The Hidden Reason Your Sentinel Playbook Won't Show Up in Automation Rules (It's Not RBAC)
If a Logic App using the native Microsoft Sentinel incident trigger doesn't show up in the "Run playbook" picker of an Automation Rule — even though permissions, region, and connection are all correct — check the internal action name of the trigger in the JSON code view. If the Logic App was created while the Azure portal was set to a non-English language, the designer may generate a localized action name instead of the expected Microsoft_Sentinel_incident, and the playbook won't be picked up. Environment Microsoft Sentinel (Log Analytics workspace) Logic Apps (Consumption plan) Trigger: native Microsoft Sentinel connector, "Incident" trigger (/incident-creation path) Authentication: system-assigned Managed Identity Same subscription, resource group, and region as the Sentinel workspace Problem Two Logic Apps, both triggered by the native Microsoft Sentinel incident trigger, both using a system-assigned managed identity with the Microsoft Sentinel Responder role granted on the workspace. Same subscription, same resource group, same region. Only one of the two appeared in the "Run playbook" dropdown when configuring an Automation Rule in Sentinel. The other was simply absent — no error message, no warning, nothing in the run history to explain it. Investigation The usual suspects were checked and ruled out one by one: API connection health — one azuresentinel connection was indeed in an "Error" state, but it turned out not to be the one referenced by the affected trigger. Managed identity RBAC — the Microsoft Sentinel Responder role was correctly assigned to the Logic App's managed identity on the Sentinel workspace. User's own RBAC/PIM role — the Logic App Contributor role (required for the account configuring the Automation Rule to even see the resource) was active via PIM at the time of testing. Region/subscription/resource group — identical for both Logic Apps. Trigger type — both used the native "Incident in Microsoft Sentinel" trigger added through the visual designer, not a generic HTTP trigger or a deprecated connector. Every documented requirement was met. The playbook was still invisible. Root Cause Comparing the raw JSON (Logic app > Development tools > Code view) revealed the actual difference. Working Logic App (visible in the picker): "triggers": { "Microsoft_Sentinel_incident": { "type": "ApiConnectionWebhook", "inputs": { "body": { "callback_url": "@{listCallbackUrl()}" }, "path": "/incident-creation" } } } Broken Logic App (invisible in the picker): "triggers": { "Incident_dans_Microsoft_Sentinel": { "type": "ApiConnectionWebhook", "inputs": { "body": { "callback_url": "@listCallbackUrl()" }, "path": "/incident-creation" } } } The connector type and the path (/incident-creation) are identical. The one meaningful structural difference is the trigger's action name: Microsoft_Sentinel_incident versus Incident_dans_Microsoft_Sentinel. The second Logic App's trigger had been added while the Azure portal was set to French. The designer generated a localized action name instead of the canonical English one. Everything points to Sentinel's playbook discovery mechanism scanning Logic App definitions for that exact canonical action name (Microsoft_Sentinel_incident) to identify a resource as a valid Sentinel playbook — rather than relying solely on the connector type or the webhook path, as one would reasonably expect. Fix Renaming the trigger's action key in the JSON to the canonical name was enough: "triggers": { "Microsoft_Sentinel_incident": { ... } } No permission, connection, or region change was required — only this rename. Takeaways The Sentinel Automation Rule playbook picker appears to depend on the exact trigger action name, not just its type or functional behavior — a dependency that isn't documented anywhere in the official Microsoft docs as of this writing. The Azure portal's display language at the time a trigger is added has a direct impact on that Logic App's compatibility with Sentinel Automation Rules. A playbook can be fully functional (running without errors when triggered manually or through an older automation mechanism) while still being invisible in the newer Automation Rule selector — which makes this particular issue easy to miss, since nothing explicitly flags it. Recommendations Temporarily switch the Azure portal to English (Portal settings > Language and region) before adding a Sentinel trigger to a Logic App, if your team usually works in another language. If an existing Logic App won't show up despite an otherwise correct configuration, check the trigger's action name in the JSON code view first — it's a 30-second check that can save hours of RBAC/connection troubleshooting. Document this in your internal runbooks if your team works in a localized portal — this kind of detail is easy to lose and can cost significant troubleshooting time down the line. Has anyone else run into similar localization-related quirks in Sentinel or Logic Apps? Would be curious to hear about other cases in the comments.The Hidden Reason Your Sentinel Playbook Won't Show Up in Automation Rules (It's Not RBAC)
If a Logic App using the native Microsoft Sentinel incident trigger doesn't show up in the "Run playbook" picker of an Automation Rule — even though permissions, region, and connection are all correct — check the internal action name of the trigger in the JSON code view. If the Logic App was created while the Azure portal was set to a non-English language, the designer may generate a localized action name instead of the expected Microsoft_Sentinel_incident, and the playbook won't be picked up. Environment Microsoft Sentinel (Log Analytics workspace) Logic Apps (Consumption plan) Trigger: native Microsoft Sentinel connector, "Incident" trigger (/incident-creation path) Authentication: system-assigned Managed Identity Same subscription, resource group, and region as the Sentinel workspace Problem Two Logic Apps, both triggered by the native Microsoft Sentinel incident trigger, both using a system-assigned managed identity with the Microsoft Sentinel Responder role granted on the workspace. Same subscription, same resource group, same region. Only one of the two appeared in the "Run playbook" dropdown when configuring an Automation Rule in Sentinel. The other was simply absent — no error message, no warning, nothing in the run history to explain it. Investigation The usual suspects were checked and ruled out one by one: API connection health — one azuresentinel connection was indeed in an "Error" state, but it turned out not to be the one referenced by the affected trigger. Managed identity RBAC — the Microsoft Sentinel Responder role was correctly assigned to the Logic App's managed identity on the Sentinel workspace. User's own RBAC/PIM role — the Logic App Contributor role (required for the account configuring the Automation Rule to even see the resource) was active via PIM at the time of testing. Region/subscription/resource group — identical for both Logic Apps. Trigger type — both used the native "Incident in Microsoft Sentinel" trigger added through the visual designer, not a generic HTTP trigger or a deprecated connector. Every documented requirement was met. The playbook was still invisible. Root Cause Comparing the raw JSON (Logic app > Development tools > Code view) revealed the actual difference. Working Logic App (visible in the picker): "triggers": { "Microsoft_Sentinel_incident": { "type": "ApiConnectionWebhook", "inputs": { "body": { "callback_url": "@{listCallbackUrl()}" }, "path": "/incident-creation" } } } Broken Logic App (invisible in the picker): "triggers": { "Incident_dans_Microsoft_Sentinel": { "type": "ApiConnectionWebhook", "inputs": { "body": { "callback_url": "@listCallbackUrl()" }, "path": "/incident-creation" } } } The connector type and the path (/incident-creation) are
