Answer Area Task

User

Enable Microsoft Defender for Servers on virtual mac hines:

User1

Review security recommendations and enable server vulnerability scans:

User1

Query successful

This is a role-based access control (RBAC) question using the principle of least privilege.

The table of users and roles is:

Name

Role

User1

Security administrator

User2

Security reader

User3

Contributor

Export to Sheets

Action: This is a management/configuration task at the subscription level, often related to enabling Defender plans and installing extensions on VMs.

Required Permissions: The ability to modify security policies and settings in Microsoft Defender for Cloud and the permission to install extensions on VMs.

The Security Administrato r role is explicitly designed for this, granting permissions to manage the security features, policies, and program enrollment (like enabling Defender plans).

The Contributor role can also perform this by installing the necessary agents and extensions, but it grants broad access to manage all resources, violating the principle of least privilege for a purely security-focused task.

Least Privilege User: User1 (Security administrator)

A ction: This task has two parts:

Review security recommendations: This is a read-only action. The Security Reader role is sufficient.

Enable server vulnerability scans: This means configuring a security feature (Vulnerability Assessment), which requires wri te access to the security configuration or the resource itself.

The Security Reader role cannot perform the " enable " action.

The Security Administrator role has the permissions required to modify security policy and configuration to enable scanning features.

The Contributor role can also do this, but again, the Security Administrator role is the least privileged for a security-specific configuration change.

Least Privilege User: Since the entire task includes an " enable " (write) action, we must use the role that can perform both read and write security actions. User1 (Security administrator) is the appropriate choice.

Task Analysis and Role Mapping 1. Enable Microsoft Defender for Servers on virtual machines. 2. Review security recommendations and enable server vulnerability scans.

Answer Area Task

User

Enable Microsoft Defender for Servers on virtual machines:

User1

Review security recommendations and enable server vulnerability scans:

User1

SecurityEvent

| where EventID == 4624

| summarize arg_max(TimeGenerated, *) by Account

Comprehensive and Detailed Explanation with all Microsoft Security Operations (SecOps) documents: =

This query is designed for Microsoft Sentinel (Log Analytics) to identify the latest successful logon (Event ID 4624) for each account. Event ID 4624 in the SecurityEvent table indicates a successful logon in Windows security logs.

Let’s break down the logic step-by-step:

| where EventID == 4624

This line filters the SecurityEvent table to include only records that correspond to successful logon events.

Filtering first ensures that only relevant events are processed by the summarization step, improving performance and accuracy.

| summarize arg_max(TimeGenerated, *) by Account

The arg_max() aggregation function retrieves the record that has the maximum value of TimeGenerated for each Account.

The * symbol ensures that all columns from that most recent record are returned (not just TimeGenerated and Account).

In this context, this means we get the most recent 4624 logon event per user account.

Why this order matters:

If arg_max() is placed before the where clause, the summarization would occur over all events first, not just 4624, producing incorrect results.

Therefore, the correct logical order is to filter first (where EventID == 4624), and then summarize (arg_max(TimeGenerated, *) by Account).

Alternative incorrect options explained:

summarize make_list(Account) or make_set(Account) by EventID → These aggregate account names for each EventID, not the most recent event per account.

Placing where EventID == 4624 after summarize → Filters after aggregation, which won’t return correct results per account.

When you deploy the ASIM unifying parser for DNS , you should query the normalized schema via the function imDns() (ASIM convention: im < Domain > ). To maximize performance , pass filtering arguments directly to the parser so the function prunes data early at source, ra ther than piping a large dataset into subsequent where filters. ASIM DNS supports parameters such as starttime , endtime , and responsecodename (for example, NXDOMAIN ). Therefore, using imDns(starttime=ago(1d), responsecodename= ' NXDOMAIN ' ) limits ingestion t o the last 24 hours and only events whose ResponseCodeName equals NXDOMAIN .

Once normalized rows are returned, use summarize with bin() to aggregate efficiently. The ASIM DNS schema exposes the client address as SrcIpAddr ; grouping by this field and bin(TimeGenerated, 15m) produces per–source-IP counts in 15-minute buckets. The final query:

imDns(starttime=ago(1d), responsecodename= ' NXDOMAIN ' )

| summarize count() by SrcIpAddr, bin(TimeGenerated, 15m)

meets all requirements: it lists all DNS events with NXDOMAIN in the last 24 hours and aggregates them by source IP in 15-minute intervals , using ASIM’s parameterized parser to achieve optimal performance.

