Smart Auditing: Leveraging Azure AI Agents to Transform Financial Oversight
In today's data-driven business environment, audit teams often spend weeks poring over logs and databases to verify spending and billing information. This time-consuming process is ripe for automation. But is there a way to implement AI solutions without getting lost in complex technical frameworks? While tools like LangChain, Semantic Kernel, and AutoGen offer powerful AI agent capabilities, sometimes you need a straightforward solution that just works. So, what's the answer for teams seeking simplicity without sacrificing effectiveness? This tutorial will show you how to use Azure AI Agent Service to build an AI agent that can directly access your Postgres database to streamline audit workflows. No complex chains or graphs required, just a practical solution to get your audit process automated quickly. The Auditing Challenge: It's the month end, and your audit team is drowning in spreadsheets. As auditors reviewing financial data across multiple SaaS tenants, you're tasked with verifying billing accuracy by tracking usage metrics like API calls, storage consumption, and user sessions in Postgres databases. Each tenant generates thousands of transactions daily, and traditionally, this verification process consumes weeks of your team's valuable time. Typically, teams spend weeks: Manually extracting data from multiple database tables. Cross-referencing usage with invoices. Investigating anomalies through tedious log analysis. Compiling findings into comprehensive reports. With an AI-powered audit agent, you can automate these tasks and transform the process. Your AI assistant can: Pull relevant usage data directly from your database Identify billing anomalies like unexpected usage spikes Generate natural language explanations of findings Create audit reports that highlight key concerns For example, when reviewing a tenant's invoice, your audit agent can query the database for relevant usage patterns, summarize anomalies, and offer explanations: "Tenant_456 experienced a 145% increase in API usage on April 30th, which explains the billing increase. This spike falls outside normal usage patterns and warrants further investigation." Let’s build an AI agent that connects to your Postgres database and transforms your audit process from manual effort to automated intelligence. Prerequisites: Before we start building our audit agent, you'll need: An Azure subscription (Create one for free). The Azure AI Developer RBAC role assigned to your account. Python 3.11.x installed on your development machine. OR You can also use GitHub Codespaces, which will automatically install all dependencies for you. You’ll need to create a GitHub account first if you don’t already have one. Setting Up Your Database: For this tutorial, we'll use Neon Serverless Postgres as our database. It's a fully managed, cloud-native Postgres solution that's free to start, scales automatically, and works excellently for AI agents that need to query data on demand. Creating a Neon Database on Azure: Open the Neon Resource page on the Azure portal Fill out the form with the required fields and deploy your database After creation, navigate to the Neon Serverless Postgres Organization service Click on the Portal URL to access the Neon Console Click "New Project" Choose an Azure region Name your project (e.g., "Audit Agent Database") Click "Create Project" Once your project is successfully created, copy the Neon connection string from the Connection Details widget on the Neon Dashboard. It will look like this: postgresql://[user]:[password]@[neon_hostname]/[dbname]?sslmode=require Note: Keep this connection string saved; we'll need it shortly. Creating an AI Foundry Project on Azure: Next, we'll set up the AI infrastructure to power our audit agent: Create a new hub and project in the Azure AI Foundry portal by following the guide. Deploy a model like GPT-4o to use with your agent. Make note of your Project connection string and Model Deployment name. You can find your connection string in the overview section of your project in the Azure AI Foundry portal, under Project details > Project connection string. Once you have all three values on hand: Neon connection string, Project connection string, and Model Deployment Name, you are ready to set up the Python project to create an Agent. All the code and sample data are available in this GitHub repository. You can clone or download the project. Project Environment Setup: