Understanding the Fundamentals of AI Concepts for Nonprofits
Artificial Intelligence (AI) has become a cornerstone of modern technology, driving innovation across various sectors. Nonprofits, too, can harness the power of AI to enhance their operations and amplify their impact. In this blog, we'll explore fundamental AI concepts, common AI workloads, Microsoft's Responsible AI policies, and the tools and services available through Azure AI, all tailored for the nonprofit sector. Understanding AI Workloads AI workloads refer to the different types of tasks that AI systems can perform. Here are some common AI workloads relevant to nonprofits: Machine Learning: This involves training a computer model to make predictions and draw conclusions from data. Nonprofits can use machine learning to predict donor behavior, optimize fundraising strategies, and analyze program outcomes. Computer Vision: This capability allows software to interpret the world visually through cameras, video, and images. Applications include identifying and tracking wildlife for conservation efforts or analyzing images to assess disaster damage. Natural Language Processing (NLP): NLP enables computers to understand and respond to human language. Nonprofits can use NLP for sentiment analysis of social media posts, language translation for multilingual communities, and developing conversational AI like chatbots for donor engagement. Anomaly Detection: This involves automatically detecting errors or unusual activity. It is useful for fraud detection in financial transactions, monitoring network security, and ensuring data integrity. Conversational AI: This refers to the capability of a software agent to engage in conversations with humans. Examples include chatbots and virtual assistants that can answer questions, provide recommendations, and perform tasks, enhancing donor and beneficiary interactions. Responsible AI Practices As AI technology continues to evolve, it is crucial to ensure it is developed and used responsibly. Microsoft's Responsible AI policies emphasize the importance of fairness, reliability, safety, privacy, security, inclusiveness, transparency, and accountability in AI systems. These principles guide the development and deployment of AI solutions to ensure they benefit everyone and do not cause harm. To learn more about Microsoft Responsible AI Practices click here: Empowering responsible AI practices | Microsoft AI Azure AI Services for Nonprofits Microsoft Azure offers a suite of AI services that enable nonprofits to build intelligent applications. Some key services include: Azure Machine Learning: A comprehensive platform for building, training, and deploying machine learning models. It supports a wide range of machine learning frameworks and tools, helping nonprofits analyze data and make informed decisions. To learn more or get started with Azure Machine Learning click here: Azure Machine Learning - ML as a Service | Microsoft Azure Azure AI Bot Service: A service for building conversational AI applications. It provides tools for creating, testing, and deploying chatbots that can interact with users through various channels, improving donor engagement and support services. To learn more or get started with Azure AI Bot Service click here: Azure AI Bot Service | Microsoft Azure Azure Cognitive Services: A collection of APIs that enable developers to add AI capabilities to their applications. These services include vision, speech, language, and decision-making APIs, which can be used for tasks like image recognition, language translation, and sentiment analysis. To learn more about the various Cognitive Service please click here: Azure AI Services – Using AI for Intelligent Apps | Microsoft Azure Conclusion AI has the potential to transform the nonprofit sector by enhancing efficiency, driving innovation, and providing valuable insights. By understanding AI workloads, adhering to responsible AI practices, and leveraging Azure AI services, nonprofits can unlock the full potential of AI to better serve their communities and achieve their missions. Embrace the power of AI to take your nonprofit organization to new heights and make a greater impact. For a deeper dive into the fundamental concepts of AI, please visit the module Fundamental AI Concepts. This resource will provide you with essential insights and a solid foundation to enhance your knowledge in the ever-evolving field of artificial intelligence.