Cloudflare Log ingestion in Sentinel with CCF
Hi all, I built a connector to ingest cloudflare firewall logs using CCF. The reason why I had to build this custom one while the official one was available was because official one uses Logpush which is a service that is available only on Enterprise plan, so if you are on pro or business plan you can not use it. Putting it out here in case anyone wants to try. https://amankhan.net/posts/Cloudflare-CCF-Connector/Integrating Proofpoint and Mimecast Email Security with Microsoft Sentinel
Microsoft Sentinel can ingest rich email security telemetry from Proofpoint and Mimecast to power advanced phishing detection. The Proofpoint On Demand (POD) Email Security and Proofpoint Targeted Attack Protection (TAP) connectors pull threat logs (quarantines, spam, phishing attempts) and user click data into Sentinel. Similarly, the Mimecast Secure Email Gateway connector ingests detailed mail flow and targeted-threat logs (attachment/URL scans, impersonation events). These integrations use Azure-hosted ingestion (via Logic Apps or Azure Functions) and the new Codeless Connector framework to call vendor APIs on a schedule. The result is a consolidated dataset in Sentinel’s Log Analytics, enabling correlated alerting and hunting across email, identity, and endpoint signals. Figure: Phishing emails are processed by Mimecast’s gateway and Proofpoint POD/TAP services. Security logs (delivery/quarantine events, malicious attachments/links, user clicks) flow into Microsoft Sentinel. In Sentinel, these mail signals are correlated with identity (Azure AD), endpoint (Defender) and network telemetry for end-to-end phishing detection. Proofpoint POD (Email Protection) Connector The Proofpoint POD connector ingests core email protection logs. It creates two tables, ProofpointPODMailLog_CL and ProofpointPODMessage_CL. These logs include per-message metadata (senders, recipients, subject, message size, timestamps), threat scores (spamScore, phishScore, malwareScore, impostorScore), and attachment details (number of attachments, names, hash values and sandbox verdicts). Quarantine actions are recorded (quarantine folder/rule) and malicious indicators (URL or file hash) and campaign IDs are tagged in the threatsInfoMap field. For example, each ProofpointPODMessage_CL record may carry a sender_s (sender email domain hashed), recipient list, subject, and any detected threat type (Phish/Malware/Spam/Impostor) with associated threat hash or URL. Deployment: Proofpoint POD uses Sentinel’s codeless connector (an Azure Function behind the scenes). You must provide Proofpoint API credentials (Cluster ID and API token) in the connector UI. The connector periodically calls the Proofpoint SIEM API to fetch new log events (typically in 1–2 hour batches). The data lands in the above tables. (Older custom logic-app approaches similarly parse JSON output from the /v2/siem/messages endpoints.) Proofpoint TAP (Targeted Attack Protection) Connector Proofpoint TAP provides user-click and message-delivery events. Its connector creates four tables: ProofPointTAPMessagesDeliveredV2_CL, ProofPointTAPMessagesBlockedV2_CL, ProofPointTAPClicksPermittedV2_CL, and ProofPointTAPClicksBlockedV2_CL. The message tables report emails with detected threats (URL or attachment defense) that were delivered or blocked by TAP. They include the same fields as POD (message GUID, sender, recipients, subject, threat campaign ID, scores, attachments info). The click tables log when users click on URLs: each record has the URL, click timestamp (clickTime), the user’s IP (clickIP), user-agent, the message GUID, and the threat ID/category. These fields allow you to see who clicked which malicious link and when. As the connector description notes, these logs give “visibility into Message and Click events in Microsoft Sentinel” for hunting. Deployment: The TAP connector also uses the codeless framework. You supply a TAP API service principal and secret (proofpoint SIEM API credentials) in the Sentinel content connector. The function app calls TAP’s /v2/siem/clicks/blocked, /permitted, /messages/blocked, and /delivered endpoints. The Proofpoint SIEM API limits queries to 1-hour windows and 7-day history, with no paging (all events in the interval are returned). (A Logic App approach could also be used, as shown in the Tech Community blog: one HTTP GET per event type and a JSON Parse before sending to Log Analytics.) Mimecast Secure Email Gateway Connector The Mimecast connector ingests the Secure Email Gateway (SEG) logs and targeted-threat (TTP) logs. Inbound, outbound and internal mail events from the Mimecast MTA (receipt, processing, delivery stages) are pulled via the API. Typical fields include the unique message ID (aCode), sender, recipient, subject, attachment count/names, and the policy actions or holds (e.g. spam quarantine). For example, the Mimecast “Process” log shows AttCnt, AttNames, and if the message was held (Hld) for review. Delivery logs include the success/failure and TLS details. In addition, Mimecast TTP logs are collected: URL Protect logs (when a user clicks a blocked URL) include the clicked URL (url), category (urlCategory), sender/recipient, and block reason. Impersonation Protect logs capture spoofing detections (e.g. if an internal name is impersonated), with fields like Sender, Recipient, Definition and Action (hold/quarantine). Attachment Protect logs record malicious file detections (filename, hash, threat type). Deployment: Like Proofpoint, Mimecast’s connector uses Azure Functions via the Sentinel content hub. You install the Mimecast solution, open the connector page, then enter Azure app credentials and Mimecast API keys (API Application ID/Key and Access/Secret for the service account). As shown in the deployment guide, you must provide the Azure Subscription, Resource Group, Log Analytics Workspace and the Azure Client (App) ID, Tenant ID and Object ID of the admin performing the setup. On the Mimecast side, you supply the API Base URL (regional), App ID/Secret and user Access/Secret. The connector creates a Function App that polls Mimecast’s SIEM APIs on a cron schedule (default every 30 minutes). You can optionally specify a start date for backfilling up to 7 days of logs. The default tables are MimecastSIEM_CL (for email flow logs) and MimecastDLP_CL (for DLP/TTP events), though custom names can be set. Ingestion Considerations Data Latency: All these connectors are pull-based and typically run on a schedule (often 30–60 minutes). For example, the Proofpoint POD docs note hourly log increments, and Mimecast logs are aggregated every 30 minutes. Expect a delay of up to an hour or more from event occurrence to Sentinel ingestion. Schema Nuances: The APIs often return nested arrays and optional fields. For instance, the Proofpoint blog warns that some JSON fields can be null or vary in type, so the parse schema should account for all possibilities. Similarly, Mimecast logs come in pipe-delimited or JSON format, with values sometimes empty (e.g. no attachments). In KQL, use tostring() or parse_json() on the raw _CL columns, and mv-expand on any multivalue fields (like message parts or threat lists). Table Names: Use the connector’s tables as listed. For Proofpoint: ProofpointPODMailLog_CL and ProofpointPODMessage_CL; for