Create a .env file with your credentials: PROJECT_CONNECTION_STRING="<Your AI Foundry connection string> "AZURE_OPENAI_DEPLOYMENT_NAME="gpt4o" NEON_DB_CONNECTION_STRING="<Your Neon connection string>" Create and activate a virtual environment: python -m venv .venv source .venv/bin/activate # on macOS/Linux .venv\Scripts\activate # on Windows Install required Python libraries: pip install -r requirements.txt Example requirements.txt: Pandas python-dotenv sqlalchemy psycopg2-binary azure-ai-projects ==1.0.0b7 azure-identity Load Sample Billing Usage Data: We will use a mock dataset for tenant usage, including computed percent change in API calls and storage usage in GB: tenant_id date api_calls storage_gb tenant_456 2025-04-01 1000 25.0 tenant_456 2025-03-31 950 24.8 tenant_456 2025-03-30 2200 26.0 Run python load_usage_data.py Python script to create and populate the usage_data table in your Neon Serverless Postgres instance: # load_usage_data.py file import os from dotenv import load_dotenv from sqlalchemy import ( create_engine, MetaData, Table, Column, String, Date, Integer, Numeric, ) # Load environment variables from .env load_dotenv() # Load connection string from environment variable NEON_DB_URL = os.getenv("NEON_DB_CONNECTION_STRING") engine = create_engine(NEON_DB_URL) # Define metadata and table schema metadata = MetaData() usage_data = Table( "usage_data", metadata, Column("tenant_id", String, primary_key=True), Column("date", Date, primary_key=True), Column("api_calls", Integer), Column("storage_gb", Numeric), ) # Create table with engine.begin() as conn: metadata.create_all(conn) # Insert mock data conn.execute( usage_data.insert(), [ { "tenant_id": "tenant_456", "date": "2025-03-27", "api_calls": 870, "storage_gb": 23.9, }, { "tenant_id": "tenant_456", "date": "2025-03-28", "api_calls": 880, "storage_gb": 24.0, }, { "tenant_id": "tenant_456", "date": "2025-03-29", "api_calls": 900, "storage_gb": 24.5, }, { "tenant_id": "tenant_456", "date": "2025-03-30", "api_calls": 2200, "storage_gb": 26.0, }, { "tenant_id": "tenant_456", "date": "2025-03-31", "api_calls": 950, "storage_gb": 24.8, }, { "tenant_id": "tenant_456", "date": "2025-04-01", "api_calls": 1000, "storage_gb": 25.0, }, ], ) print("✅ usage_data table created and mock data inserted.") Create a Postgres Tool for the Agent: Next, we configure an AI agent tool to retrieve data from Postgres. The Python script billing_agent_tools.py contains: The function billing_anomaly_summary() that: Pulls usage data from Neon. Computes % change in api_calls. Flags anomalies with a threshold of > 1.5x change. Exports user_functions list for the Azure AI Agent to use. You do not need to run it separately. # billing_agent_tools.py file import os import json import pandas as pd from sqlalchemy import create_engine from dotenv import load_dotenv # Load environment variables load_dotenv() # Set up the database engine NEON_DB_URL = os.getenv("NEON_DB_CONNECTION_STRING") db_engine = create_engine(NEON_DB_URL) # Define the billing anomaly detection function def billing_anomaly_summary( tenant_id: str, start_date: str = "2025-03-27", end_date: str = "2025-04-01", limit: int = 10, ) -> str: """ Fetches recent usage data for a SaaS tenant and detects potential billing anomalies. :param tenant_id: The tenant ID to analyze. :type tenant_id: str :param start_date: Start date for the usage window. :type start_date: str :param end_date: End date for the usage window. :type end_date: str :param limit: Maximum number of records to return. :type limit: int :return: A JSON string with usage records and anomaly flags. :rtype: str """ query = """ SELECT date, api_calls, storage_gb FROM usage_data WHERE tenant_id = %s AND date BETWEEN %s AND %s ORDER BY date DESC LIMIT %s; """ df = pd.read_sql(query, db_engine, params=(tenant_id, start_date, end_date, limit)) if df.empty: return json.dumps( {"message": "No usage data found for this tenant in the specified range."