Navigating Azure Bot Networking: Key Considerations for Privatization
Navigating the complexities of cloud solutions can be a daunting task, and Azure Bot Solutions are no exception. Many customers face the challenge of privatizing their bot’s messaging endpoint, only to encounter communication breakdowns with the channel—resulting in 502 errors and unresponsive bots. While the necessity of a public messaging endpoint is outlined in the Bot Framework Security and Privacy Frequently Asked Questions - Bot Service | Microsoft Learn, I aim to share insights and practical considerations from my experience working with clients. Please reach to Microsoft Support for more guidance.Empower Your Nonprofit with AI: A Beginner’s Guide to Teaching AI Skills
That’s where AI Skills for Educators comes in—a free resource designed to help educators (including nonprofit leaders, trainers, and community advocates) introduce AI concepts to their audience. Why Should Nonprofits Teach AI? Nonprofits thrive on impact. By integrating AI education into your programs, you can: ✅ Expand career opportunities for your community members. ✅ Boost digital literacy and future-proof skills. ✅ Enhance nonprofit operations by leveraging AI for efficiency. Whether you're running workforce development programs, youth education initiatives, or volunteer training, AI skills can add immense value. What Is AI Skills for Educators? Microsoft’s AI Skills for Educators is a free training hub that provides lesson plans, resources, and interactive learning materials to help you teach AI—even if you’re not an AI expert. Key Features: 🎓 AI Basics – Understand and teach foundational AI concepts. 📖 Pre-made Lesson Plans – Save time with structured AI courses. 🖥️ Hands-on Exercises – Engage learners with real-world AI scenarios. 🎤 Presentation Materials – Ready-to-use slides and guides for training. How Can Your Nonprofit Use These Resources? Here are some ways your nonprofit can start teaching AI today: 1. AI Workshops for Your Community Use the free lesson plans to host in-person or virtual AI workshops. Whether it's a basic AI introduction for job seekers or advanced concepts for tech learners, these resources provide an easy starting point. 2. AI Training for Volunteers & Staff Empower your internal team with AI knowledge! Whether it's automating administrative tasks or enhancing outreach, an AI-skilled team can work smarter. 3. Partner with Schools & Libraries Collaborate with educators, libraries, and youth programs to introduce AI concepts to students and underrepresented communities. AI education shouldn't be limited to tech companies—your nonprofit can bridge the gap. 4. Digital Equity & Inclusion Programs If your nonprofit focuses on closing the digital divide, these resources can help you teach AI literacy in underserved communities, giving more people access to future job opportunities. Getting Started Is Easy! ✅ Step 1: Visit aka.ms/AISkillsForEducators. ✅ Step 2: Explore the lesson plans and materials. ✅ Step 3: Adapt the content to fit your nonprofit’s mission. ✅ Step 4: Start teaching AI in your community! By integrating AI education into your nonprofit, you're not just teaching technology—you’re shaping futures. 💡 Is your nonprofit ready to empower communities with AI? Visit aka.ms/AISkillsForEducators and start making an impact today!Bot Framework: Build an AI Security Assistant with ease
How to create intelligent Bots with the Bot Framework Intro In an era where cybersecurity threats loom large, the need for vigilant and responsive security measures has never been greater. The Microsoft Bot Framework SDK, with its powerful AI capabilities, offers a new frontier in security management. This blog post will delve into the development of such an AI security assistant, showcasing how to leverage the SDK to interpret security logs, generate KQL queries, and provide real-time security alerts. We’ll explore how to integrate with existing security infrastructure and harness the power of AI to build our own AI Security Assistant. Join us as we explore this exciting intersection of AI and cybersecurity, where intelligent bots stand guard against the ever-evolving landscape of digital threats. Setup Before we start with the Bot Framework SDK, we need to prepare our development environment. This section will guide you through the necessary steps to