TAP: ProofPointTAPMessagesDeliveredV2_CL, ProofPointTAPMessagesBlockedV2_CL, ProofPointTAPClicksPermittedV2_CL, ProofPointTAPClicksBlockedV2_CL. For Mimecast SEG/TTP: MimecastSIEM_CL (seg logs) and MimecastDLP_CL (TTP logs). API Behavior: The Proofpoint TAP API has no paging. Be aware of timezones (Proofpoint uses UTC) and use the Sentinal ingestion TimeGenerated or event timestamp fields for binning. Detection Engineering and Correlation To detect phishing effectively, we correlate these email logs with identity, endpoint and intel data: Identity (Azure AD): Mail logs contain recipient addresses and (hashed) sender user parts. A common tactic is to correlate SMTP recipients or sender domains with Azure AD user records. For example, join TAP clicks by recipient to the user’s UPN. The Proofpoint logs also include the clicker’s IP (clickIP); we can match that to Azure AD sign-in logs or VPN logs to find which device/location clicked a malicious link. Likewise, anomalous Azure AD sign-ins (impossible travel, MFA failure) after a suspicious email can strengthen the case. Endpoints (Defender): Once a user clicks a bad link or opens a malicious attachment (captured in TAP or Mimecast logs), watch for follow-on behaviors. For instance, use Sentinel’s DeviceSecurityEvents or DeviceProcessEvents to see if that user’s machine launched unusual processes. The threatID or URL hash from email events can be looked up in Defender’s file data. Correlate by username (if available) or IP: if the email log shows a link click from IP X, see if any endpoint alerts or logon events occurred from X around the same time. As the Mimecast integration touts, this enables “correlation across Mimecast events, cloud, endpoint, and network data”. Threat Intelligence: Use Sentinel’s ThreatIntelligenceIndicator tables or Microsoft’s TI feeds to tag known bad URLs/domains in the email logs. For example, join ProofPointTAPClicksBlockedV2_CL on the clicked url against ThreatIntelligenceIndicator (type=URL) to automatically flag hits. Proofpoint’s logs already classify threats (malware/phish) and provide a threatID; one can enrich that with external intel (e.g. check if the hash appears in TI feeds). Mimecast’s URL logs include a urlCategory field, which can be mapped to known malicious categories. Automated playbooks can also pull Intel: e.g. use Sentinel’s TI REST API or Azure Sentinel watchlists containing phishing domains to annotate events. In summary, a robust detection strategy might look like: (1) Identify malicious email events (high phish scores, quarantines, URL clicks). (2) Correlate these events by user with Azure AD logs (did the user log in from a new IP after a phish click?). (3) Correlate with endpoint alerts (Defender found malware on that device). (4) Augment with threat intelligence lookups on URLs and attachments from the email logs. By linking the Proofpoint/Mimecast signals to identity and endpoint events, one can detect the full attack chain from email compromise to endpoint breach. KQL Query Here are representative Kusto queries for common phishing scenarios (adapt table/field names as needed): Malicious URL Click Detection: Identify users who clicked known-malicious URLs. For example, join TAP click logs to TI indicators:This flags any permitted click where the URL matches a known threat indicator. Alternatively, aggregate by domain: let TI = ThreatIntelligenceIndicator | where Active == true and _EntityType == "URL"; ProofPointTAPClicksPermittedV2_CL | where url_s != "" | project ClickTime=TimeGenerated, Recipient=recipient_s, URL=url_s, SenderIP=senderIP_s | join kind=inner TI on $left.URL == TI._Value | project ClickTime, Recipient, URL, Description=TI.Description This flags any permitted click where the URL matches a known threat indicator. Alternatively, aggregate by domain: ProofPointTAPClicksPermittedV2_CL | extend clickedDomain = extract(@"https?://([^/]+)", 1, url_s) | summarize ClickCount=count() by clickedDomain | where clickedDomain has "maliciousdomain.com" or clickedDomain has "phish.example.com" Quarantine Spike (Burst) Detection: Detect sudden spikes in quarantined messages. For example, using POD mail log:This finds hours with an unusually high number of held (quarantined) emails, which may indicate a phishing campaign. You could similarly use ProofPointTAPMessagesBlockedV2_CL. ProofpointPODMailLog_CL | where action_s == "Held" | summarize HeldCount=count() by bin(TimeGenerated, 1h) | order by TimeGenerated desc | where HeldCount > 100 Targeted User Phishing: Find if a specific user received multiple malicious emails. E.g., for user email address removed for privacy reasons:This lists recent phish attempts targeting Username. You might also join with TAP click logs to see if she clicked anything. ProofpointPODMessage_CL | where recipient has "email address removed for privacy reasons" | where array_length(threatsInfoMap) > 0 and threatsInfoMap_classification_s == "Phish" | project TimeGenerated, sender_s, subject_s, threat=threatsInfoMap_threat_s Campaign-Level Analysis: Group emails by Proofpoint campaign ID to see scope of each campaign:This shows each campaign ID with how many unique recipients were hit and one example subject. Combining TAP and POD tables on GUID_s or QID_s can further link click events back to the originating message/campaign. ProofpointPODMessage_CL | mv-expand threatsInfoMap | summarize Recipients=make_set(recipient), Count=dcount(recipient) by CampaignID=threatsInfoMap_campaignId_s | project CampaignID, RecipientCount=Count, Recipients, SampleSubject=any(subject_s) Each query can be refined (for instance, filtering only within a recent time window) and embedded in Sentinel Analytics rules or hunting. The key is using the connectors’ fields – URLs, sender/recipient addresses, campaign IDs – to pivot between email data and other security signals.Your Sentinel AMA Logs & Queries Are Public by Default - AMPLS Architectures to Fix That
When you deploy Microsoft Sentinel, security log ingestion travels over public Azure Data Collection Endpoints by default. The connection is encrypted, and the data arrives correctly — but the endpoint is publicly reachable, and so is the workspace itself, queryable from any browser on any network. For many organisations, that trade-off is fine. For others — regulated industries, healthcare, financial services, critical infrastructure — it is the exact problem they need to solve. Azure Monitor Private Link Scope (AMPLS) is how you solve it. What AMPLS Actually Does AMPLS is a single Azure resource that wraps your monitoring pipeline and controls two settings: Where logs are allowed to go (ingestion mode: Open or PrivateOnly) Where analysts are allowed to query from (query mode: Open or PrivateOnly) Change those two settings and you fundamentally change the security posture — not as a policy recommendation, but as a hard platform enforcement. Set ingestion to PrivateOnly and the public endpoint stops working. It does not fall back gracefully. It returns an error. That is the point. It is not a firewall rule someone can bypass or a policy someone can override. Control is baked in at the infrastructure level. Three Patterns — One Spectrum There is no universally correct answer. The right architecture depends on your organisation's risk appetite, existing network infrastructure, and how much operational complexity your team can realistically manage. These three patterns cover the full range: Architecture 1 — Open / Public (Basic) No AMPLS. Logs travel to public Data Collection Endpoints over the internet. The workspace is open to queries from anywhere. This is the