} ) df.sort_values("date", inplace=True) df["pct_change_api"] = df["api_calls"].pct_change() df["anomaly"] = df["pct_change_api"].abs() > 1.5 return df.to_json(orient="records") # Register this in a list to be used by FunctionTool user_functions = [billing_anomaly_summary] Create and Configure the AI Agent: Now we'll set up the AI agent and integrate it with our Neon Postgres tool using the Azure AI Agent Service SDK. The Python script does the following: Creates the agent Instantiates an AI agent using the selected model (gpt-4o, for example), adds tool access, and sets instructions that tell the agent how to behave (e.g., “You are a helpful SaaS assistant…”). Creates a conversation thread A thread is started to hold a conversation between the user and the agent. Posts a user message Sends a question like “Why did my billing spike for tenant_456 this week?” to the agent. Processes the request The agent reads the message, determines that it should use the custom tool to retrieve usage data, and processes the query. Displays the response Prints the response from the agent with a natural language explanation based on the tool’s output. # billing_anomaly_agent.py import os from datetime import datetime from azure.ai.projects import AIProjectClient from azure.identity import DefaultAzureCredential from azure.ai.projects.models import FunctionTool, ToolSet from dotenv import load_dotenv from pprint import pprint from billing_agent_tools import user_functions # Custom tool function module # Load environment variables from .env file load_dotenv() # Create an Azure AI Project Client project_client = AIProjectClient.from_connection_string( credential=DefaultAzureCredential(), conn_str=os.environ["PROJECT_CONNECTION_STRING"], ) # Initialize toolset with our user-defined functions functions = FunctionTool(user_functions) toolset = ToolSet() toolset.add(functions) # Create the agent agent = project_client.agents.create_agent( model=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"], name=f"billing-anomaly-agent-{datetime.now().strftime('%Y%m%d%H%M')}", description="Billing Anomaly Detection Agent", instructions=f""" You are a helpful SaaS financial assistant that retrieves and explains billing anomalies using usage data. The current date is {datetime.now().strftime("%Y-%m-%d")}. """, toolset=toolset, ) print(f"Created agent, ID: {agent.id}") # Create a communication thread thread = project_client.agents.create_thread() print(f"Created thread, ID: {thread.id}") # Post a message to the agent thread message = project_client.agents.create_message( thread_id=thread.id, role="user", content="Why did my billing spike for tenant_456 this week?", ) print(f"Created message, ID: {message.id}") # Run the agent and process the query run = project_client.agents.create_and_process_run( thread_id=thread.id, agent_id=agent.id ) print(f"Run finished with status: {run.status}") if run.status == "failed": print(f"Run failed: {run.last_error}") # Fetch and display the messages messages = project_client.agents.list_messages(thread_id=thread.id) print("Messages:") pprint(messages["data"][0]["content"][0]["text"]["value"]) # Optional cleanup: # project_client.agents.delete_agent(agent.id) # print("Deleted agent") Run the agent: To run the agent, run the following command python billing_anomaly_agent.py Snippet of output from agent: Using the Azure AI Foundry Agent Playground: After running your agent using the Azure AI Agent SDK, it is saved within your Azure AI Foundry project. You can now experiment with it using the Agent Playground. To try it out: Go to the Agents section in your Azure AI Foundry workspace. Find your billing anomaly agent in the list and click to open it. Use the playground interface to test different financial or billing-related questions, such as: “Did tenant_456 exceed their API usage quota this month?” “Explain recent storage usage changes for tenant_456.” This is a great way to validate your agent's behavior without writing more code. Summary: You’ve now created a working AI agent that talks to your Postgres database, all using: A simple Python function Azure AI Agent Service A Neon Serverless Postgres backend This approach is beginner-friendly, lightweight, and practical for real-world use. Want to go further? You can: Add more tools to the agent Integrate with vector search (e.g., detect anomaly reasons from logs using embeddings) Resources: Introduction to Azure AI Agent Service Develop an AI agent with Azure AI Agent Service Getting Started with Azure AI Agent Service Neon on Azure Build AI Agents with Azure AI Agent Service and Neon Multi-Agent AI Solution with Neon, Langchain, AutoGen and Azure OpenAI Azure AI Foundry GitHub Discussions That's it, folks! But the best part? You can become part of a thriving community of learners and builders by joining the Microsoft Learn Student Ambassadors Community. Connect with like-minded individuals, explore hands-on projects, and stay updated with the latest in cloud and AI. 💬 Join the community on Discord here and explore more benefits on the Microsoft Learn Student Hub.