set up your “canvas” and get started with building your AI-powered writing assistant. Prerequisites: Visual Studio: Ensure you have Visual Studio installed with the .NET desktop development workload. You can download it from the official Microsoft website. Azure Subscription: An active Azure subscription is required to access the Copilot SDK and its related services. If you don’t have one already, you can sign up for a free trial. Bot Framework Emulator: This tool allows you to test your bot locally before deploying it to Azure. Download it from the Bot Framework website. Creating a New Bot Project: Install the Bot Framework SDK: Open Visual Studio and create a new project. Choose the “Echo Bot (Bot Framework v4)” template. This template provides a basic bot structure to get you started quickly. Install the required NuGet Packages: Azure.AI.OpenAI Azure.Core Microsoft.Bot.Builder.Integration.AspNet.Core Configure Your Bot: In the file, you’ll need to configure your bot with the appropriate API keys and endpoints for the Copilot service. You can obtain these credentials from your Azure portal.appsettings.json { "MicrosoftAppType": "xxxxxx", // Leave it empty until publish "MicrosoftAppId": "xxxx", // Leave it empty until publish "MicrosoftAppPassword": "xxxx", // Leave it empty until publish "MicrosoftAppTenantId": "xxxxxx", // Leave it empty until publish "AzureOpenAI": { "ApiKey": "xxxxxxxxxx", "DeploymentName": "gpt-4o", "Endpoint": "https://xxxx.openai.azure.com" }, "AzureSentinel": { // Log Analytics "ClientId": "xxxx", "ClientSecret": "xxxxx", "TenantId": "xxxx", "WorkspaceId": "xxxxx" } } When you open the Echo Bot we need to make changes to our code in order to achieve 3 things: Azure OpenAI Chat Interaction and generic advice KQL Query generation KQL Query execution against a Sentinel Workspace \ Log Analytics The main program is EchoBot.cs (you can rename as needed). using Microsoft.Bot.Builder; using Microsoft.Bot.Schema; using Newtonsoft.Json; using System.Net.Http; using System.Text; using System.Threading; using System.Threading.Tasks; using Microsoft.Extensions.Configuration; using Azure.AI.OpenAI; using Azure; using System.Collections.Generic; using System.Linq; using System; using System.Text.RegularExpressions; namespace SecurityBot.Bots { public class Security : ActivityHandler { private readonly HttpClient _httpClient; private readonly AzureOpenAIClient _azureClient; private readonly string _chatDeployment; private readonly IConfiguration _configuration; private Dictionary<string, int> eventMapping; // Declare eventMapping here public Security(IConfiguration configuration) { _configuration = configuration ?? throw new ArgumentNullException(nameof(configuration)); _httpClient = new HttpClient(); // Load event mappings from JSON file string eventMappingPath = Path.Combine(AppContext.BaseDirectory, "eventMappings.json"); if (File.Exists(eventMappingPath)) { var json = File.ReadAllText(eventMappingPath); eventMapping = JsonConvert.DeserializeObject<Dictionary<string, int>>(json); } // Azure OpenAI Chat API configuration var endpoint = configuration["AzureOpenAI:Endpoint"]; var apiKey = configuration["AzureOpenAI:ApiKey"]; _chatDeployment = configuration["AzureOpenAI:DeploymentName"]; // Your Chat model deployment name // Initialize the Azure OpenAI client _azureClient = new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(apiKey)); } protected override async Task OnMessageActivityAsync(ITurnContext<IMessageActivity> turnContext, CancellationToken cancellationToken) { var userInput = turnContext.Activity.Text.ToLower(); // Detect if the user wants to generate a query if (userInput.Contains("generate")) { // If the user says "generate", extract event and date, then generate the query var kqlQuery = await BuildKQLQueryFromInput(userInput, turnContext, cancellationToken); await turnContext.SendActivityAsync(MessageFactory.Text($"Generated KQL Query: {kqlQuery}"), cancellationToken); } else if (userInput.Contains("run")) { // If the user says "run", extract event and date, then run the query var kqlQuery = await BuildKQLQueryFromInput(userInput, turnContext, cancellationToken); var queryResult = await RunKqlQueryAsync(kqlQuery); await turnContext.SendActivityAsync(MessageFactory.Text($"KQL Query: {kqlQuery}\n\nResult: {queryResult}"), cancellationToken); } else { // For other inputs, handle the