Enabling User and Entity Behavior Analytics (UEBA) in Microsoft Sentinel requires permissions in both Azure Active Directory (Microsoft Entra ID) and Microsoft Sentinel because UEBA integrates identity data from Azure AD to perform behavioral analytics and anomaly detection.

Here’s the reasoning based on Microsoft’s official documentation and the principle of least privilege:

1️ ⃣ Azure AD role → Security administ rator

The Security Administrator role in Azure AD allows a user to manage security-related features , including security settings and integration of identity signals with other services like Microsoft Sentinel.

This role has the rights necessary to grant Sentinel access to Azure AD data for UEBA without requiring broader, high-privilege roles such as Global Administrator .

Microsoft documentation explicitly recommends the Security Administrator role to enable UEBA because Sentinel uses Azure AD identity inf ormation and risk detections for its entity behavior modeling.

2️ ⃣ Azure role → Microsoft Sentinel Contributor

Within Sentinel, the Microsoft Sentinel Contributor role allows a user to configure settings, enable features like UEBA, and manage analytic rule s, workbooks, and connectors , but does not grant rights to access workspace data directly (which would be excessive).

It’s the appropriate role for managing Sentinel features while adhering to the least privilege principle.

The Sentinel Responder role is t oo limited—it can handle incidents but cannot enable or configure UEBA.

✅ Final Answer:

Azure AD role: Security administrator

Azure role: Microsoft Sentinel Contributor

1. IdentityQueryEvents When considering a table with AccountSid and it ' s about the LDAP request, it is " IdentityQueryEvents. "

2. isnotempty For determining whether there is a value in the AccountSid, it is " isnotempty. "

In Microsoft Sentinel with UEBA enabled, user-enrichment data about identities (such as Department , Title , and Account/SID mappings ) is written t o the IdentityInfo table. Microsoft guidance recommends joining your security telemetry (for example, SecurityEvent ) with IdentityInfo to filter or scope results by organizational attributes like department. To meet the requirement “only include security-s ensitive actions by users that are NOT members of the IT department” , you enrich the Windows security events with IdentityInfo and then filter Department !~ " IT " (or equivalent).

To minimize noise and duplicated rows when joining many-to-one identity recor ds, Sentinel KQL best practices recommend using join kind=innerunique . This join returns at most one matching row from the right table for each row on the left, which helps reduce false positives that can arise from duplicate or stale identity records whil e still ensuring matches are required (i.e., inner ). After enriching, you continue your query logic (for example, restricting to Server1 and the subset of security-sensitive event IDs or an UEBA-derived mapping) to identify only the relevant actions.

There fore, the correct completions are to use join kind=innerunique and join to IdentityInfo to apply the department filter and lower false positives.

C:\Users\Waqas Shahid\Desktop\Mudassir\Untitled.jpg

To automatically send a Microsoft Teams message when an incident (like a sign-in risk) is activated, you mus t use Logic Apps playbooks in Microsoft Sentinel.

Steps based on Microsoft documentation:

Add (create) a playbook (Option D) — a Logic App workflow that defines the automation (e.g., post a message to a Teams channel).

Associate the playbook to the analyti cs rule (Option B) that generates the incident — this ensures the playbook triggers automatically whenever that rule fires.

Other options do not accomplish the goal:

Entity behavior analytics (A) provides user and entity context but does not automate actions.

Fusion rule (C) correlates multi-stage attacks automatically but isn’t used to trigger notifications.

Workbooks (E) are for visualization, not automation.

✅ Final Answers:

Question 80: D. Assign the incident

Question 83: B and D

In Microsoft 365 Defender advanced hunting, if you want to automatically receive alerts based on a KQL query—such as detecting when a process disables System Restore—you must convert that query into a custom detection rule . According to Microsoft’s official documentation, custom detection rules “run hunting querie s on a schedule and create alerts and incidents when results are found.”

In order for the detection rule to function properly and correlate results across devices and incidents, the query must output DeviceId and ReportId . These fields are mandatory for an y advanced hunting query that you want to convert into a detection rule be cause they uniquely identify the device and event instance. Without them, the rule cannot properly generate correlated alerts.