default — operational in minutes with zero network setup. Cloud service connectors (Microsoft 365, Defender, third-party) work immediately because they are server-side/API/Graph pulls and are unaffected by AMPLS. Azure Monitor Agents and Azure Arc agents handle ingestion from cloud or on-prem machines via public network. Simplicity: 9/10 | Security: 6/10 Good for: Dev environments, teams getting started, low-sensitivity workloads Architecture 2 — Hybrid: Private Ingestion, Open Queries (Recommended for most) AMPLS is in place. Ingestion is locked to PrivateOnly — logs from virtual machines travel through a Private Endpoint inside your own network, never touching a public route. On-premises or hybrid machines connect through Azure Arc over VPN or a dedicated circuit and feed into the same private pipeline. Query access stays open, so analysts can work from anywhere without needing a VPN/Jumpbox to reach the Sentinel portal — the investigation workflow stays flexible, but the log ingestion path is fully ring-fenced. You can also split ingestion mode per DCE if you need some sources public and some private. This is the architecture most organisations land on as their steady state. Simplicity: 6/10 | Security: 8/10 Good for: Organisations with mixed cloud and on-premises estates that need private ingestion without restricting analyst access Architecture 3 — Fully Private (Maximum Control) Infrastructure is essentially identical to Architecture 2 — AMPLS, Private Endpoints, Private DNS zones, VPN or dedicated circuit, Azure Arc for on-premises machines. The single difference: query mode is also set to PrivateOnly. Analysts can only reach Sentinel from inside the private network. VPN or Jumpbox required to access the portal. Both the pipe that carries logs in and the channel analysts use to read them are fully contained within the defined boundary. This is the right choice when your organisation needs to demonstrate — not just claim — that security data never moves outside a defined network perimeter. Simplicity: 2/10 | Security: 10/10 Good for: Organisations with strict data boundary requirements (regulated industries, audit, compliance mandates) Quick Reference — Which Pattern Fits? Scenario Architecture Getting started / low-sensitivity workloads Arch 1 — No network setup, public endpoints accepted Private log ingestion, analysts work anywhere Arch 2 — AMPLS PrivateOnly ingestion, query mode open Both ingestion and queries must be fully private Arch 3 — Same as Arch 2 + query mode set to PrivateOnly One thing all three share: Microsoft 365, Entra ID, and Defender connectors work in every pattern — they are server-side pulls by Sentinel and are not affected by your network posture. Please feel free to reach out if you have any questions regarding the information provided.Building Microsoft Sentinel Connectors in Minutes with the Sentinel Connector Builder Agent
Overview We previously announced the public preview of the Microsoft Sentinel connector builder agent via VS code extension, that helps developers build Microsoft Sentinel codeless connectors faster with low-code and AI-assisted prompts. This post walks through a hands-on lab using a mock Network Log API to demonstrate how the Sentinel connector builder agent simplifies building Codeless Connector Framework (CCF) pull connectors. Instead of manually creating ingestion infrastructure and configuration files, you’ll use a guided, conversational workflow in VS Code to generate connector artifacts, test them against a live API, and deploy them into Microsoft Sentinel. The lab focuses on the end-to-end experience ranging from API setup to validated connector deployment so you can see how quickly a working integration can be produced. For additional guidance beyond this lab, refer to our MS Learn documentation. The Lab Environment This lab is built around a mock Network Log API hosted as an Azure Function App. The purpose of the lab environment is to give us a live API that we can use to build, validate, and test the Sentinel CCF connector builder agent against end to end. The API exposes 50 synthetic network activity records that look and behave like a real product data source, including web traffic, DNS requests, blocked remote access attempts, malware command-and-control blocks, VPN activity, and other common network events. That makes it a useful stand-in for the type of telemetry many teams want to onboard into Microsoft Sentinel. The API is intentionally shaped like the kind of source a customer might expose for telemetry retrieval. It uses API key authentication through the X-API-Key header, returns paginated results through a nextLink model, and provides a predictable response structure that the builder agent can map into a pull connector configuration. The repo contains everything needed for the walkthrough. There is an ARM template to deploy the Function App, reference documentation for the API, and a sample connector package showing the generated polling config, table schema, DCR, and connector definition. The end goal of the lab is straightforward: use the builder agent to generate a CCF pull connector that ingests this API into the custom NetworkLogAPIGetNetworkLogs_CL table in Sentinel. Prerequisites Before starting, make sure you have the following: Azure subscription -- with Contributor access on a resource group (for deploying the Function App) and Microsoft Sentinel Contributor access on a Sentinel-enabled workspace (for deploying the connector) Microsoft Sentinel workspace -- an existing Log Analytics workspace with Sentinel enabled. See Onboard Microsoft Sentinel to a Log Analytics workspace for more information. Azure CLI -- See How to install the Azure CLI for more information. VS Code with the Microsoft Sentinel for Visual Studio Code extension installed. GitHub Copilot -- with access to premium models. The connector builder agent requires Claude Sonnet 4.5 or 4.6, which uses Copilot premium model credits. Lab Repository -- Once the aforementioned prerequisites are met, you can access the lab repository here: Azure-Sentinel/Tools/CCF-Connector-Builder-Agent-Accelerator at master · Azure/Azure-Sentinel Deploying the Mock API The full CLI commands for this section are available in the repo. For a simpler option, you can use GitHub Copilot to handle the deployment. Enter this prompt: Follow the deployment instructions in Sentinel-CCF-Pull-Connector-Builder-Agent-Accelerator/agent-instructions.md. Let’s deploy the Network Log API and build a CCF pull connector. At a high level, the setup is four steps: clone the repo, create a resource group, ensure you have a Sentinel-enabled workspace, and deploy the Function App using the included ARM template. The template takes two parameters: an ApiKey of your choice (the secret the CCF connector will use to authenticate) and your Log Analytics workspace resource ID for Application Insights. Deployment takes about two to three minutes and outputs the FunctionAppName and endpoint URLs you will need later. Once deployed, verify the API is live: curl -s -H "X-API-Key: <your-api-key>" \ "https://<functionappname>.azurewebsites.net/api/GetNetworkLogs?page=1&pageSize=3" </functionappname></your-api-key> You should see a response like this: The API also exposes an /api/RefreshData endpoint that regenerates the 50 sample records with fresh timestamps. This is useful later in the walkthrough when you want to produce new events and trigger an immediate ingestion cycle without waiting for the next polling interval: curl -s -X POST -H "X-API-Key: <your-api-key>" \ "https://<functionappname>.azurewebsites.net/api/RefreshData" </functionappname></your-api-key> Building