conversation with Azure OpenAI await GenerateChatResponseAsync(turnContext, userInput, cancellationToken); } } // Generate responses using the Azure OpenAI Chat API without streaming private async Task GenerateChatResponseAsync(ITurnContext<IMessageActivity> turnContext, string userInput, CancellationToken cancellationToken) { var chatClient = _azureClient.GetChatClient(_chatDeployment); // Set up the chat conversation context var chatMessages = new List<ChatMessage> { new SystemChatMessage("You are a cybersecurity assistant responding only to Security related questions. For irrelevant topics answer with 'Irrelevant'"), new UserChatMessage(userInput) }; // Call the Azure OpenAI API to get the complete chat response var chatResponse = await chatClient.CompleteChatAsync(chatMessages); // Access the completion content properly var assistantMessage = chatResponse.Value.Content.FirstOrDefault()?.Text; if (!string.IsNullOrEmpty(assistantMessage)) { // Send the entire response to the user at once await turnContext.SendActivityAsync(MessageFactory.Text(assistantMessage.ToString().Trim()), cancellationToken); } else { await turnContext.SendActivityAsync(MessageFactory.Text("I'm sorry, I couldn't process your request."), cancellationToken); } } // Build a KQL query from the user's input using Text Analytics private async Task<string> BuildKQLQueryFromInput(string input, ITurnContext<IMessageActivity> turnContext, CancellationToken cancellationToken) { // Start with a base KQL query string kqlQuery = "SecurityEvent | where 1 == 1 "; // Use the eventMapping dictionary to map the user's input to an EventID var matchedEventId = eventMapping.FirstOrDefault(mapping => input.Contains(mapping.Key)).Value; if (matchedEventId != 0) // EventID was found { kqlQuery += $"| where EventID == {matchedEventId} "; } else { // Fallback if no matching EventID is found await turnContext.SendActivityAsync(MessageFactory.Text("Sorry, I couldn't find a matching event ID for your request."), cancellationToken); return null; // Exit early if no valid EventID is found } // Extract the DateRange (e.g., "7 days") and add it to the query var dateRange = ExtractDateRange(input); if (!string.IsNullOrEmpty(dateRange)) { kqlQuery += $"| where TimeGenerated > ago({dateRange}) | project TimeGenerated, Account, Computer, EventID | take 10 "; } return kqlQuery; // Return the constructed KQL query } private string ExtractDateRange(string input) { // Simple extraction logic to detect "7 days", "3 days", etc. var match = Regex.Match(input, @"(\d+)\s+days?"); if (match.Success) { return $"{match.Groups[1].Value}d"; // Return as "7d", "3d", etc. } return null; // Return null if no date range found } // Run KQL query in Azure Sentinel / Log Analytics private async Task<string> RunKqlQueryAsync(string kqlQuery) { var _workspaceId = _configuration["AzureSentinel:WorkspaceId"]; string queryUrl = $"https://api.loganalytics.io/v1/workspaces/{_workspaceId}/query"; var accessToken = await GetAccessTokenAsync(); // Get Azure AD token var requestBody = new { query = kqlQuery }; var jsonContent = new StringContent(JsonConvert.SerializeObject(requestBody), Encoding.UTF8, "application/json"); _httpClient.DefaultRequestHeaders.Clear(); _httpClient.DefaultRequestHeaders.Add("Authorization", $"Bearer {accessToken}"); var response = await _httpClient.PostAsync(queryUrl, jsonContent); var responseBody = await response.Content.ReadAsStringAsync(); return responseBody; // Return the query result } // Get Azure AD token for querying Log Analytics private async Task<string> GetAccessTokenAsync() { var _tenantId = _configuration["AzureSentinel:TenantId"]; var _clientId = _configuration["AzureSentinel:ClientId"]; var _clientSecret = _configuration["AzureSentinel:ClientSecret"]; var url = $"https://login.microsoftonline.com/{_tenantId}/oauth2/v2.0/token"; var body = new Dictionary<string, string> { { "grant_type", "client_credentials" }, { "client_id", _clientId }, { "client_secret", _clientSecret }, { "scope", "https://api.loganalytics.io/.default" } }; var content = new FormUrlEncodedContent(body); var response = await _httpClient.PostAsync(url, content); var responseBody = await response.Content.ReadAsStringAsync(); dynamic result = JsonConvert.DeserializeObject(responseBody); return result.access_token; } } } Event ID Mapping Let’s map most important Event ids to utterances. The