Back to school again... again... again
hi all I have been having an issue where each time open planner it gives me the guide. this happens when I log in or refresh the page. Its three clicks that I would like to reduce when I'm trying to re-enter my day. there appears to be no option to permanently say I got the message and move on with my life till the next big update. the X removes the message so that would be one click, clicking next till the end dose the same, but as soon as I need to refresh (because the Planner is out of sync with the cloud) bam "easy filtering", "Create a task", "start a plan". I got it the first time. Perhaps I'm missing something.Microsoft 365 Administration Cookbook: Essential Recipes for IT Pros
I'm excited to announce the release of my 10th book, Microsoft 365 Administration Cookbook: Enhance Your Microsoft 365 Productivity to Manage and Optimize Its Apps and Services. This fully updated second edition cookbook is packed with recipes to spice up and streamline your Microsoft 365 administration and features a foreword by Karuana Gatimu, Director of Microsoft's M365 Customer Advocacy Group. Key Features: Manage Identities and Roles: Efficiently handle Microsoft 365 identities, groups, and permissions. Streamline Communication and Teamwork: Optimize Microsoft Teams, Exchange Online, and SharePoint for seamless collaboration. Enhance Productivity and Knowledge Sharing: Leverage Microsoft Search, SharePoint, and OneDrive for effective information retrieval and document management. Automate with PowerShell: Master PowerShell to automate tasks and manage roles, improving service efficiency. Optimize Security and Compliance: Strengthen your environment with Microsoft Defender and manage compliance with Microsoft Purview. This cookbook provides step-by-step recipes for app configurations and administrative tasks, offering strategies for managing Microsoft 365 apps and services. It covers new features and capabilities introduced in this edition and guides you through navigating Microsoft 365 subscription options and services. Whether you're a seasoned IT professional or new to Microsoft 365, this book is designed to enhance your skills with practical insights and best practices. Purchase your copy today. Thanks for your support, Nate ChamberlainGUIDE: Where to find information about Windows Updates and release information (any version)
WINDOWS RELEASE HEALTH DASHBOARD Contains all links below, except for old OSes, Azure Stack HCI, Microcode Updates, Flight hub, Servicing Stack Updates https://docs.microsoft.com/windows/release-health/ RESOLVED AND KNOWN ISSUES All OS, except Azure Stack HCI OS Expand categories on the drop-down menu left hand side https://docs.microsoft.com/windows/release-health/windows-message-center RELEASE INFORMATION + SUPPORT LIFECYCLE Gives an overview about Windows 10 release dates and end of support / supported OS versions and recommended versions. Azure Stack HCI OS https://docs.microsoft.com/azure-stack/hci/release-information Windows Server 2016 - 2022 LTSC, Windows Version version xxxx SAC https://docs.microsoft.com/windows/release-health/windows-server-release-info Windows 11 GAC (+ former SAC) https://docs.microsoft.com/windows/release-health/windows11-release-information Windows 10 GAC (+ former SAC) https://docs.microsoft.com/windows/release-health/release-information Windows 10 2015 - 2021 LTSC https://docs.microsoft.com/windows/whats-new/ltsc/ INSIDER FLIGHT HUB Announces changes for Windows 10 / 11 Insider builds Dev, Beta and release preview builds and patches as well as Windows Feature Pack Updates descriptions (excluding Windows Server Insider and ADK) https://docs.microsoft.com/windows-insider/flight-hub/ INTEL MICROCODE UPDATES Tables top to bottom contains the higher level of security / number of mitigations. Means the bottom table has the highest level of protection / mitigation. These are partly part of the OS depending on the version; therefore, some Windows versions are not listed. If you use WSUS consider importing these updates. Unfortunately they are not part of any product category, even though they are security releated. https://support.microsoft.com/topic/summary-of-intel-microcode-updates-08c99af2-075a-4e16-1ef1-5f6e4d8637c4 Microsoft is making available Intel-validated microcode updates that are related to Spectre Variant 2 (CVE 2017-5715 [“Branch Target