the Connector with the Sentinel Connector Builder Agent With the Microsoft Sentinel extension installed and GitHub Copilot running in agent mode, open a Copilot chat and enter a single prompt pointing at the API documentation file: That is the entire invocation. The agent takes it from there. It works through a structured seven-step sequence: preparation, polling config, table schema, DCR, connector definition, package validation, and summary. The agent produces four files in a sentinel-connectors/NetworkLogAPI_CCF/ output folder: NetworkLogAPI_PollingConfig.json – This is the API poller configuration. The agent reads the documentation and correctly identifies the GET /api/GetNetworkLogs endpoint, configures API Key authentication via the X-API-Key header, sets up NextPageUrl pagination using $.metadata.nextLink with a $.metadata.hasNextPage stop condition, and wires up the since query parameter for incremental delta pulls using the timestamp field. The RefreshData endpoint is correctly excluded, which the agent recognizes as a maintenance operation, not a security data stream. NetworkLogAPI_Table.json – This is the custom Log Analytics table schema for NetworkLogAPIGetNetworkLogs_CL . All 20 fields from the API response are mapped to the correct column types, with timestamp promoted to TimeGenerated as the standard Sentinel time column. NetworkLogAPI_DCR.json – This is the Data Collection Rule. This defines the stream declaration, the workspace destination, and the KQL transform that maps the raw snake_case API fields ( sourceIp , destinationIp , threatIndicator , etc.) to their PascalCase table columns. NetworkLogAPI_ConnectorDefinition.json – This is the connector UI configuration. This drives what the connector page looks like in Microsoft Sentinel: the title, description, prerequisite instructions, the BaseUrl and ApiKey input fields, sample KQL queries, and the connectivity status logic. The only point where the agent paused for input was to propose a connector description and ask for confirmation before writing it to the file. Everything else such as endpoint selection, auth type, pagination pattern, schema mapping, KQL transform, cross-file consistency was selected autonomously. To put that in perspective: without the agent, a developer building this connector from scratch would need to manually author four JSON files, understand the CCF schema for polling configs, DCRs, and connector definitions, write the KQL transform by hand, and validate that every cross-file reference lines up correctly. The agent compresses that work, typically hours of reading documentation, trial-and-error, and portal debugging, into a single prompt. Testing the Connector Before deploying anything to a Sentinel workspace, the Microsoft Sentinel connector builder agent lets you validate the generated polling config against the live API directly from your editor. Right-click the sentinel-connectors/NetworkLogAPI_CCF folder, select Microsoft Sentinel → Test Connector (Preview), and a Configuration Variables panel opens asking for the two template variables from the polling config: BaseUrl and apiKey . For other API patterns, there may be additional and different inputs. For example, apiKey input could be swapped with clientID and secret if the API supports OAUTH. Enter the Function App base URL and your API key, and the test runner connects immediately. The panel shows a live polling session. Poll #1 returns HTTP 200 with 50 events, and a countdown timer shows when the next poll will fire. Switching to the Events tab displays the ingested records in a tabular view with columns for timestamp , severity , action , bytesIn , bytesOut , category , and the rest of the mapped fields fresh from the API. Additionally, there are tabs for Headers, Payload, and Response, which can be useful for verifying that your pollerconfig.json configuration provides the expected request to your api with a working response. Data Extracted: The Test Connector feature can be used to visualize the response data in a table format to verify that data will land in a Sentinel table based on your configuration. Request from Poller: The Test Connector feature can be used to validate the request and response headers that will go out to the API based on the generated poller configuration. Request Response: The Test Connector feature shows you the live response from the API with respect to the request going to the API based on the poller configuration. This is a meaningful pre-flight check. It confirms that auth is working, the $.data events path resolves correctly, pagination is functional, and the polling interval fires as configured all before a single file is deployed to Azure. The most common connector configuration issues (wrong base URL, incorrect header name, mismatched JSON path) surface here in seconds rather than after a failed deployment and a 20-minute wait for Sentinel to attempt its first ingestion cycle. It is also the fastest way to troubleshoot if something goes wrong after deployment, far quicker than pushing changes to Azure and waiting for the connector to poll again. Deploying and Enabling the Connector With the connector tested and passing, deployment is the same right-click menu: right-click the sentinel-connectors/NetworkLogAPI_CCF folder, select Microsoft Sentinel → Deploy Connector (Preview). If you are not already signed in to Azure, the extension will prompt you to authenticate. The agent will also provide a clickbox in the chat window to invoke a connector deployment. Right Click Deploy Connector: UI Prompt Based Deploy Method: Once signed in, a workspace picker lists all available Log Analytics workspaces across your subscriptions. Select the one with Sentinel enabled and click Deploy. The extension deploys all four files to the workspace in the correct order: table schema first, then DCR, polling config, and connector definition. Once deployed, navigate to your Sentinel workspace via https://security.microsoft.com, go to Data Connectors, and find the Network Log API connector. The connector page shows the description, prerequisite notes, and the two credential fields generated by the agent: API Base URL and API Key. Enter your Function App base URL and API key and click Connect. The status updates to show the connector is connected and the deployment succeeded. Note: Data will appear in the workspace within 5 to 30 minutes depending on the polling interval. Run this query in Log Analytics to confirm ingestion. Note that the agent derives the table name from the vendor name and endpoint, so yours may differ slightly from the example below. Check the agent's summary output or the NetworkLogAPI_Table.json file for the exact name: NetworkLogAPIGetNetworkLogs_CL | sort by TimeGenerated desc | take 10 If you want to generate a fresh batch of events immediately rather than waiting for the next polling cycle, use the RefreshData endpoint to reset the sample records with new timestamps: curl -s -X POST -H "X-API-Key: " \ "https://.azurewebsites.net/api/RefreshData" Next Steps If you want to go further: Try it with your own API. The lab repo includes documentation on adapting the polling config, schema, and KQL transform to a real data source. Review the CCF connector schema documentation to understand the full range of supported configurations: pagination patterns, auth types, incremental pull strategies, and delta filter expressions. Explore the Microsoft Sentinel content hub to see how published connectors are structured and what the certification requirements look like for production submissions. Conclusion Following these steps, you saw how a working Sentinel connector can be generated, tested, and deployed in minutes rather than requiring days of manual configuration and infrastructure setup. If you are an ISV building a Sentinel integration and want hands-on support, Microsoft’s App Assure program is available to help. We partner with ISVs on connector development, validation, and deployment and provide guidance through implementation, testing, and readiness for production. You can get started by reaching out through our intake form. See our other Sentinel connector feature’s hands-on labs Building a CCF Nested API Pull Connector: A Technical Lab WalkthroughSentinel Foundry - MCP Server (Github Community Release)
