
  • Type: Container - Application Service
  • Description: A service responsible for receiving face images, preprocessing them, and generating face embeddings using the Facenet model.
  • Responsibilities:
    • Receiving face images from the API Gateway.
    • Preprocessing images for model input.
    • Generating face embeddings using the Model Server.
    • Potentially performing face detection.
  • Security controls:
    • security control: Input validation of images.
    • security control: Secure communication with Model Server (e.g., mutual TLS).
    • security control: Resource limits and rate limiting.

• Element:

  • Name: Model Server
  • Type: Container - Application Service
  • Description: A service that hosts the trained Facenet model and performs inference to generate face embeddings. It loads the model from Model Storage and provides embedding generation capabilities to the Embedding Service.
  • Responsibilities:
    • Loading the Facenet model from Model Storage.
    • Performing model inference to generate face embeddings.
    • Managing model versions and updates.
  • Security controls:
    • security control: Access control to model files in Model Storage.
    • security control: Secure loading and execution of the model.
    • security control: Resource limits and monitoring.

• Element:

  • Name: Model Storage
  • Type: Container - Data Store
  • Description: A storage system (e.g., object storage, file system) that stores the trained Facenet models.
  • Responsibilities:
    • Storing trained Facenet models.
    • Providing access to models for the Model Server.
    • Ensuring model integrity and availability.
  • Security controls:
    • security control: Access control to model files.
    • security control: Data encryption at rest (recommended).
    • security control: Integrity checks for model files (e.g., cryptographic hashing).

DEPLOYMENT

Deployment Architecture Option: Cloud-based API Service (AWS, GCP, Azure)

graph LR
    subgraph "Cloud Provider (e.g., AWS)"
        subgraph "Region"
            subgraph "Availability Zone A"
                APIInstanceA[/"API Gateway Instance"/]
                WebAppInstanceA[/"Web Application Instance"/]
                EmbeddingServiceInstanceA[/"Embedding Service Instance"/]
                ModelServerInstanceA[/"Model Server Instance"/]
            end
            subgraph "Availability Zone B"
                APIInstanceB[/"API Gateway Instance"/]
                WebAppInstanceB[/"Web Application Instance"/]
                EmbeddingServiceInstanceB[/"Embedding Service Instance"/]
                ModelServerInstanceB[/"Model Server Instance"/]
            end
            LoadBalancer[/"Load Balancer"/]
            ModelStorageService[/"Object Storage (e.g., S3)"/]
            DatabaseService[/"Managed Database (e.g., RDS)"/]
        end
        Internet[/"Internet"/]
    end
    User[/"User"/]

    User --> Internet
    Internet --> LoadBalancer
    LoadBalancer --> APIInstanceA
    LoadBalancer --> APIInstanceB
    APIInstanceA --> EmbeddingServiceInstanceA
    APIInstanceB --> EmbeddingServiceInstanceB
    EmbeddingServiceInstanceA --> ModelServerInstanceA
    EmbeddingServiceInstanceB --> ModelServerInstanceB
    ModelServerInstanceA --> ModelStorageService
    ModelServerInstanceB --> ModelStorageService
    WebAppInstanceA --> APIInstanceA
    WebAppInstanceB --> APIInstanceB
    WebAppInstanceA --> DatabaseService
    WebAppInstanceB --> DatabaseService

    style LoadBalancer fill:#ccf;
    style ModelStorageService fill:#ccf;
    style DatabaseService fill:#ccf;

    linkStyle 0,1,2,3,4,5,6,7,8,9,10,11,12,13 stroke:#333,stroke-width:2px;

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Deployment Diagram Elements (Cloud-based API Service):

• Element:

  • Name: Load Balancer
  • Type: Infrastructure - Load Balancer
  • Description: Distributes incoming traffic across multiple API Gateway instances for high availability and scalability.
  • Responsibilities:
    • Traffic distribution.
    • Health checks of backend instances.
    • SSL termination (optional).
  • Security controls:
    • security control: DDoS protection.
    • security control: SSL/TLS encryption.
    • security control: Access control lists (ACLs).

• Element:

  • Name: API Gateway Instance (A & B)
  • Type: Infrastructure - Compute Instance (e.g., EC2, VM)
  • Description: Instances of the API Gateway container running in separate Availability Zones for redundancy.
  • Responsibilities:
    • API request routing, authentication, authorization, etc. (as described in Container Diagram).
  • Security controls:
    • security control: Instance-level firewall.
    • security control: Operating system hardening.
    • security control: Regular security patching.

• Element:

  • Name: Web Application Instance (A & B)
  • Type: Infrastructure - Compute Instance (e.g., EC2, VM)
  • Description: Instances of the Web Application container running in separate Availability Zones for redundancy.
  • Responsibilities:
    • Serving the web application frontend.
  • Security controls:
    • security control: Instance-level firewall.
    • security control: Operating system hardening.
    • security control: Regular security patching.

• Element:

  • Name: Embedding Service Instance (A & B)
  • Type: Infrastructure - Compute Instance (e.g., EC2, VM)
  • Description: Instances of the Embedding Service container running in separate Availability Zones for redundancy.
  • Responsibilities:
    • Generating face embeddings (as described in Container Diagram).
  • Security controls:
    • security control: Instance-level firewall.
    • security control: Operating system hardening.
    • security control: Regular security patching.

• Element:

  • Name: Model Server Instance (A & B)
  • Type: Infrastructure - Compute Instance (e.g., EC2, VM)
  • Description: Instances of the Model Server container running in separate Availability Zones for redundancy.
  • Responsibilities:
    • Model inference (as described in Container Diagram).
  • Security controls:
    • security control: Instance-level firewall.
    • security control: Operating system hardening.
    • security control: Regular security patching.

