🔒 Strengthening Azure DNS Zone Security with RBAC and Resource Locks
🔎 DNS security is more than just configuration it’s about protecting critical assets against unauthorized changes and accidental deletions. 🔎 Managing DNS zones effectively requires a layered security approach. 🔎 Two powerful mechanisms in Azure : Role-Based Access Control (RBAC) and Resource Locks 🚀 Role-Based Access Control (RBAC) 🚀 * Granular DNS Access Control * RBAC ensures controlled access management at both the DNS zone and record set levels. * Instead of assigning broad permissions, RBAC enables precise delegation using built-in roles such as: 🔹 Owner – Full control over the DNS zone, including configurations and deletions. 🔹 Contributor – Can modify DNS settings but cannot change access permissions. 🔹 Network Contributor – Can manage networking configurations related to DNS, but not modify records. 🔹 DNS Zone Contributor – Dedicated role for managing DNS zones without broader networking privileges. ✅ Key Advantages of RBAC in DNS Security: ✔ Prevent unauthorized modifications by restricting access to only necessary roles. ✔ Ensure operational integrity by limiting exposure to critical configurations. ✔ Improve governance by aligning roles with organizational security policies. 🔐 Resource Locks 🔐 * Guardrails for DNS Protection * Even with well-defined RBAC settings, accidental deletions can still occur. * Azure Resource Locks add an additional safeguard by preventing changes to a DNS zone or specific record sets. 🔹 Zone Lock ----> Protects an entire DNS zone from being deleted, preserving all associated record sets. 🔹 SOA Lock ----> Prevents unintentional zone deletions while allowing record modifications within the zone. ✅ How Resource Locks Enhance Security: ✔ Shields DNS zones from accidental or malicious deletions. ✔ Maintains continuity by ensuring record sets remain intact. ✔ Strengthens compliance controls for critical infrastructure. 🛠 Best Practices for Securing DNS with RBAC & Resource Locks 🔸 Assign least privilege roles—never give unnecessary access. 🔸 Implement locks on essential zones to prevent configuration errors. 🔸 Regularly audit access permissions using Azure Policy & Activity Logs. 🔸 Use Automation & Alerts to track modifications for enhanced security. 🔹 Implementing RBAC & Resource Locks ensures your cloud environment remains secure, operational, and fault-tolerant.
BYOPI - Design your own custom private AI Search indexer with no code ADF (SQLServer on private VM)
Executive Summary Building a fully private search indexing solution using Azure Data Factory (ADF) to sync SQL Server data from private VM to Azure AI Search is achievable but comes with notable complexities and limitations. This blog shares my journey, discoveries, and honest assessment of the BYOPI (Build Your Own Private Indexer) architecture. Architectural flow: Table of Contents Overall Setup How ADF works in this approach with Azure AI Search Challenges - discovered Pros and Cons: An Honest Assessment Conclusion and Recommendations 1. Overall Setup: Phase 1: Resource Group & Network Setup : create resource group and vNET (virtual network) in any region of your choice Phase 2: Deploy SQL Server VM: Phase 3: Create Azure Services - ADF (Azure Data Factory), Azure AI Search and AKV (Azure Key Vault) service from portal or from your choice of deployment. Phase 4: Create Private Endpoints for all the services in their dedicated subnets: Phase 5: Configure SQL Server on VM : connect to VM via bastion and setup database, tables & SP: Sample metadata used as below: CREATE DATABASE BYOPI_DB; GO USE BYOPI_DB; GO CREATE TABLE Products ( ProductId INT IDENTITY(1,1) PRIMARY KEY, ProductName NVARCHAR(200) NOT NULL, Description NVARCHAR(MAX), Category NVARCHAR(100), Price DECIMAL(10,2), InStock BIT DEFAULT 1, Tags NVARCHAR(500), IsDeleted BIT DEFAULT 0, CreatedDate DATETIME DEFAULT GETDATE(), ModifiedDate DATETIME DEFAULT GETDATE() ); CREATE TABLE WatermarkTable ( TableName NVARCHAR(100) PRIMARY KEY, WatermarkValue DATETIME ); INSERT INTO WatermarkTable VALUES ('Products', '2024-01-01'); CREATE PROCEDURE