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sec-design.md

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Okay, let's create a design document for the Microsoft Semantic Kernel project.

BUSINESS POSTURE

Business Priorities and Goals:

  • Enable developers to easily integrate AI and Large Language Models (LLMs) into their applications.
  • Provide a flexible and extensible framework for building AI-powered features.
  • Simplify the orchestration of AI components and services.
  • Foster innovation and experimentation with AI technologies.
  • Reduce the complexity of working with various AI models and APIs.
  • Support multiple programming languages (C#, Python, Java).

Most Important Business Risks:

  • Data privacy and security: LLMs can potentially expose sensitive data if not handled carefully.
  • Model bias and fairness: LLMs can exhibit biases that lead to unfair or discriminatory outcomes.
  • Reliability and availability: Applications relying on LLMs need to be resilient to failures and ensure consistent performance.
  • Cost management: Using LLMs can be expensive, so cost optimization is crucial.
  • Dependency on third-party services: Reliance on external AI models and APIs introduces potential risks.
  • Rapidly evolving technology: The AI landscape is changing quickly, requiring continuous adaptation and updates.
  • Lack of explainability: LLM decisions can be difficult to understand, posing challenges for debugging and accountability.

SECURITY POSTURE

Existing Security Controls:

  • security control: Secure coding practices: The project's GitHub repository shows evidence of secure coding practices, including input validation and handling of secrets. (Described in contribution guidelines and code reviews).
  • security control: Dependency management: The project uses package managers (NuGet, pip, Maven) to manage dependencies, reducing the risk of using vulnerable libraries. (Described in project files).
  • security control: Code reviews: The project uses pull requests and code reviews to ensure code quality and security. (Visible in the GitHub repository).
  • security control: Static analysis: The project likely uses static analysis tools to identify potential security vulnerabilities. (Inferred from Microsoft's standard development practices).
  • security control: Authentication and authorization: The project itself doesn't handle authentication/authorization directly, but it's designed to integrate with services that do (e.g., Azure OpenAI, Hugging Face). (Described in documentation and examples).
  • accepted risk: The project relies on the security of third-party LLM services (e.g., OpenAI, Azure OpenAI). The project itself does not control the security of these services.
  • accepted risk: The project is under active development, and new vulnerabilities may be introduced.

Recommended Security Controls:

  • security control: Implement dynamic analysis (DAST) to complement static analysis.
  • security control: Conduct regular penetration testing to identify vulnerabilities that automated tools might miss.
  • security control: Establish a clear vulnerability disclosure program.
  • security control: Implement a robust secrets management solution (e.g., Azure Key Vault, HashiCorp Vault).
  • security control: Provide clear guidance on securely configuring and deploying applications built with the Semantic Kernel.
  • security control: Implement monitoring and logging to detect and respond to security incidents.

Security Requirements:

  • Authentication:
    • The Semantic Kernel should support integration with various authentication mechanisms (e.g., API keys, OAuth 2.0, Azure Active Directory).
    • Authentication credentials should be securely stored and managed.
  • Authorization:
    • The Semantic Kernel should allow developers to define granular access control policies for different AI components and services.
    • Authorization should be enforced consistently across all supported programming languages.
  • Input Validation:
    • All inputs to the Semantic Kernel and underlying LLMs should be validated to prevent prompt injection attacks and other vulnerabilities.
    • Input validation should be context-aware and consider the specific LLM being used.
  • Cryptography:
    • Sensitive data (e.g., API keys, user data) should be encrypted in transit and at rest.
    • Cryptographic keys should be securely managed.
  • Output Encoding:
    • Output from the Semantic Kernel should be properly encoded to prevent cross-site scripting (XSS) and other injection vulnerabilities.