• Element:

  • Name: Model Storage Service (e.g., S3)
  • Type: Infrastructure - Managed Storage Service
  • Description: Managed object storage service for storing Facenet models.
  • Responsibilities:
    • Secure and scalable storage for models.
    • Data durability and availability.
  • Security controls:
    • security control: Access control policies (IAM).
    • security control: Data encryption at rest and in transit.
    • security control: Versioning and backup.

• Element:

  • Name: Database Service (e.g., RDS)
  • Type: Infrastructure - Managed Database Service
  • Description: Managed database service for storing application data (e.g., user profiles, application settings).
  • Responsibilities:
    • Storing application data.
    • Data persistence and availability.
  • Security controls:
    • security control: Access control policies.
    • security control: Data encryption at rest and in transit.
    • security control: Database backups and recovery.
    • security control: Database auditing.

BUILD

graph LR
    Developer[/"Developer"/] --> CodeRepo[/"Code Repository (GitHub)"/]
    CodeRepo --> CI[/"CI/CD Pipeline (GitHub Actions)"/]
    CI --> BuildArtifacts[/"Build Artifacts (Container Images, Models)"/]
    BuildArtifacts --> ContainerRegistry[/"Container Registry (e.g., Docker Hub, ECR)"/]
    ContainerRegistry --> DeploymentEnv[/"Deployment Environment"/]

    subgraph "CI/CD Pipeline"
        Build[/"Build Stage"/]
        Test[/"Test Stage (Unit, Integration, Security Scans)"/]
        Publish[/"Publish Stage"/]
    end
    CI --> Build
    Build --> Test
    Test --> Publish
    Publish --> BuildArtifacts

    linkStyle 0,1,2,3,4,5,6,7,8,9 stroke:#333,stroke-width:2px;

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Build Process Description:

The build process for Facenet, when deployed as a cloud service, would typically involve a CI/CD pipeline to automate the build, test, and deployment stages.

• Developer: Developers write code, train models, and commit changes to the Code Repository.
• Code Repository (GitHub): The source code and model training scripts are stored in a Git repository hosted on GitHub.
• CI/CD Pipeline (GitHub Actions): GitHub Actions or a similar CI/CD system is used to automate the build process whenever changes are pushed to the repository.
  • Build Stage: This stage compiles code, packages dependencies, and potentially trains or prepares the Facenet model. It also includes steps to build container images (e.g., Docker images) for the services.
  • Test Stage: Automated tests are executed, including unit tests, integration tests, and security scans (SAST, DAST, dependency scanning).
  • Publish Stage: Build artifacts, such as container images and trained models, are published to a Container Registry.
• Build Artifacts (Container Images, Models): These are the packaged outputs of the build process, ready for deployment.
• Container Registry (e.g., Docker Hub, ECR): A registry for storing and managing container images.
• Deployment Environment: The target environment where the application is deployed (e.g., cloud infrastructure).

Security Controls in Build Process:

• security control: Secure coding practices enforced through linters and static analysis tools in the CI pipeline.
• security control: Automated security scanning (SAST/DAST) integrated into the CI pipeline to detect vulnerabilities in code and dependencies.
• security control: Dependency scanning to identify and manage vulnerable dependencies.
• security control: Container image scanning for vulnerabilities before publishing to the registry.
• security control: Code review process for all code changes before merging to the main branch.
• security control: Access control to the CI/CD pipeline and build artifacts.
• security control: Immutable infrastructure principles for deployment (deploying container images as build artifacts).
• security control: Supply chain security measures, such as verifying the integrity of dependencies and build tools.

RISK ASSESSMENT

Critical business processes we are trying to protect (assuming a face recognition access control system):

• Physical access control: Ensuring only authorized individuals can access restricted areas.
• Data security: Protecting sensitive data related to user identities and access logs.
• System availability: Maintaining the availability of the access control system to ensure continuous operation.
• User privacy: Protecting the privacy of individuals whose facial data is processed by the system.

Data we are trying to protect and their sensitivity:

• Facial images: Highly sensitive biometric data. If compromised, can lead to identity theft, privacy violations, and potential misuse.
• Facial embeddings: Derived biometric data, still sensitive as they represent facial features. Compromise can lead to similar risks as facial images.
• User identity data: Names, employee IDs, access privileges linked to facial data. Sensitive personal information that needs protection.
• Access logs: Records of access events, potentially containing timestamps, user IDs, and location data. Sensitive information that can reveal user activity patterns.
• System configuration and credentials: Sensitive data required for system operation and management. Compromise can lead to complete system takeover.

QUESTIONS & ASSUMPTIONS

Questions:

• What is the specific business use case for Facenet? (Access control, user authentication, surveillance, etc.)
• What are the data privacy regulations that need to be complied with? (GDPR, CCPA, etc.)
• What is the expected scale and performance requirements for the system?
• What is the organization's risk appetite and security maturity level?
• Are there any existing security policies or standards that need to be followed?
• What is the budget and timeline for implementing security controls?

Assumptions:

• The project is intended for a business application, not just research.
• Data privacy and security are important considerations.
• The system will be deployed in a cloud environment.
• A CI/CD pipeline will be used for build and deployment automation.
• The organization has some level of security awareness and is willing to invest in security controls.
• The focus is on securing a deployed application using Facenet, rather than securing the Facenet project repository itself (although repository security is also important).