Solution can be enhanced with Text Analytics and NLU, but for this workshop we are creating the dictionary. { "failed sign-in": 4625, "successful sign-in": 4624, "account lockout": 4740, "password change": 4723, "account creation": 4720, "logon type": 4624, "registry value was modified": 4657, "user account was changed": 4738, "user account was enabled": 4722, "user account was disabled": 4725, "user account was deleted": 4726, "user account was undeleted": 4743, "user account was locked out": 4767, "user account was unlocked": 4768, "user account was created": 4720, "attempt was made to duplicate a handle to an object": 4690, "indirect access to an object was requested": 4691, "backup of data protection master key was attempted": 4692, "recovery of data protection master key was attempted": 4693, "protection of auditable protected data was attempted": 4694, "unprotection of auditable protected data was attempted": 4695, "a primary token was assigned to process": 4696, "a service was installed in the system": 4697, "a scheduled task was created": 4698, "a scheduled task was deleted": 4699, "a scheduled task was enabled": 4700, "a scheduled task was disabled": 4701, "a scheduled task was updated": 4702, "a token right was adjusted": 4703, "a user right was assigned": 4704, "a user right was removed": 4705, "a new trust was created to a domain": 4706, "a trust to a domain was removed": 4707, "IPsec Services was started": 4709, "IPsec Services was disabled": 4710 } Make all required updates to Program.cs and Startup.cs for the Namespace and the public class. // Generated with Bot Builder V4 SDK Template for Visual Studio EchoBot v4.22.0 using Microsoft.AspNetCore.Hosting; using Microsoft.Extensions.Hosting; namespace SecurityBot { public class Program { public static void Main(string[] args) { CreateHostBuilder(args).Build().Run(); } public static IHostBuilder CreateHostBuilder(string[] args) => Host.CreateDefaultBuilder(args) .ConfigureWebHostDefaults(webBuilder => { webBuilder.UseStartup<Startup>(); }); } } Testing Run the Application and open the Azure Bot Emulator to test the Bot. All you need is to add the localhost URL to the Emulator and make some Chat interactions for example: What is a SOAR ? Using OpenAI Chat Generate a KQL query for failed sign-in logs on the past 3 days Run a KQL query for failed sign-in logs on the past 3 days We have correct executions and KQL against our Sentinel\Log Analytics workspace. Let’s build this Bot on Azure and use it from our Teams Client as our Trusted Security Assistant ! Build on Azure The logic behind a Bot build on Azure is to create an Azure Web App and then the relevant Azure Bot Service. All the steps are published in Microsoft Documentation. You will find the ARM Templates on the Solution Window in Visual Studio 2022: Use the following commands to create your app registration and set its password. On success, these commands generate JSON output. Use the command to create an Microsoft Entra ID app registration.az ad app create This command generates an app ID that you’ll use in the next step. az ad app create –display-name “<app-registration-display-name>” –sign-in-audience “AzureADMyOrg” Use for a single tenant app.AzureADMyOrg Use the command to generate a new password for your app registration. az ad app credential resetad app credential reset --id "<appId>" Record values you’ll need in later steps: the app ID and password from the command output. Once you have the App Registration ready and configured deploy the Web App on Azure using the deployment Templates. Create the App Service and the Azure Bot resources for your bot. Both steps use an ARM template and the Azure CLI command to create the resource or resources.az deployment group create Create an App Service resource for your bot. The App service can be within a new or existing App Service Plan.For detailed steps, see Use Azure CLI to create an App Service. Create an Azure Bot resource for your bot.For detailed steps, see Use Azure CLI to create or update an Azure Bot. az deployment group create –resource-group <resource-group> –template-file <template-file-path> –parameters “@<parameters-file-path>” Now time to build and Publish the Bot, make sure you have run the Bot resource ARM deployment as we did with the Web App Create the deployment file for the Bot: Switch to your project’s root folder. For