Therefore:

Create a detection rule (A) – ensures the qu ery runs automatically and alerts are generated.

Add DeviceId and ReportId (E) – required for detection rule creation and accurate device/event correlation.

Other options are incorrect:

Suppression rule (B) filters alerts, not generate them.

Order by Times tamp (C) is optional for display, not alerting.

DeviceNetworkEvents (D) is unrelated to this process query.

 Table: DeviceFileEvents

 Aggregation function: count()

In Microsoft Defender XDR advanced hunting, data tables such as DeviceFileEvents , DeviceProcessEvents , and CloudAppEvents are used to investigate various types of activities. Since this query aims to investigate an issue related to file activity—specifically identifying when files have been accessed, modified, or created repeatedly—the correct data source table is DeviceFileEvents . This table contains information about file-level activities recorded by Defender for Endpoint sensors, including file path, file name, action type, and user account involved.

The KQL structure shown in the image follows standard hunting query syntax:

DeviceFileEvents

| where Timestamp > ago(2d)

| summarize activityCount = count() by FolderPath, FileName, ActionType, AccountDisplayName

| where activityCount > 5

Here’s why:

The where Timestamp > ago(2d) clause filters results from the last 2 days, a typi cal timeframe for immediate investigations.

The summarize operator groups events by FolderPath, FileName, ActionType, and AccountDisplayName, then uses count() to determine how many times each file was acted upon.

Finally, where activityCount > 5 filters t o show only unusually high-frequency activity, which might indicate suspicious or automated file manipulation.

Microsoft Defender XDR documentation highlights that DeviceFileEvents is the correct schema for file activity investigations, while DeviceProcess Events focuses on process creation and execution, and CloudAppEvents targets cloud application usage.

Thus, the verified and documented correct completions are:

Table: DeviceFileEvents

Aggregation function: count()

 Internal threat: Modify the access policy settings for the key vault.

 External threat: Modify the Key Vault firewall s ettings.

For internal threats involving a potential compromise of Fabrikam’s own Azure AD applications, the most direct and least disruptive remediation is to modify the Key Vault access policies (or RBAC assignments, if RBAC for Key Vault data-plane is in use) to immediately remove or reduce the compromised service principal’s permissions (Get/List/Decrypt/Sign/Wrap). Microsoft guidance for Key Vault access emphasizes least privilege and promptly revoking credentials or app permissions when compromise i s suspected. Access policies (or data-plane RBAC) govern which identities can access secrets, keys, and certificates; adjusting these stops further data-plane actions by the compromised app. “Resource locks” protect against deletion or configuration change s at the management plane, but they do not remove a compromised identity’s ability to read or use vault objects, so they are not an appropriate first response for this scenario.

For external threats , Microsoft recommends hardening Key Vault firewall and networking : restrict public network access, allow only required IPs, use virtual network rules, and prefer private endpoints. Key Vault includes a built-in firewall for IP and VNet ACLs; tuning these controls reduces exposure to the public internet and blo cks unauthorized traffic. NSGs apply to IaaS subnets/nics and don’t directly secure the public Key Vault endpoint. Azure Firewall can add perimeter control, but it is not necessary for remediating a specific Key Vault Defender alert; the most effective and immediate remediation is tightening the Key Vault firewall settings to limit external access pathways.

To integrate Microsoft Defender for Cloud Apps (MCAS) with Microsoft Sentinel , Microsoft’s official SecOps and Sentinel documentation specifies a two-step configuration process.

In the Defender for Cloud Apps portal – You add a security extension to enable integration with external SIEM platforms. This action allows MCAS to forward its alerts, activities, and discovered app telemetry to other Microsoft or third-party security platforms. By adding the security extension , Defende r for Cloud Apps is authorized to send data streams and alerts to Microsoft Sentinel through a supported API connection.

In Microsoft Sentinel (Azure portal) – You then add a data connector . Data connectors in Sentinel are predefined integration pipelines that bring in telemetry from Microsoft or external security solutions. The Microsoft Defender for Cloud Apps connector specifically ingests MCAS alerts and audit logs into Sentinel, where they can be correlated with other Microsoft Defender XDR signals, en abling unified detection and investigation across identity, endpoint, and cloud layers.