DESIGN

C4 CONTEXT

graph LR
    User["User"]
    SemanticKernel["Semantic Kernel"]
    OpenAIService["OpenAI Service"]
    AzureOpenAIService["Azure OpenAI Service"]
    HuggingFaceService["Hugging Face Service"]
    OtherAIService["Other AI Services"]
    ExternalServices["External Services\n(e.g., Databases, APIs)"]

    User -- "Uses" --> SemanticKernel
    SemanticKernel -- "Uses" --> OpenAIService
    SemanticKernel -- "Uses" --> AzureOpenAIService
    SemanticKernel -- "Uses" --> HuggingFaceService
    SemanticKernel -- "Uses" --> OtherAIService
    SemanticKernel -- "Uses" --> ExternalServices
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Element Descriptions:

  • Element:

    • Name: User
    • Type: Person
    • Description: A person who interacts with an application that uses the Semantic Kernel.
    • Responsibilities: Provides input to the application, receives output from the application.
    • Security controls: Authentication and authorization are typically handled by the application, not the Semantic Kernel itself.

  • Element:

    • Name: Semantic Kernel
    • Type: Software System
    • Description: A lightweight SDK that lets you easily mix conventional programming languages with the latest in Large Language Model AI "prompts" with prompt templating, function chaining, and intelligent planning capabilities.
    • Responsibilities: Orchestrates AI components and services, provides a unified interface for interacting with LLMs, manages prompt templates and function chains.
    • Security controls: Input validation, secure coding practices, dependency management, code reviews, static analysis.

  • Element:

    • Name: OpenAI Service
    • Type: External System
    • Description: The OpenAI API provides access to OpenAI's powerful language models.
    • Responsibilities: Processes natural language requests, generates text, performs various AI tasks.
    • Security controls: Authentication via API keys, rate limiting, usage monitoring (managed by OpenAI).

  • Element:

    • Name: Azure OpenAI Service
    • Type: External System
    • Description: Provides access to OpenAI models through the Azure cloud platform.
    • Responsibilities: Same as OpenAI Service, but with Azure-specific security and management features.
    • Security controls: Azure Active Directory integration, role-based access control, network security, encryption (managed by Azure).

  • Element:

    • Name: Hugging Face Service
    • Type: External System
    • Description: Provides access to a wide range of open-source and commercial LLMs.
    • Responsibilities: Processes natural language requests, generates text, performs various AI tasks.
    • Security controls: Authentication via API keys, usage monitoring (managed by Hugging Face).

  • Element:

    • Name: Other AI Services
    • Type: External System
    • Description: Represents other AI services that the Semantic Kernel might integrate with.
    • Responsibilities: Varies depending on the specific service.
    • Security controls: Varies depending on the specific service.

  • Element:

    • Name: External Services
    • Type: External System
    • Description: Represents external services (databases, APIs, etc.) that the Semantic Kernel might interact with.
    • Responsibilities: Varies depending on the specific service.
    • Security controls: Varies depending on the specific service.

C4 CONTAINER

graph LR
    User["User"]
    App["Application\n(C#, Python, Java)"]
    SemanticKernelLib["Semantic Kernel Library"]
    Connectors["Connectors\n(OpenAI, Azure OpenAI,\nHugging Face, etc.)"]
    Plugins["Plugins\n(Custom Logic)"]
    OpenAIService["OpenAI Service"]
    AzureOpenAIService["Azure OpenAI Service"]
    HuggingFaceService["Hugging Face Service"]

    User -- "Uses" --> App
    App -- "Uses" --> SemanticKernelLib
    SemanticKernelLib -- "Uses" --> Connectors
    SemanticKernelLib -- "Uses" --> Plugins
    Connectors -- "Uses" --> OpenAIService
    Connectors -- "Uses" --> AzureOpenAIService
    Connectors -- "Uses" --> HuggingFaceService
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Element Descriptions:

  • Element:

    • Name: User
    • Type: Person
    • Description: A person who interacts with an application that uses the Semantic Kernel.
    • Responsibilities: Provides input to the application, receives output from the application.
    • Security controls: Authentication and authorization are typically handled by the application.

  • Element:

    • Name: Application (C#, Python, Java)
    • Type: Container
    • Description: The application that uses the Semantic Kernel. This can be a web app, desktop app, mobile app, etc.
    • Responsibilities: Handles user interaction, business logic, and integration with the Semantic Kernel.
    • Security controls: Authentication, authorization, input validation, output encoding, session management, etc. (specific to the application).