I’ve been cooking something that a lot of people in SOC have been struggling with — especially on the engineering side of Microsoft Sentinel. Thanks to the Microsoft Security team for shaping the capabilities of Sentinel even better with Sentinel Data Lake & Modern SecOps. Today’s the day I can finally share it. Note: This is not an official Microsoft product, but it is designed to make the Sentinel Build even better (complement) with much more intelligence. 🚀 Sentinel Foundry is now in public preview with 43 tools. (Sentinel Foundry - MCP Server) It’s an MCP server built to act like the brain of a strong Sentinel engineer — helping make building, improving, and operating Sentinel far more practical, faster, and honestly more enjoyable. For a lot of teams, the challenge is not understanding what Sentinel can do. The hard part is the engineering work around it: -> Deciding what data should actually be ingested -> Building a clean, scalable Sentinel foundation -> Writing useful detections instead of noisy ones -> Balancing security value with cost -> Turning ideas into deployable engineering outputs That is exactly why I built Sentinel Foundry to help communities grow stronger. It helps with the real engineering tasks behind Sentinel — from architecture thinking to detection design, deployment planning, ingestion strategy, automation ideas, and many of the workflows outlined in the GitHub project. How does it work? Here’s one of the flagship prompts I ran with it: “Give me a complete security posture report for our workspace. Score each pillar and tell me what to prioritise.” And within seconds, it produced a structured engineering blueprint that would normally take a lot longer to pull together manually. You can see the example prompts here in what it can do: https://github.com/prabhukiranveesam/Sentinel-Foundry#what-can-it-do I want building Sentinel to feel less like repetitive engineering overhead — and more like real security engineering that is fast, creative, and enjoyable. If you work with Sentinel as a SOC L2 analyst, engineer, detection engineer, consultant, or architect, I’d genuinely love for you to try it and tell me what you think. 🔗 Public Preview: https://github.com/prabhukiranveesam/Sentinel-Foundry This is just the start of an AI era — and I’m excited to keep shaping it with more powerful features over the coming days. This is very easy to set up and will be available to all of you at no cost during this month as part of the public preview, and your feedback is extremely valuable to shape this as a powerful solution.
Hunting AI Agent Configuration Drift with Microsoft Sentinel
Four KQL patterns for detecting instruction changes, new MCP servers, ownership changes, and organization-wide sharing I recently authored and contributed four new Microsoft Sentinel hunting queries for detecting security-relevant configuration drift in AI agents. They have been reviewed, approved, and merged into Microsoft's public Azure-Sentinel repository. I built the queries around four changes that can materially affect an agent's behavior, access, or exposure: instructions being modified, MCP servers being connected, owners being added, and sharing being expanded to the entire organization. Each modification may be legitimate, but each deserves enough context for a security team to verify that it was expected and authorized. For a security operations team, the difficult question is often not what does this agent look like now? It is what changed since the last known state? Microsoft Sentinel's AgentsInfo table provides inventory-style snapshots of AI agents and their associated configuration. That makes it useful for more than posture reporting. By comparing a recent snapshot with an earlier baseline, we can hunt for configuration drift that deserves investigation. This post walks through four practical hunting scenarios: Instructions changed on a previously published agent A newly observed MCP server on an existing agent An owner added to an MCP-enabled agent Sharing expanded from a restricted scope to the entire organization The complete hunting queries are available in Microsoft's public https://github.com/Azure/Azure-Sentinel/tree/master/Hunting%20Queries/AI%20Agents. The focus here is the detection design behind them, the KQL patterns they share, and the investigation questions they help answer. What I contributed I wrote the four standalone hunting queries discussed in this article and submitted them to Azure/Azure-Sentinel in https://github.com/Azure/Azure-Sentinel/pull/14702: AI Agents - Instructions changed on previously published agent AI Agents - Newly observed MCP server on existing agent AI Agents - Owner added to MCP-enabled agent AI Agents - Sharing expanded to organization-wide The contribution went through several rounds of technical review. Across six commits, I aligned the queries with the unified AgentsInfo schema, added schema-tolerant IdentityInfo enrichment, improved entity mappings, bounded the identity lookback, expanded all owner values, and kept the ATT&CK mappings limited to scenarios where a precise technique could be defended. Repository collaborator v-atulyadav approved the final revision, and the four queries were merged into master on July 20, 2026. This article explains the detection logic and engineering decisions behind that contribution rather than simply reproducing the final YAML files. Why current-state queries are not enough A current-state query can answer questions such as: Which agents are published? Which agents have MCP servers configured? Which agents are shared with the organization? Who owns a particular agent? Those are important posture questions, but they do not tell us whether the state is new. An agent with an MCP server might have been reviewed and approved months ago. The same MCP server appearing for the first time today is a different security signal. Configuration-drift hunting adds the missing time dimension. Instead of treating a risky-looking property as an event, it compares two states of the same agent and reports only meaningful transitions. The common detection pattern I used the same basic time model across all four hunts: let lookback = 14d; let recent = 2d; The latest snapshot observed during the last two days becomes the current state. The latest snapshot from the preceding portion of the 14-day lookback becomes the baseline. Conceptually, the comparison looks like this: let CurrentState = AgentsInfo | where Timestamp > ago(recent) | summarize arg_max(Timestamp, *) by AgentId | where LifecycleStatus != "Deleted"; let BaselineState = AgentsInfo | where Timestamp between (ago(lookback) .. ago(recent)) | where LifecycleStatus != "Deleted" | summarize arg_max(Timestamp, *) by AgentId; CurrentState | join kind=inner BaselineState on AgentId Several details matter here: arg_max(Timestamp, *) by AgentId selects the latest available state for