Injection”]). Spectre Variant 3a (CVE-2018-3640: "Rogue System Register Read (RSRE)") Spectre Variant 4 (CVE-2018-3639: "Speculative Store Bypass (SSB)"), and L1TF (CVE-2018-3620, CVE-2018-3646: "L1 Terminal Fault"). The following table lists specific Microsoft Knowledge Base articles by Windows version. The article contains links to the available Intel microcode updates by CPU. KB number and description Windows version Source KB4589212: Intel microcode updates Windows 10, version 2004 and 20H2, and Windows Server, version 2004 and 20H2 Microsoft Update Catalog KB4497165 Intel microcode updates Windows 10, version 1903 and 1909, Windows Server, version 1903 and 1909 Microsoft Update Catalog KB4494174 Intel microcode updates Windows 10, version 1809, Windows Server 2019 Microsoft Update Catalog KB4494451 Intel microcode updates Windows 10, version 1803, and Windows Server, version 1803 Microsoft Update Catalog KB4494452 Intel microcode updates Windows 10, version 1709, and Windows Server 2016, version 1709 Microsoft Update Catalog KB4494453 Intel microcode updates Windows 10, version 1703 Microsoft Update Catalog KB4494175 Intel microcode updates Windows 10, version 1607, and Windows Server 2016 Microsoft Update Catalog KB4494454 Intel microcode updates Windows 10 (RTM) Microsoft Update Catalog SERVICING STACK UPDATES These special updates for maintain the servicing stack of Windows (which is responsible for updates, dism, adding and removing features etc) have to be installed before any regular update. Currently Windows 10 1909, Windows Server 2019 and Azure Stack HCI OS have combined CUs, means the SSU is already integrated and will care for the correct installation order. Windows Client OS, Windows Server, Azure Stack HCI OS 21H2 and later version have integrated SSU into the CU. Possibly this will be backported to earlier versions. For https://msrc.microsoft.com/update-guide/vulnerability/ADV990001 WINDOWS UPDATE HISTORY aka Release Notes Check the KBs, release date, changes, known issues and remediations of Windows Updates per Windows Version AZURE STACK HCI OS Azure Stack HCI OS 21H2 (release + public preview) Build 20348 https://support.microsoft.com/en-us/topic/release-notes-for-azure-stack-hci-version-21h2-5c5e6adf-e006-4a29-be22-f6faeff90173 Azure Stack HCI OS 20H2 Build 17784 https://support.microsoft.com/topic/release-notes-for-azure-stack-hci-64c79b7f-d536-015d-b8dd-575f01090efd WINDOWS SERVER LTSC Windows Server 2022 LTSC build 20348, internal 21H2 http://support.microsoft.com/help/5005454 Windows Server 2019 LTSC build 17763, internal 1809 https://support.microsoft.com/topic/windows-10-and-windows-server-2019-update-history-725fc2e1-4443-6831-a5ca-51ff5cbcb059 Windows Server 2016 LTSC build 14393, internal 1607 https://support.microsoft.com/topic/windows-10-and-windows-server-2016-update-history-4acfbc84-a290-1b54-536a-1c0430e9f3fd Windows Server 2012 R2 / 2012 R2 Update 1 KB2919355 / ESU (soon) build 6.3.9200 / build 6.3.9600 Mind that you cannot install updates after KB2919355 if this update and prerequisites are not installed https://support.microsoft.com/topic/windows-8-1-and-windows-server-2012-r2-update-history-47d81dd2-6804-b6ae-4112-20089467c7a6 Windows Server 2012 + ESU (soon) build 6.2.9200 https://support.microsoft.com/topic/windows-server-2012-update-history-abfb9afd-2ebf-1c19-4224-ad86f8741edd Windows Server 2008 R2 Service Pack 1 (SP1) + ESU build 6.1.7601 https://support.microsoft.com/topic/windows-7-sp1-and-windows-server-2008-r2-sp1-update-history-720c2590-fd58-26ba-16cc-6d8f3b547599 Windows Server 2008 Service Pack 2 (SP2) build 6.0.6002 https://support.microsoft.com/topic/updateverlauf-f%C3%BCr-windows-server-2008-sp2-9197740a-7430-f69f-19ff-4998a4e8b25b WINDOWS SERVER VERSION, GAC (+ former SAC) This special core version of Windows Server, eligble to use with active Software Assurance for Windows Server started with 1709 ends with 20H2. For Update history check the Windows 10 release notes. I have not listed them specifically due to their very specific usecase and probably low installation count. WINDOWS OS LTSC (Long-Term Support Channel) Windows 10 21H2 LTSC https://support.microsoft.com/topic/windows-10-update-history-857b8ccb-71e4-49e5-b3f6-7073197d98fb (link unconfirmed to apply for LTSC as of 13.01.2022) Windows 10 1809 LTSC Build 17763 https://support.microsoft.com/topic/windows-10-and-windows-server-2019-update-history-725fc2e1-4443-6831-a5ca-51ff5cbcb059 