sp_update_watermark @TableName NVARCHAR(100), @NewWatermark DATETIME AS BEGIN UPDATE WatermarkTable SET WatermarkValue = @NewWatermark WHERE TableName = @TableName; END; INSERT INTO Products (ProductName, Description, Category, Price, Tags) VALUES ('Laptop Pro', 'High-end laptop', 'Electronics', 1299.99, 'laptop,computer'), ('Office Desk', 'Adjustable desk', 'Furniture', 599.99, 'desk,office'), ('Wireless Mouse', 'Bluetooth mouse', 'Electronics', 29.99, 'mouse,wireless'); Phase 6: Install Self-Hosted Integration Runtime Create SHIR in ADF: Go to ADF resource in Azure Portal Click "Open Azure Data Factory Studio" Note: You need to access from a VM in the same VNet or via VPN since ADF is private In ADF Studio, click Manage (toolbox icon) Select Integration runtimes → "+ New" Select "Azure, Self-Hosted" → "Self-Hosted" Name: SHIR-BYOPI or of your choice Click "Create" Copy Key1 (save it) Install SHIR on VM In the VM (via Bastion): Open browser, go to: https://www.microsoft.com/download/details.aspx?id=39717 Download and install Integration Runtime During setup: Launch Configuration Manager Paste the Key1 from Step 14 Click "Register" Wait for "Connected" status Phase 7: Create Search Index through below powershell script and saving it as search_index.ps1 $searchService = "search-byopi" $apiKey = "YOUR-ADMIN-KEY" $headers = @{ 'api-key' = $apiKey 'Content-Type' = 'application/json' } $index = @{ name = "products-index" fields = @( @{name="id"; type="Edm.String"; key=$true} @{name="productName"; type="Edm.String"; searchable=$true} @{name="description"; type="Edm.String"; searchable=$true} @{name="category"; type="Edm.String"; filterable=$true; facetable=$true} @{name="price"; type="Edm.Double"; filterable=$true} @{name="inStock"; type="Edm.Boolean"; filterable=$true} @{name="tags"; type="Collection(Edm.String)"; searchable=$true} ) } | ConvertTo-Json -Depth 10 Invoke-RestMethod ` -Uri "https://$searchService.search.windows.net/indexes/products-index?api-version=2020-06-30" ` -Method PUT ` -Headers $headers ` -Body $index Phase 8: Configure AKV & ADF Components - Link AKV and ADF for secrets Create Key Vault Secrets Navigate to kv-byopi (created AKV resource) in Portal Go to "Access policies" Click "+ Create" Select permissions: Get, List for secrets Select principal: adf-byopi-private Create Go to "Secrets" → "+ Generate/Import": Name: sql-password, Value: <> Name: search-api-key, Value: Your search key Create Linked Services in ADF Access ADF Studio from the VM (since it's private): Key Vault Linked Service: Manage → Linked services → "+ New" Search "Azure Key Vault" Configure: Name: LS_KeyVault Azure Key Vault: kv-byopi Integration runtime: AutoResolveIntegrationRuntime Test connection → Create SQL Server Linked Service: "+ New" → "SQL Server" Configure: Name: LS_SqlServer Connect via: SHIR-BYOPI Server name: localhost Database: BYOPI_DB Authentication: SQL Authentication User: sqladmin Password: Select from Key Vault → LS_KeyVault → sql-password Test → Create Azure Search Linked Service: "+ New" → "Azure Search" Configure: Name: LS_AzureSearch URL: https://search-byopi.search.windows.net Connect via: SHIR-BYOPI - Important - use SHIR API Key: From Key Vault → LS_KeyVault → search-api-key Test → Create Phase 9: Create ADF Datasets and PipelineCreate Datasets SQL Products Dataset: Author → Datasets → "+" → "New dataset" Select "SQL Server" → Continue Select "Table" → Continue Properties: Name: DS_SQL_Products Linked service: LS_SqlServer Table: Select Products click OK Watermark Dataset: Repeat with: Name: DS_SQL_Watermark Table: WatermarkTable Search Dataset: "+" → "Azure Search" Properties: Name: DS_Search_Index Linked service: LS_AzureSearch Index name: products-index Create Pipeline Author → Pipelines → "+" → "Pipeline" Name: PL_BYOPI_Private From Activities → General, drag "Lookup" activity Configure Lookup 1: Name: LookupOldWatermark Settings: Source dataset: DS_SQL_Watermark Query: below sql SELECT WatermarkValue FROM WatermarkTable WHERE TableName='Products' - **First row only**: ✓ Add another Lookup: Name: LookupNewWatermark Query: below sql