each agent in the relevant time range. The inner join restricts results to agents that exist in both periods. A newly created agent is therefore not automatically treated as configuration drift on an existing agent. Deleted lifecycle snapshots are excluded so that a deletion record does not become the effective baseline or current configuration. The two-day current window is operationally significant. To retain coverage, these hunts should run within two days of a change. The 14-day and two-day values are practical defaults, not universal constants. Environments with different ingestion cadence or retention requirements can adjust them, but the current and baseline windows must remain non-overlapping. Scenario 1: Instructions changed on a published agent An agent's instructions define its default behavior, persona, and operating boundaries. Changing them can be part of normal development, but it can also weaken restrictions, redirect the agent's behavior, or modify how it uses connected capabilities. The first hunt compares the current and previous instruction values only when the agent was published in both snapshots: CurrentState | join kind=inner BaselineState on AgentId | where CurrentInstructions != PreviousInstructions | extend PreviousInstructionsHash = hash_sha256(PreviousInstructions), CurrentInstructionsHash = hash_sha256(CurrentInstructions), InstructionsLengthDelta = strlen(CurrentInstructions) - strlen(PreviousInstructions) I deliberately chose to expose hashes and a length delta rather than returning both instruction bodies in plaintext. This confirms that a change occurred without unnecessarily spreading potentially sensitive prompts through query results, exports, or screenshots. Useful investigation questions include: Was the change associated with an approved development or release process? Did the agent remain published while the instructions changed? Were guardrails, declared tools, permissions, or sharing settings modified around the same time? Do audit records identify an expected actor and change path? The query maps well to an integrity-focused investigation. Its MITRE ATT&CK mapping is T1565.001 (Stored Data Manipulation), but the result is still a hunting lead rather than proof of malicious manipulation. https://github.com/Azure/Azure-Sentinel/blob/master/Hunting%20Queries/AI%20Agents/AgentsInfoInstructionsChangedOnPublishedAgent.yaml Scenario 2: A newly observed MCP server Model Context Protocol servers can extend an agent with external tools, data sources, or actions. From a defender's perspective, the important transition is not simply that an MCP server exists. It is that a server name appears in the current configuration but was absent from the baseline. The query expands the dynamic McpServers array and builds a set of server names for each agent: let CurrentMcp = CurrentRaw | mv-expand Mcp = McpServers | extend McpName = tostring(Mcp.name) | where isnotempty(McpName) | summarize CurrentMcpServers = make_set(McpName) by AgentI It performs the same normalization for the baseline, then calculates the difference: | extend AddedMcpServers = set_difference(CurrentMcpServers, BaselineMcpServers) | where array_length(AddedMcpServers) > 0 Using set_difference() avoids raising a result merely because the order of array elements changed. The hunt reports only MCP server names present in the current set and absent from the previous set. An analyst should validate more than the displayed name: Is the MCP integration part of the approved inventory? What endpoint, authentication method, and permissions are associated with it? Which tools or data can the server expose to the agent? Was the integration introduced through an expected deployment path? Did ownership, instructions, or sharing change in the same period? I did not assign an ATT&CK technique to this query. Adding an MCP server does not, by itself, prove command execution, persistence, or a specific attacker behavior. Avoiding an overly broad mapping keeps the signal honest. https://github.com/Azure/Azure-Sentinel/blob/master/Hunting%20Queries/AI%20Agents/AgentsInfoNewlyObservedMcpServer.yaml Scenario 3: An owner added to an MCP-enabled agent Ownership is a control-plane relationship. A newly added owner may be able to modify an agent's configuration, instructions, integrations, or publication state. The risk becomes more interesting when the agent already has MCP servers configured. The hunt first limits the current state to MCP-enabled agents: | where array_length(coalesce(McpServers, dynamic([]))) > 0 | project AgentId, Timestamp, Name, Platform, CreatedDateTime, CurrentOwners = coalesce(Owners, dynamic([])), McpServers It then compares the owner arrays as sets: | extend AddedOwners = set_difference(CurrentOwners, PreviousOwners) | where array_length(AddedOwners) > 0 | mv-expand AddedOwnerId = AddedOwners to typeof(string) Expanding AddedOwners produces one row per newly observed owner. This is more useful than returning one opaque dynamic array because every added identity can be enriched, mapped, and investigated independently. I kept the raw object identifier in the result even when identity enrichment fails: | extend AddedOwnerUpn = AccountUpn, UnresolvedAddedOwnerId = iff(isempty(AccountUpn), AddedOwnerId, "") That fallback matters. A missing UPN should not hide the underlying ownership change. Investigation should establish: Is the added owner an expected person, service identity, or administrative group? Does the identity's role and business function justify control of this agent? Was the owner added before other configuration changes? Does the identity appear in related sign-in, audit, or privileged-access activity? Should ownership be removed while the change is reviewed? This query maps to T1098 (Account Manipulation) under Persistence and Privilege Escalation. As with the instruction-change hunt, the mapping frames an investigation hypothesis; it does not label every ownership change as malicious. https://github.com/Azure/Azure-Sentinel/blob/master/Hunting%20Queries/AI%20Agents/AgentsInfoOwnerAddedToMcpAgent.yaml Scenario 4: Sharing expanded to the entire organization An agent can move from a limited audience to organization-wide availability without changing its underlying tools or instructions. That transition can materially increase exposure, especially when the agent has MCP integrations or declared tools. The hunt treats "*" in SharedWith as the organization-wide state. The current snapshot must contain it, while the baseline must not: // Current state | where set_has_element(coalesce(SharedWith, dynamic([])), "*") // Baseline state | where not(set_has_element(coalesce(SharedWith, dynamic([])), "*")) The result also counts MCP servers and declared tools: | extend McpServerCount = array_length(coalesce(McpServers, dynamic([]))), DeclaredToolCount = array_length(coalesce(DeclaredTools, dynamic([]))) | extend HasElevatedCapabilities = McpServerCount > 0 or DeclaredToolCount > 0 | sort by HasElevatedCapabilities desc, Timestamp desc I use HasElevatedCapabilities as a prioritization field, not a verdict. It brings agents with connected capabilities to the top of the result set so analysts can review the potentially larger blast radius first. Questions for triage include: Was organization-wide publication explicitly approved? Is the agent intended for every user, or was a group-based scope expected? What data sources, tools, and MCP servers can organization-wide users reach through it? Do the instructions contain assumptions that were safe only for a restricted audience? Were access reviews or user-acceptance tests completed