C#, the root is the folder that contains the .csproj file. Do a clean rebuild in release mode. If you haven’t done so before, run to add required files to the root of your local source code directory. This command generates a file in your bot project folder.az bot prepare-deploy.deployment Within your project’s root folder, create a zip file that contains all files and sub-folders. I suggest after this to run either: Run the az webapp deploy command from the command line to perform deployment using the Kudu zip push deployment for your app service (web app). Or select the Publish option from the Solution Explorer and Publish using the created Web App. Remember to add the App ID and the relevant details to appsettings.json we saw earlier. In case you need to re test with the Emulator, remove the App Type, the App ID , Password and Tenant ID settings before running the App locally! Upon success make sure the Bot Messaging Endpoint has the Web App URL we created, followed by the /api/messages suffix. In case it is missing add it: Now we must add the correct API Permissions to the App registration in Entra ID. Select the App Registration, go to API Permissions, add permission and select API My Organization uses. Find the Log analytics and add the Application Permissions for Read: This way we are able to run\execute KQL against our Sentinel – Log Analytics Workspace. Bot Channels – Teams Now that our Bot is active and we can Test in “Test in Web Chat”, we can create the Teams Integration. It is really a simple step, where we select the Teams option from the Channels and verify the configuration. Once we enable that, we can get the HTTPS code from the Get Embed option in the Channels Menu, or open he URL Directly when we select the Teams Channel: Before we start using the Bot we must make a significant configuration in Teams Admin Center. Otherwise the Bot will probably show-up but unable to get messages from the Chat. Bot in Teams Finally we are able to use our Security Assistant Bot in Teams, Web or Desktop App. The Bot will provide generic advice from Azure OpenAI Chat model, will generate KQL queries for a number of Events and execute those Queries in Log Analytics and we will see the results in our UI. We can always change the appearance of the results, in this workshop we have minimal presentation for better visibility. The next phase of this Deployment can utilize Language Service where all Event IDs are dynamically recognized through a Text Analytics service. Conclusion In conclusion, this workshop demonstrated the seamless integration of Azure’s powerful AI services and Log Analytics to build a smart, security-focused chatbot. By leveraging tools like Azure OpenAI, Log Analytics, and the Bot Framework, we’ve empowered bots to provide dynamic insights and interact meaningfully with data. Whether it’s querying log events or responding to security inquiries, this solution highlights the potential of AI-driven assistants to elevate security operations. Keep exploring and building with Azure, and unlock new possibilities in automation and intelligence! Architecture:Deploying GPT-4o AI Chat app on Azure via Azure AI Services – a step-by-step guide
Are you ready to revolutionize your business with cutting-edge AI technology? Dive into our comprehensive step-by-step guide on deploying a GPT-4o AI Chat app using Azure AI Services. Discover how to harness the power of advanced natural language processing to create interactive, human-like chat experiences. From setting up your Azure account to deploying your AI model and customizing your chat app, this guide covers it all. Unleash the potential of AI in your business and stay ahead of the curve with the latest advancements from Microsoft Azure. Don’t miss out on this opportunity to transform your workflows and elevate customer interactions to new heights!Leveraging Azure AI Services for Nonprofits
Nonprofits can harness the power of AI through Microsoft Azure's affordable and accessible AI services. Azure Cognitive Services, Azure Machine Learning, and Azure Bot Services offer a wide range of capabilities that can be utilized by nonprofits to streamline operations enhance decision-making and create innovative solutions. By leveraging these services, nonprofits can achieve greater efficiency, effectiveness, and impact without compromising financial constraints.