This integration approach adheres to Microsoft’s principle of minimizing administrative effort by using native connectors rather than custom ingestion or log collector configurations. Once connected, Sentinel automatically normalizes MCAS alerts into its SecurityAlert and CloudAppEvents tables for rule creation, playbook automation, and incident correlation.

Therefore, the verified correct configuration is:

Defender for Cloud Apps: Add a security extension

Sentinel: Add a data connector

To restrict cloud apps running on CLIENT1 (a Window s 10 endpoint) in compliance with Microsoft Defender for Endpoint (MDE) requirements, you must integrate Microsoft Defender for Endpoint with Microsoft Defender for Cloud Apps (formerly Cloud App Security) using Cloud Discovery . This integration enables th e blocking of unsanctioned cloud apps through the endpoint’s network protection capabilities.

According to Microsoft Defender for Cloud Apps documentation , Cloud Discovery uses traffic data from Defender for Endpoint to identify and manage the use of shado w IT. The relevant steps include:

1️ ⃣ Enable advanced features in Microsoft Defender for Endpoint (M365 Defender portal → Settings → Endpoints → Advanced features).

You must enable the following advanced features:

Microsoft Defender for Cloud Apps integrat ion

Network protection (in block mode)

Custom network indicators (if applicable) These options allow Defender for Endpoint to share telemetry and enforce app restrictions received from Defender for Cloud Apps.

2️ ⃣ Configure Cloud Discovery settings in Micr osoft Defender for Cloud Apps.

In the Defender for Cloud Apps portal, Cloud Discovery must be configured to receive continuous reports from Defender for Endpoint devices. Within these settings, you define sanctioned and unsanctioned applications. Once an a pp is marked as unsanctioned , Defender for Endpoint enforces blocking on all onboarded devices (like CLIENT1).

This two-part configuration ensures that MDE enforces the blocking of unsanctioned cloud applications discovered through Cloud App Security telem etry, fulfilling the business requirement that “All Cloud App Security unsanctioned apps must be blocked on the Windows 10 computers by using Microsoft Defender for Endpoint.”

 Log Analytics workspace to use: LA1

 Windo ws security events to collect: All Events

To meet the Azure Defender (Microsoft Defender for Cloud) requirement that all servers send logs to the same Log Analytics workspace , you should select the existing workspace LA1 . Defender for Cloud best practic es recommend centralizing data in a single workspace for unified analytics, incident correlation, and cost control. Using the “Default workspace created by Azure Security Center” or creating a new workspace would fragment telemetry, complicate management, and contradict the stated requirement and the business goal to minimize costs (multiple workspaces can increase ingestion/retention overhead and complicate RBAC and automation).

For Windows hosts, Defender for Cloud’s Data collection setting controls the l evel of Windows Security Events collected: Minimal , Common , or All Events . The business requirement calls for logs that provide a full audit trail of user activities . In Microsoft guidance, All Events is the level intended for comprehensive auditing (inclu ding logon/logoff, account changes, privilege use, process creation, object access, and other advanced categories). Therefore, to satisfy the “full audit trail” requirement and ensure complete visibility for investigations and Sentinel analytics, choose Al l Events .

In summary: centralize on LA1 (single workspace) and collect All Events to achieve both operational and compliance objectives with Defender for Cloud and Sentinel.

According to Micros oft’s official Defender for Identity deployment documentation, setting up Microsoft Defender for Identity (MDI) on an on-premises domain controller (DC) such as DC1 requires a specific sequence of steps. MDI monitors and protects Active Directory from adva nced threats by using sensors installed directly on domain controllers.

The correct sequence is as follows:

1️ ⃣ Provide global administrator credentials to the Azure AD tenant.

Microsoft Defender for Identity is created and managed in the Microsoft 365 Def ender portal or Microsoft Security portal, which requires global administrator permissions to provision the MDI instance and connect it to the Azure AD tenant.

2️ ⃣ Create an instance of Microsoft Defender for Identity.

You must create an MDI instance in th e portal before deploying any sensors. This instance defines your Defender for Identity workspace and generates the configuration package that sensors will later use to connect to the cloud service.