Windows 10 1607 LTSC Build 14393 https://support.microsoft.com/topic/windows-10-and-windows-server-2016-update-history-4acfbc84-a290-1b54-536a-1c0430e9f3fd Windows 10 1507 LTSC Build 10240 Note that this build has no sub build numbers yet so you cannot recognize in the version which update / CU is installed https://support.microsoft.com/topic/windows-10-update-history-93345c32-4ae1-6d1c-f885-6c0b718adf3b WINDOWS OS, SAC (naming till 21H1), GAC (General Availability Channel, naming for 21H2 and later) Windows 11 version 21H2 Build 22000 https://support.microsoft.com/topic/windows-11-update-history-a19cd327-b57f-44b9-84e0-26ced7109ba9 Windows 10 version 21H2 Build 19044 https://support.microsoft.com/topic/windows-10-update-history-857b8ccb-71e4-49e5-b3f6-7073197d98fb Windows 10 version 21H1 Build 19043 https://support.microsoft.com/topic/windows-10-update-history-1b6aac92-bf01-42b5-b158-f80c6d93eb11 Windows 10 version 20H2 Build 19042 https://support.microsoft.com/topic/windows-10-update-history-7dd3071a-3906-fa2c-c342-f7f86728a6e3 Windows 10 version 2004 Build 19041 https://support.microsoft.com/topic/windows-10-update-history-24ea91f4-36e7-d8fd-0ddb-d79d9d0cdbda Windows 10 version 1909 Build 18363 https://support.microsoft.com/topic/windows-10-update-history-53c270dc-954f-41f7-7ced-488578904dfe Windows 10 version 1903 Build 18362 https://support.microsoft.com/topic/windows-10-update-history-e6058e7c-4116-38f1-b984-4fcacfba5e5d Windows 10 version 1809 Build 17763 https://support.microsoft.com/topic/windows-10-and-windows-server-2019-update-history-725fc2e1-4443-6831-a5ca-51ff5cbcb059 Windows 10 version 1803 Build 17134 https://support.microsoft.com/topic/windows-10-update-history-0d8c2da6-3dba-66e4-2ef2-059192bf7869 Windows 10 version 1709 Build 16299 https://support.microsoft.com/topic/windows-10-and-windows-server-update-history-8e779ac1-e840-d3b8-524e-91037bf7645a Windows 10 version 1703 Build 15063 https://support.microsoft.com/topic/windows-10-update-history-83aa43c0-82e0-92d8-1580-10642c9ed612 Windows 10 version 1607 Build 14393 https://support.microsoft.com/topic/windows-10-and-windows-server-2016-update-history-4acfbc84-a290-1b54-536a-1c0430e9f3fd Windows 10 version 1511 Build 10586 https://support.microsoft.com/topic/windows-10-update-history-2ad7900f-882c-1dfc-f9d7-82b7ca162010 Windows 10 version 1507 Build 10240 Note that this build has no sub build numbers yet so you cannot recognize in the version which update / CU is installed https://support.microsoft.com/topic/windows-10-update-history-93345c32-4ae1-6d1c-f885-6c0b718adf3b Windows 8.1 + 8.1 Update 1 KB2919355 build 6.3.9200 / 6.3.9600 Mind that you cannot install updates after KB2919355 if this update and prerequisites are not installed https://support.microsoft.com/topic/windows-8-1-and-windows-server-2012-r2-update-history-47d81dd2-6804-b6ae-4112-20089467c7a6 Windows 8.0 build 6.2 https://support.microsoft.com/topic/windows-server-2012-update-history-abfb9afd-2ebf-1c19-4224-ad86f8741edd Windows 7.0 Service Pack 1 (SP1) build 6.1.7601 https://support.microsoft.com/topic/windows-7-sp1-and-windows-server-2008-r2-sp1-update-history-720c2590-fd58-26ba-16cc-6d8f3b547599 Windows Vista Service Pack 2 (SP2) build 6.0.6002 https://support.microsoft.com/en-us/topic/windows-server-2008-sp2-update-history-9197740a-7430-f69f-19ff-4998a4e8b25b HISTORY: 13.01.2022 - added release information for Azure Stack HCI, Windows 10 LTSC, corrections on naming conventions of SAC, GAC, LTSC 13.12.2021 - corrected some links that directly linked to en-us instead of the generic page which would redirect to localized pages, - added Windows 10 21H2 SAC link. - honoured naming convention branch > channel. - added General availability channel which is the successor of the SAC (Semi Annual Channel starting with 21H2 releases. 03.11.2021 - Added information for Windows 11 21H1 13.10.2021 - Added information to Azure Stack HCI 21H2 release, other docs still not mention it except update history 07.10.2021 - Added Intel Microcode updates for all available threats and versions 07.10.2021 - Updated one available link for Windows 11. Update histories for 21H2 version W10/W11 are still missing 17.09.2021 - fixed some links pointing to a specific language 17.09.2021 - formatting, seperating LTSC from SAC for Windows 10, added SSUs 17.09.2021 - initial version