before the expansion? No ATT&CK mapping is assigned because a broader sharing scope is a security-relevant exposure change, but not a sufficiently precise adversary technique on its own. https://github.com/Azure/Azure-Sentinel/blob/master/Hunting%20Queries/AI%20Agents/AgentsInfoSharingExpandedToOrgWide.yaml Resolving owners without making the hunt schema-fragile The Owners field contains identifiers. Human-readable identity context makes results easier to triage, and entity mappings make those identities more useful in Sentinel investigations. I built a small, materialized IdentityInfo lookup that is shared by the four hunts: let IdentityIdtoUPN = materialize( IdentityInfo | extend ResolvedAccountUpn = tostring( column_ifexists("AccountUpn", column_ifexists("AccountUPN", ""))), IdentityTimestamp = todatetime( column_ifexists("Timestamp", column_ifexists("TimeGenerated", datetime(null)))) | where IdentityTimestamp >= ago(lookback) | where isnotempty(AccountObjectId) and isnotempty(ResolvedAccountUpn) | summarize arg_max(IdentityTimestamp, ResolvedAccountUpn) by AccountObjectId | project AccountObjectId = tostring(AccountObjectId), AccountUpn = ResolvedAccountUpn); There are three design choices worth noting: column_ifexists() accommodates observed IdentityInfo naming variants without maintaining separate query versions. The lookup is bounded by the same lookback period instead of scanning unbounded identity history. arg_max() keeps the latest usable identity record for each object ID. After enrichment, the queries map the account using the UPN components and the Entra object ID: entityMappings: - entityType: Account fieldMappings: - identifier: Name columnName: OwnerAccountName - identifier: UPNSuffix columnName: OwnerAccountUPNSuffix - identifier: AadUserId columnName: OwnerId The strong AadUserId identifier remains valuable even when display information changes. Microsoft Sentinel can use mapped entities in bookmarks and investigation experiences, so mapping the changed owner is more than cosmetic enrichment. Turning a result into an investigation These queries intentionally stop at the configuration transition. AgentsInfo tells us that two snapshots differ; it does not necessarily tell us who performed the change, through which interface, or whether the action was authorized. A practical investigation workflow is: Confirm that the two snapshots represent the expected agent and time period. Review the exact changed property and the agent's current capabilities. Identify the owner or newly added owner through IdentityInfo and Entra ID context. Correlate the transition with the relevant audit source for actor attribution. Check for related changes to permissions, tools, data sources, publication state, and sharing. Validate the change against an approved request, release, or ownership process. Restrict, unpublish, or revert the agent if the exposure cannot be justified. Expected changes can still be useful findings. Repeated legitimate results may reveal that a deployment process lacks a stable change window, that ownership is managed through noisy automation, or that the hunt's timing needs to be aligned with release activity. Tuning the hunts for your environment Before operational use, consider the following adjustments: Run cadence: Execute within the two-day current window. A daily cadence provides overlap without blending current and baseline periods. Lookback: Increase the 14-day lookback only if snapshot history and query cost support it. A longer lookback does not compensate for missing the current window. Known change windows: Add watchlists or environment-specific suppression logic for well-controlled automated deployments, while retaining enough context to audit the change. Agent scope: Filter by platform, business unit, agent naming convention, or owner if different teams require separate triage queues. Risk prioritization: Raise agents with sensitive declared data sources, powerful tools, privileged owners, or broad availability to the top of the result set. Audit correlation: Keep attribution logic separate unless the audit source and join keys are stable in your environment. This makes the configuration-drift hunt reusable while allowing each organization to attach its own control-plane evidence. Test with representative snapshots before treating any hunt as an operational control. In particular, validate array shapes for Owners, McpServers, and SharedWith, confirm the identity fields present in your workspace, and exercise both changed and unchanged states. Using the queries The four YAML definitions have been merged into the Hunting Queries/AI Agents folder of Microsoft's Azure-Sentinel repository. Each file contains the complete KQL, description, entity mappings, and ATT&CK mappings where a precise technique applies. https://github.com/Azure/Azure-Sentinel/blob/master/Hunting%20Queries/AI%20Agents/AgentsInfoInstructionsChangedOnPublishedAgent.yaml https://github.com/Azure/Azure-Sentinel/blob/master/Hunting%20Queries/AI%20Agents/AgentsInfoNewlyObservedMcpServer.yaml https://github.com/Azure/Azure-Sentinel/blob/master/Hunting%20Queries/AI%20Agents/AgentsInfoOwnerAddedToMcpAgent.yaml https://github.com/Azure/Azure-Sentinel/blob/master/Hunting%20Queries/AI%20Agents/AgentsInfoSharingExpandedToOrgWide.yaml The broader pattern is reusable beyond these four scenarios: select a stable current snapshot, select a non-overlapping baseline, normalize dynamic properties into comparable sets, calculate the transition, and enrich only after the drift has been identified. That keeps the core detection explainable and gives the analyst the before-and-after context needed for a defensible investigation. References https://learn.microsoft.com/en-us/azure/azure-monitor/reference/tables/agentsinfo https://learn.microsoft.com/en-us/azure/azure-monitor/reference/queries/agentsinfo https://learn.microsoft.com/en-us/azure/azure-monitor/reference/tables/identityinfo https://learn.microsoft.com/en-us/azure/sentinel/entities-reference https://github.com/Azure/Azure-Sentinel/pull/14702 I authored the four hunting queries discussed in this article and contributed them to Microsoft's Azure-Sentinel repository as https://github.com/Azure/Azure-Sentinel/pull/14702. The complete implementations and review history are publicly available through the links above.
Looking for a simple deployment guide
MS Learn is a great starting point, but it just doesn't seem to cover the steps needed to get up and running safely. I have concerns about adding or setting something that suddenly creates a vulnerability or exposure. Where is the installation guide that installs and configures the solution then tells you, "You are now protected". Do I really want to set my own policies? Why aren't the default set of rules good enough, safe enough. I can't have a solution that is so complicated I need to hire a team to manage it 24 hours a day. I am okay investigating an alert and helping a user solve a pop-up question. Why is every major corporation around the world required to re-invent the same or similar policies the company next door is creating to make this tool work? I want to onboard all of our Intune devices and monitor anything that CAN'T be stopped by default security measures. Just the fact that Sentinel appears to be changing as an embedded tool within Defender gives me hope that this will be getting closer to a more manageable tool. But that still seems a way off. I am ready to do the reading and research to get this set up but I am hoping for a guide that is specific enough to achieve a final result. Thank for understanding my challenges here.