3️ ⃣ Provide domain administrator credentials to the on-pr emises Active Directory domain.

Domain admin credentials are required for the sensor installation because the sensor must access the domain controller’s security logs, event logs, and directory services to monitor authentication traffic and suspicious acti vities.

4️ ⃣ Install the sensor on DC1.

Since DC1 is a domain controller connected directly to the internet, the standard sensor (not the standalone sensor) is installed directly on it. The standalone sensor is used only when you do not want to install the sensor directly on a DC. The sensor automatically connects to the created MDI instance and begins collecting telemetry for detection.

This sequence aligns with Microsoft Defender for Identity deployment guidance, ensuring secure onboarding of DC1 while mai ntaining the principle of least privilege and meeting the business requirement to protect all domain controllers.

✅ Final Order:

Provide global administrator credentials →

Create instance of Microsoft Defender for Identity →

Provide domain administrator credentials →

Install the sensor on DC1.

To block unsanctioned cloud apps on Windows 10 endpoints with Microsoft Defender for Endpoint and Microsoft Defender for Cloud Apps (formerly Cl oud App Security), you must enable and configure the product integration on both sides. First, in Microsoft Defender Security Center → Settings → Advanced features , turn on the Microsoft Defender for Cloud Apps integration (and ensure network protection pr erequisites are met). This allows Defender for Endpoint to receive the unsanctioned app list and enforce endpoint-based blocking when users on CLIENT1 attempt to access those apps via the browser or client.

Second, in Defender for Cloud Apps → Settings → C loud Discovery , configure the Microsoft Defender for Endpoint integration and enable Block unsanctioned apps . In Cloud Discovery, apps are discovered, assessed, and can be tagged as Unsanctioned . Once the MDE integration is enabled, that tag is exported to endpoints, which then enforce blocking based on the tenant’s app catalog and policies.

Options A (Onboarding settings) are for enrolling devices and do not control app blocking behavior. B (Anomaly detection policies) govern behavioral detections (e.g., i mpossible travel, anonymous IP) and are unrelated to endpoint enforcement of app access. Therefore, the two configurations you must modify to meet the requirement “block unsanctioned apps on Windows 10 computers by using Microsoft Defender for Endpoint” ar e C. Advanced features in Microsoft Defender Security Center and D. Cloud Discovery settings in Cloud App Security .

To attach notes that you can later see during investigations and hunting, you use Bookmarks in Microsoft Sentinel. Bookmarks are created from the Hunting (or Logs) experience inside the Sentinel workspace and let you pin a specific query result (including IPs, accounts, hosts) with notes, tags, and mappe d entities so it appears in the investigation graph and is easily referenceable. The correct workflow is: first, run your KQL query from the Microsoft Sentinel workspace (not from generic Azure Monitor), because Sentinel’s hunting experience is where bookmarks integrate with incidents and the investigation graph. Next, select a specific query result that represents the suspicious activity (e.g., a data access event from the target IP). Finally, create a Bookmark and map the relevant entity (IP address, Account, etc.) while adding your notes . Mapping the entity ensures the bookmarked event is connected in the investigation graph; the notes provide the narrative/context you need when pivoting later. Adding the query to favorites is optional for convenience but does not attach notes to a specific event, and running the query from Azure Monitor would not place the bookmark within Sentinel’s investigation context.

To integrate Microsoft Defender for Cloud Apps (MCAS) with Microsoft Sentinel , Microsoft’s official SecOps and Sentinel documentation specifies a two-step configuration process.

In the Defender for Cloud Apps portal – You add a security extension to enable integration with external SIEM platforms. This action allows MCAS to forward its alerts, activities, and discovered app telemetry to other Microsoft or third-party security platforms. By adding the secu rity extension , Defender for Cloud Apps is authorized to send data streams and alerts to Microsoft Sentinel through a supported API connection.

In Microsoft Sentinel (Azure portal) – You then add a data connector . Data connectors in Sentinel are predefined integration pipelines that bring in telemetry from Microsoft or external security solutions. The Microsoft Defender for Cloud Apps connector specifically ingests MCAS alerts and audit logs into Sentinel, where they can be correlated with other Microsoft D efender XDR signals, enabling unified detection and investigation across identity, endpoint, and cloud layers.