  • Element:

    • Name: Semantic Kernel Library
    • Type: Container
    • Description: The core library of the Semantic Kernel.
    • Responsibilities: Provides the main API for interacting with LLMs, manages plugins and connectors.
    • Security controls: Input validation, secure coding practices, dependency management.

  • Element:

    • Name: Connectors (OpenAI, Azure OpenAI, Hugging Face, etc.)
    • Type: Container
    • Description: Components that handle communication with specific LLM services.
    • Responsibilities: Translates requests from the Semantic Kernel into the format required by the LLM service, handles authentication and authorization with the LLM service.
    • Security controls: Secure handling of API keys, secure communication (HTTPS), input validation.

  • Element:

    • Name: Plugins (Custom Logic)
    • Type: Container
    • Description: Custom code that extends the functionality of the Semantic Kernel.
    • Responsibilities: Performs specific tasks, such as data processing, interacting with external services, or implementing custom logic.
    • Security controls: Depends on the specific plugin; developers are responsible for implementing appropriate security controls.

  • Element:

    • Name: OpenAI Service
    • Type: External System
    • Description: The OpenAI API.
    • Responsibilities: Processes natural language requests, generates text.
    • Security controls: Managed by OpenAI.

  • Element:

    • Name: Azure OpenAI Service
    • Type: External System
    • Description: OpenAI models on Azure.
    • Responsibilities: Processes natural language requests, generates text.
    • Security controls: Managed by Azure.

  • Element:

    • Name: Hugging Face Service
    • Type: External System
    • Description: Hugging Face LLMs.
    • Responsibilities: Processes natural language requests, generates text.
    • Security controls: Managed by Hugging Face.

DEPLOYMENT

Possible Deployment Solutions:

  1. Cloud-based deployment (Azure, AWS, GCP): This is the most common and recommended approach for applications using LLMs.
  2. On-premises deployment: Possible, but less common due to the computational requirements of LLMs.
  3. Hybrid deployment: A combination of cloud and on-premises.
  4. Edge deployment: Deploying the application and potentially smaller LLMs on edge devices.

Chosen Deployment Solution (for detailed description): Cloud-based deployment on Azure.

graph LR
    Azure["Azure Cloud"]
    ResourceGroup["Resource Group"]
    AppService["App Service"]
    AppServicePlan["App Service Plan"]
    AzureOpenAIService["Azure OpenAI Service"]
    KeyVault["Azure Key Vault"]
    AppInsights["Application Insights"]

    Azure -- "Contains" --> ResourceGroup
    ResourceGroup -- "Contains" --> AppServicePlan
    ResourceGroup -- "Contains" --> AppService
    ResourceGroup -- "Contains" --> AzureOpenAIService
    ResourceGroup -- "Contains" --> KeyVault
    ResourceGroup -- "Contains" --> AppInsights
    AppService -- "Uses" --> AzureOpenAIService
    AppService -- "Uses" --> KeyVault
    AppService -- "Uses" --> AppInsights
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Element Descriptions:

  • Element:

    • Name: Azure Cloud
    • Type: Infrastructure
    • Description: Microsoft's cloud computing platform.
    • Responsibilities: Provides the infrastructure for hosting the application and related services.
    • Security controls: Azure-wide security features (network security, DDoS protection, etc.).

  • Element:

    • Name: Resource Group
    • Type: Infrastructure
    • Description: A container that holds related resources for an Azure solution.
    • Responsibilities: Organizes and manages Azure resources.
    • Security controls: Role-based access control (RBAC).

  • Element:

    • Name: App Service Plan
    • Type: Infrastructure
    • Description: Defines the compute resources for running the application.
    • Responsibilities: Provides the CPU, memory, and storage for the application.
    • Security controls: Scaling options, network isolation.

  • Element:

    • Name: App Service
    • Type: Infrastructure
    • Description: A fully managed platform for building, deploying, and scaling web apps.
    • Responsibilities: Hosts the application code.
    • Security controls: HTTPS enforcement, authentication/authorization integration, network security.