SELECT MAX(ModifiedDate) as NewWatermark FROM Products Add Copy Data activity: Name: CopyToSearchIndex Source: Dataset: DS_SQL_Products Query: sql SELECT CAST(ProductId AS NVARCHAR(50)) as id, ProductName as productName, Description as description, Category as category, Price as price, InStock as inStock, Tags as tags, CASE WHEN IsDeleted = 1 THEN 'delete' ELSE 'upload' END as [@search.action] FROM Products WHERE ModifiedDate > '@{activity('LookupOldWatermark').output.firstRow.WatermarkValue}' AND ModifiedDate <= '@{activity('LookupNewWatermark').output.firstRow.NewWatermark}' Sink: Dataset: DS_Search_Index Write behavior: Merge Batch size: 1000 Add Stored Procedure activity: Name: UpdateWatermark SQL Account: LS_SqlServer Stored procedure: sp_update_watermark Parameters: TableName: Products NewWatermark: @{activity('LookupNewWatermark').output.firstRow.NewWatermark} Connect activities with success conditions Phase 10: Test and Schedule Test Pipeline Click "Debug" in pipeline Monitor in Output panel Check for green checkmarks Create Trigger In pipeline, click "Add trigger" → "New/Edit" Click "+ New" Configure: Name: TR_Hourly Type: Schedule Recurrence: Every 1 Hour OK → Publish All Monitor Go to Monitor tab View Pipeline runs Check Trigger runs Your pipeline should look like this: Phase 11: Validation & Testing Verify Private Connectivity From the VM, run PowerShell: # Test DNS resolution (should return private IPs) nslookup adf-byopi-private.datafactory.azure.net # Should show private IP like : 10.0.2.x nslookup search-byopi.search.windows.net # Should show private IP like : 10.0.2.x nslookup kv-byopi.vault.azure.net # Should show private IP like : 10.0.2.x # Test Search $headers = @{ 'api-key' = 'YOUR-KEY' } Invoke-RestMethod -Uri "https://search-byopi.search.windows.net/indexes/products-index/docs?`$count=true&api-version=2020-06-30" -Headers $headers Test Data Sync (adding few records) and verify in search index: -- Add test record INSERT INTO Products (ProductName, Description, Category, Price, Tags) VALUES ('Test Product Private', 'Testing private pipeline', 'Test', 199.99, 'test,private'); -- Trigger pipeline manually or wait for schedule -- Then verify in Search index 2. How ADF works in this approach with Azure AI search: Azure AI Search uses a REST API for indexing or called as uploading. When ADF sink uploads data to AI Search, it's actually making HTTP POST requests: for example - POST https://search-byopi.search.windows.net/indexes/products-index/docs/index?api-version=2020-06-30 Content-Type: application/json api-key: YOUR-ADMIN-KEY { "value": [ { "@search.action": "upload", "id": "1", "productName": "Laptop", "price": 999.99 }, { "@search.action": "delete", "id": "2" } ] } Delete action used here is soft delete and not hard delete. pipeline query: SELECT CAST(ProductId AS NVARCHAR(50)) as id, -- Renamed to match index field ProductName as productName, -- Renamed to match index field Description as description, Category as category, Price as price, InStock as inStock, Tags as tags, CASE WHEN IsDeleted = 1 THEN 'delete' ELSE 'upload' END as [@search.action] -- Special field with @ prefix FROM Products WHERE ModifiedDate > '2024-01-01' ``` Returns this resultset: ``` id | productName | description | category | price | inStock | tags | @search.action ----|----------------|------------------|-------------|--------|---------|----------------|--------------- 1 | Laptop Pro | High-end laptop | Electronics | 1299 | 1 | laptop,computer| upload 2 | Office Chair | Ergonomic chair | Furniture | 399 | 1 | chair,office | upload 3 | Deleted Item | Old product | Archive | 0 | 0 | old | delete The @search.action Field - The Magic Control This special field tells Azure AI Search what to do with each document: @search.action What It Does When to Use What Happens If Document... upload Insert OR Update Most common - upsert operation Exists: Updates it<br>Doesn't exist: Creates it merge Update only When you know it exists Exists: Updates specified fields<br>Doesn't exist: ERROR mergeOrUpload Update OR Insert Safe update Exists: Updates fields<br>Doesn't exist: Creates it delete Remove from index To remove documents Exists: Deletes it<br>Doesn't exist: Ignores (no error) ADF automatically converts SQL results to JSON format required by Azure Search: { "value": [ { "@search.action": "upload", "id": "1", "productName": "Laptop Pro", "description": "High-end laptop", "category": "Electronics", "price": 1299.00, "inStock": true, "tags": "laptop,computer" }, { "@search.action": "upload", "id": "2", "productName": "Office Chair", "description": "Ergonomic chair", "category": "Furniture", "price": 399.00, "inStock": true, "tags": "chair,office" }, { "@search.action": "delete", "id": "3" // For delete, only ID is needed } ] } ADF doesn't send all records at once. It batches them based on writeBatchSize and each batch is a separate HTTP POST to Azure Search How ADF will detect new changes and run batches: Watermark will be updated after each successful ADF run to detect new changes as below: Handling different scenarios: Scenario 1: No Changes Between Runs: Run at 10:00 AM: - Old Watermark: 09:45:00 - New Watermark: 10:00:00 - Query: WHERE ModifiedDate > '09:45' AND <= '10:00' - Result: 0 rows - Action: Still update watermark to 10:00 - Why: Prevents reprocessing if changes come later Scenario 2: Bulk Insert Happens: Someone inserts 5000 records at 10:05 AM Run at 10:15 AM: - Old Watermark: 10:00:00 - New Watermark: 10:15:00 - Query: WHERE ModifiedDate > '10:00' AND <= '10:15' - Result: 5000 rows - Action: Process all 5000, update watermark to 10:15 Scenario 3: Pipeline Fails Run at 10:30 AM: - Old Watermark: 10:15:00 (unchanged from last success) - Pipeline fails during Copy activity - Watermark NOT updated (still 10:15:00) Next Run at 10:45 AM: - Old Watermark: 10:15:00 (still the last successful) - New Watermark: 10:45:00 - Query: WHERE ModifiedDate > '10:15' AND <= '10:45' - Result: Gets ALL changes from 10:15 to 10:45 (30 minutes of data) - No data loss! Note: There is still room for improvement by refining this logic to handle more advanced scenarios. However, I have not examined the logic in depth, as the goal here is to review how the overall setup functions, identify its limitations, and compare it with the indexing solutions available in AI Search. 3. Challenges - disovered: When I tried to set out to build a private search indexer for SQL Server data residing on an Azure VM with no public IP, the solution seemed straightforward: use Azure Data Factory to orchestrate the data movement to Azure AI Search. The materials made it sound simple. The reality? It's possible, but the devil is in the details. What We Needed: ✅ SQL Server on private VM (no public IP) ✅ Azure AI Search with private endpoint ✅ No data over public internet ✅ Support for full CRUD operations ✅ Near real-time synchronization ✅ No-code/low-code solution Reality Check: ⚠️ DELETE operations not natively supported in ADF sink ⚠️ Complex networking requirements ⚠️ Higher costs than expected ⚠️ Significant setup complexity ✅ But it IS possible with workarounds Components Required Azure VM: ~$150/month (D4s_v3) Self-Hosted Integration Runtime: Free (runs on VM) Private Endpoints: ~$30/month (approx 3 endpoints) Azure Data Factory: ~$15-60/month (depends on frequency) Azure AI Search: ~$75/month (Basic tier) Total: ~$270-315/month** The DELETE Challenge: Despite Azure AI Search REST API fully supporting delete operations via @search.action, ADF's native Azure Search sink does NOT support delete operations. -- This SQL query with delete action SELECT ProductId as id, CASE WHEN IsDeleted = 1 THEN 'delete' ELSE 'upload' END as [@search.action] FROM Products -- Will NOT delete documents in Azure Search when using Copy activity -- The @search.action = 'delete' is ignored by ADF sink! Nevertheless, there is a workaround using the Web Activity approach or by calling the REST API from the ADF side to perform the delete operation. { "name": "DeleteViaREST", "type": "Web", "typeProperties": { "url": "https://search.windows.net/indexes/index/docs/index", "method": "POST", "body": { "value": [ {"@search.action": "delete", "id": "123"} ] } } } Development Challenges