  • Element:

    • Name: Azure OpenAI Service
    • Type: PaaS
    • Description: Provides access to OpenAI models on Azure.
    • Responsibilities: Processes natural language requests.
    • Security controls: Azure Active Directory integration, role-based access control, network security, encryption.

  • Element:

    • Name: Azure Key Vault
    • Type: PaaS
    • Description: A service for securely storing and managing secrets, keys, and certificates.
    • Responsibilities: Stores API keys and other sensitive information.
    • Security controls: Access policies, auditing, encryption.

  • Element:

    • Name: Application Insights
    • Type: PaaS
    • Description: A monitoring service for web applications.
    • Responsibilities: Collects telemetry data, detects anomalies, and provides insights into application performance and usage.
    • Security controls: Data encryption, access control.

BUILD

graph LR
    Developer["Developer"]
    SourceCode["Source Code\n(GitHub)"]
    CI["CI/CD Pipeline\n(GitHub Actions)"]
    BuildServer["Build Server"]
    StaticAnalysis["Static Analysis\n(SAST)"]
    DependencyCheck["Dependency Check"]
    Artifacts["Build Artifacts\n(NuGet, pip, Maven)"]

    Developer -- "Commits Code" --> SourceCode
    SourceCode -- "Triggers" --> CI
    CI -- "Runs on" --> BuildServer
    BuildServer -- "Performs" --> StaticAnalysis
    BuildServer -- "Performs" --> DependencyCheck
    BuildServer -- "Produces" --> Artifacts
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Build Process Description:

  1. Developer commits code to the GitHub repository.
  2. A commit to the main branch (or a pull request) triggers the CI/CD pipeline (GitHub Actions).
  3. The CI/CD pipeline runs on a build server.
  4. The build server performs the following steps:
    • Checks out the source code.
    • Restores dependencies (using NuGet, pip, Maven).
    • Compiles the code.
    • Runs unit tests.
    • Performs static analysis (SAST) to identify potential security vulnerabilities.
    • Checks for known vulnerabilities in dependencies.
    • Packages the code into build artifacts (NuGet packages, pip packages, Maven packages).
  5. The build artifacts are published to a package repository (e.g., NuGet.org, PyPI, Maven Central).

Security Controls:

  • security control: CI/CD pipeline (GitHub Actions): Automates the build process, ensuring consistency and repeatability.
  • security control: Static analysis (SAST): Identifies potential security vulnerabilities in the code.
  • security control: Dependency check: Scans for known vulnerabilities in dependencies.
  • security control: Code signing: (Potentially) Digitally signs the build artifacts to ensure their integrity and authenticity.
  • security control: Build server security: The build server itself should be secured (e.g., using a secure operating system, limiting access).

RISK ASSESSMENT

Critical Business Processes:

  • Providing a reliable and secure platform for developers to integrate AI into their applications.
  • Maintaining the trust of developers and users by protecting sensitive data.
  • Ensuring the accuracy and fairness of AI-powered features.

Data Sensitivity:

  • User data: Potentially sensitive, depending on the application.
  • API keys: Highly sensitive, should be protected at all costs.
  • Prompt inputs: Can contain sensitive information, depending on the application.
  • LLM outputs: Can contain sensitive information, depending on the prompt and the LLM.
  • Training data (for custom models): Potentially sensitive, depending on the data.

QUESTIONS & ASSUMPTIONS

Questions:

  • What specific types of user data will be processed by applications built with the Semantic Kernel?
  • What are the specific regulatory requirements (e.g., GDPR, CCPA) that apply to these applications?
  • What level of assurance is required for the accuracy and fairness of LLM outputs?
  • What is the expected volume of requests to the LLM services?
  • Are there any specific performance requirements for the Semantic Kernel?
  • What are the specific threat models for the different LLM services being used?

Assumptions:

  • Business Posture: Assumes a moderate risk appetite, balancing innovation with the need for security and compliance.
  • Security Posture: Assumes that Microsoft's standard security practices are followed. Assumes that developers using the Semantic Kernel will implement appropriate security controls in their applications.
  • Design: Assumes that the Semantic Kernel will be primarily used in cloud-based environments. Assumes that developers will use the provided connectors for interacting with LLM services.