No Direct Portal Access: With ADF private, you need: Jump box in the same VNet VPN connection Bastion for access Testing Complexity: Can't use Postman from local machine Need to test from within VNet Debugging requires multiple tools 4. Pros and Cons: An Honest Assessment: Pros: Security: Complete network isolation Compliance: Meets strict requirements No-Code: Mostly configuration-based Scalability: Can handle large datasets Monitoring: Built-in ADF monitoring Managed Service: Microsoft handles updates Cons: DELETE Complexity: Not natively supported Cost: Higher than expected Setup Complexity: Many moving parts Debugging: Difficult with private endpoints Hidden Gotchas: - SHIR requires Windows VM (Linux in preview) - Private endpoint DNS propagation delays - ADF Studio timeout with private endpoints - SHIR auto-update can break pipelines 5. Conclusion and Recommendations: When to Use BYOPI: ✅ Good Fit: Strict security requirements Needs indexing from an un-supported scenarios for example SQL server residing on private VM Budget > $500/month Team familiar with Azure networking Read-heavy workloads ❌ Poor Fit: Simple search requirements Budget conscious Need real-time updates Heavy DELETE operations Small team without Azure expertise BYOPI works, but it's more complex and expensive than initially expected. The lack of native DELETE support in ADF sink is a significant limitation that requires workarounds. Key Takeaways It works but requires significant effort DELETE (hard) operations need workarounds Costs will be higher than expected Complexity is substantial for a "no-code" solution Alternative solutions might be better for many scenarios Disclaimer: The sample scripts provided in this article are provided AS IS without warranty of any kind. The author is not responsible for any issues, damages, or problems that may arise from using these scripts. Users should thoroughly test any implementation in their environment before deploying to production. Azure services and APIs may change over time, which could affect the functionality of the provided scripts. Always refer to the latest Azure documentation for the most up-to-date information. Thanks for reading this blog! I hope you've found this approach of creating own private indexing solution for Azure AI Search (BYOPI) useful 😀Locked out of Azure account for 5 months! Spent hours on phone, still no resolution! PLEASE HELP!!!
As per the title, back in April I somehow managed to lock myself out of my Azure portal. Typically to sign in my browser would auto-fill in the password field - for some reason that fateful day the auto-fill function didn't work. So I typed in what I believed to be the password, it wasn't. Annoyed, I typed in a few more passwords (even checked browser password manager to ensure I was typing the correct password) and finally locked myself out. Worse still, the email address that Microsoft wanted to send its verification code as a back-up contingency is no longer active (domain wasn't renewed and has since been bought). So Microsoft is sending a verification email to a dead email address... When I try to reset the password via Microsoft's questionnaire, once submitted I then get an automated email response back stating I haven't provided enough correct/relevant information, so the password can't be reset. I have long since lost any hope or faith in Microsoft rectifying this issue. Being courteous on the phone and apologising constantly is all well and good, but is only meaningful if there is a resolution. All that's happened is I've been passed around from one department to another and back again, before eventually being ghosted back in the summer. I have since opened another support ticket which is already winding its way around to ultimately leading me down another dead end. At this stage, all I want is for Microsoft to release my SQL database (my intellectual property) back to me. I am able to provide old invoices relating to my Azure account (when I was able to log in and download invoices!), as well as proof of ID to prove I am who I say I am - enough is enough! Please advise.
SharePoint NoAccess Sites: Search Indexing and Copilot Misconceptions Guide
What is NoAccess Mode in SharePoint? NoAccess mode is a site-level setting in SharePoint Online that restricts user access to the site without permanently deleting it. Think of it as putting the site behind a locked door, the content still exists, but no one can open it. Why Do Organizations Use It? Temporary Lockdown: When a site is under review, being decommissioned, or needs to be secured quickly. Compliance & Security: Helps prevent accidental data exposure during audits or ownership changes. Preserve Data: Unlike deleting a site, NoAccess keeps the content intact for future reference or migration. How Does It Affect Search and Copilot? Search Indexing: By default, NoAccess mode does not remove the site from the search index. This means files may still appear in search results unless additional controls (like Restricted Content Discovery or NoCrawl) are applied. Copilot Behavior: Copilot uses the same index as Microsoft Search. If a site remains indexed, Copilot can surface summaries or references to its content even if users can’t open the files. This is why governance settings like Restricted Access Control or disabling indexing are critical when using Copilot. Why does this happen? NoAccess blocks site access, not indexing. The site remains in the search index unless indexing is explicitly disabled or Restricted Content Discovery (RCD) is enabled. Security trimming still applies. Users will only see items they have direct permissions to (e.g., via shared links). They cannot open anything they don’t have access to. Copilot respects permissions. It uses the same security model as Microsoft Search and Graph, so it never bypasses access controls. Low Priority. Marking a site as NoAccess is a bulk operation that goes into a low priority queue, specifically to avoid system bottlenecks and ensure real-time content changes are prioritized over less critical updates which means it can take much longer than expected for those sites to stop appearing in search results. What are the options to fully hide content? Turn off Allow this site to appear in search results: This setting removes the site from indexing. Note: change the search setting BEFORE setting NoAccess to a site. Enable Restricted Content Discovery (RCD): This hides the site from search and Copilot while keeping it accessible to those with permissions. There is a PowerShell cmdlet available: Set-SPOSite –identity <site-url> -RestrictContentOrgWideSearch $true Please note that for larger sites, both the RCD and no-crawl processes may require a minimum of a week to reflect updates. According to the RCD documentation, sites with more than 500,000 pages could experience update times exceeding one week. What are the options to get Site Crawl information? When setting up the site for NoCrawl, you can run REST to see if the items are returning in search from that site. You can use a simple REST call like: https://contoso.sharepoint.com/_api/search/query?querytext='path:"<siteurl>"'&sourceid='8413cd39-2156-4e00-b54d-11efd9abdb89'&trimduplicates=false. You have to login into the tenant first. An XML object will be generated, please look for <d:TotalRows m:type="Edm.Int32">1</d:TotalRows> you will see the count going down, at some point the count will be equals to 0, that means all items were removed from index. You can use PnP to check the site settings, here an example - Enable/Disable Search Crawling on Sites and Libraries | PnP Samples, remember PnP is open source and it is not supported by Microsoft. Get-PnPSite | Select NoCrawl Key Takeaways Setting a SharePoint site to NoAccess does not automatically remove it from search or Copilot. Copilot and Search always enforce permissions users never see or access unauthorized content. For complete removal, disable site indexing or enable RCD. Monitor index status to confirm content is truly hidden. Understanding and managing these settings ensures secure, seamless experiences with Copilot and Microsoft Search. Helpful Resources Lock and unlock sites - SharePoint in Microsoft 365 | Microsoft Learn Enable/Disable Search Crawling on Sites and Libraries | PnP Samples Restrict discovery of SharePoint sites and content - SharePoint in Microsoft 365 | Microsoft Learn Contributors: Tania Menice
Avoiding Access Errors with SharePoint App-Only Access
To avoid persistent access errors like “403 Forbidden” when using SharePoint Online REST API with app-only permissions, it is essential to authenticate using a self-signed X.509 certificate rather than a client secret, as SharePoint requires certificate-based authentication for app-only access to ensure stronger security and validation. The solution involves generating a certificate, updating the Azure AD app with it, and using it to obtain access tokens, as demonstrated in PnP PowerShell examples and Microsoft documentation.Securing Data with Microsoft Purview IRM + Defender: A Hands-On Lab
Hi everyone I recently explored how Microsoft Purview Insider Risk Management (IRM) integrates with Microsoft Defender to secure sensitive data. This lab demonstrates how these tools work together to identify, investigate, and mitigate insider risks. What I covered in this lab: Set up Insider Risk Management policies in Microsoft Purview Connected Microsoft Defender to monitor risky activities Walkthrough of alerts triggered → triaged → escalated into cases Key governance and compliance insights Key learnings from the lab: Purview IRM policies detect both accidental risks (like data spillage) and malicious ones (IP theft, fraud, insider trading) IRM principles include transparency (balancing privacy vs. protection), configurable policies, integrations across Microsoft 365 apps, and actionable alerts IRM workflow follows: Define policies → Trigger alerts → Triage by severity → Investigate cases (dashboards, Content Explorer, Activity Explorer) → Take action (training, legal escalation, or SIEM integration) Defender + Purview together provide unified coverage: Defender detects and responds to threats, while Purview governs compliance and insider risk This was part of my ongoing series of security labs. Curious to hear from others — how are you approaching Insider Risk Management in your organizations or labs?
Service Trust Portal no longer support Microsoft Account (MSA) access
Dear all, We need to access certain documents (i.e., SOC 2 or ISO 27xxx) on the https://servicetrust.microsoft.com/DocumentPage/d013b518-c1fe-462c-8124-de901f3b68dc. To download documents you need to be signed in first. However, when I click on "sign in" (using the same email/account as for our azure account) I get the error message "Service Trust Portal no longer support Microsoft Account (MSA) access." (see screenshot below). It seems that I am not the only one since other users had similar issues but they also could not find a solution (or at least it was not mentioned in their post): https://techcommunity.microsoft.com/t5/security-compliance-and-identity/cannot-login-to-service-trust-portal/m-p/3632978 I have been trying this now since more than a week and also created a support ticket (which has not been assigned to a support agent yet). It is quite cumbersome and I hope some of you could have an idea since getting these documents is quite crucial for us.Deep Dive: Insider Risk Management in Microsoft Purview
Hi everyone I recently explored the Insider Risk Management (IRM) workflow in Microsoft Purview and how it connects across governance, compliance, and security. This end-to-end process helps organizations detect risky activities, triage alerts, investigate incidents, and take corrective action. Key Phases in the IRM Workflow: Policy: Define rules to detect both accidental (data spillage) and malicious risks (IP theft, fraud, insider trading). Alerts: Generate alerts when policies are violated. Triage: Prioritize and classify alerts by severity. Investigate: Use dashboards, Content Explorer, and Activity Explorer to dig into context. Action: Take remediation steps such as user training, legal escalation, or SIEM integration. Key takeaways from my lab: Transparency is essential (balancing privacy vs. protection). Integration across Microsoft 365 apps makes IRM policies actionable. Defender + Purview together unify detection + governance for insider risk. This was part of my ongoing security lab series. Curious to hear from the community — how are you applying Insider Risk Management in your environments or labs?Custom Windows Server Standard VM on Azure: It Works, But Is It Licensing Compliant?
Hi everyone, I wanted to share a recent technical experience where I successfully created and deployed a Windows Server Standard VM on Azure using a fully custom image. I started by downloading the official Windows Server Standard Evaluation ISO. I created a Generation 2 VM in Hyper-V and completed the OS setup using the Desktop Experience edition. Once the configuration was done, I ran sysprep to generalize the image. After that, I converted the disk from VHDX to VHD in fixed format, which turned out to be a critical step because Azure does not accept dynamic disks. The resulting file was around 127 GB, so I uploaded it to a premium storage account container to ensure performance. From there, I created a Generation 2 image in Azure and deployed a new VM from it. I then activated the Standard edition with a valid product key. Everything worked smoothly, but I’m still unsure whether this method is fully compliant with Microsoft’s licensing policies. Specifically, I’m trying to understand if going from an Evaluation ISO to sysprep, upload, deployment, and activation in Azure is a valid and compliant scenario when not using BYOL with Software Assurance or a CSP license. Has anyone gone through this process or has any insights on the compliance aspect? Thanks in advance for any guidance or clarification.👉 Securing Azure Workloads: From Identity to Monitoring
Hi everyone 👋 — following up on my journey, I want to share how I approach end-to-end security in Azure workloads. - Identity First – Microsoft Entra ID for Conditional Access, PIM, and risk-based policies. - Workload Security – Defender for Cloud to monitor compliance and surface misconfigurations. - Visibility & Monitoring – Log Analytics + Sentinel to bring everything under one pane of glass. Through my projects, I’ve been simulating enterprise scenarios where security isn’t just a checklist — it’s integrated into the architecture. Coming soon: - A lab demo showing how Defender for Cloud highlights insecure configurations. - A real-world style Conditional Access baseline for Azure workloads. Excited to hear how others in this community are securing their Azure environments! #Azure | #AzureSecurity | #MicrosoftLearn | #ZeroTrust | #PerparimLabs