This integration approach adheres to Microsoft’s principle of minimizing administrative effort by using native connectors rather than custom inge stion or log collector configurations. Once connected, Sentinel automatically normalizes MCAS alerts into its SecurityAlert and CloudAppEvents tables for rule creation, playbook automation, and incident correlation.

Therefore, the verified correct configur ation is:

Defender for Cloud Apps: Add a security extension

Sentinel: Add a data connector

According to Microsoft Defender for Cloud (formerly Azure Security Center) documentation, when integrating servers and virtual machines for security monitoring, the Defender for Cloud data is stored and analyzed in a Log Analytics workspace . Microsoft best practices recommend using an existing workspace when one is already deployed for centralized log collection—especially if it already gathers telemetry from Azure and on-premises resources.

In this case, the existing workspace LA1 already “contains logs and metrics collected from all Azure resources and on-premises servers.” This satisfies the business requirement of “all servers must send logs to the same Log Analytics workspace.” Creating a new workspace would violate the cost-min imization and centralization requirements. Therefore, LA1 is the correct workspace to use.

For the Windows security events collection setting, Defender for Cloud offers three levels:

Minimal – collects essential security events only (insufficient for compr ehensive monitoring).

Common – collects a balanced set of security-related events (such as account logon, privilege use, and object access), recommended by Microsoft for most environments.

All Events – collects every possible security event, which increase s data ingestion cost and is typically used only for high-security or regulated environments.

Because Litware Inc. must minimize cost while ensuring a full audit trail of user activities , the Common level provides the optimal balance of visibility and cost efficiency.

Therefore, based on Microsoft Defender for Cloud configuration guidelines:

✅ Log Analytics workspace to use: LA1

✅ Windows security events to collect: Common

To have a Microsoft Sentinel workbook pull live data from an external web service, you use the workbook’s built-in JSON data source. Workbooks can query REST endpoints that return JSON and render results dynamically in visuals. Because the Azure portal (where the workbook runs) is a different origin than your on-prem/public Webapp1 , browsers will block these cross-origin requests unless the endpoint explicitly allows them. Therefore, the external service must enable CORS to permit the portal’s origin to fetch the JSON. This aligns with Microsoft guidance that Workbooks can query HTTP/HTTPS JSON endpoints and that external endpoints must allow cross-origin requests for client-side calls from the Azure portal. Enforcing CORS on Webapp1 satisfies the security model while enabling the workbook to retrieve data at view time—meeting the requirement to “dynamically retrieve data from Webapp1.” Options like “custom endpoint” or “custom resource provider” aren’t needed here, and enabling SOP or only enforcing TLS 1.2 wouldn’t address the browser’s cross-origin policy blocking the call.

For a near-real-time (NRT) analytics rule that detects sign-ins by a designated break-glass account, the most direct and performant pattern is to filter SigninLogs by joining to a Microsoft Sentinel watchlist that contains the protected account(s). Sentinel exposes watchlists to KQL through the helper function _GetWatc hlist( ' < watchlist-name > ' ) , which returns a table with standard columns (including SearchKey ) plus any custom columns you imported. Using join kind=inner ensures the result set includes only those SigninLogs rows whose UserPrincipalName matches an entry in the watchlist—ideal for alerting on a high-value account without post-filtering.

The completed query is:

SigninLogs | join kind=inner (_GetWatchlist( ' breakglass_account ' )) on $left.UserPrincipalName == $right.SearchKey

This approach satisfies the requireme nt to implement an NRT rule for the break-glass account because:

NRT rules support KQL with joins and watchlists and are optimized for rapid evaluation over fresh data.

Using a watchlist lets SecOps adjust monitored accounts without editing the rule—minimi zing administrative effort and aligning with least-privilege operations (no extra permissions beyond watchlist management).

The inner join pattern reduces noise by returning only matched events, which are then turned into alerts/incidents by the NRT rule.

Thus, select join and GetWatchlist , and join UserPrincipalName to the watchlist’s SearchKey .

In Microsoft Sentinel’s Advanced Security Information Model (ASI M) , DNS queries are normalized through the Im_Dns parser, which unifies DNS telemetry from multiple sources (Infoblox, Windows DNS, Azure Firewall DNS proxy, etc.). Microsoft guidance states that when you need broad compatibility and want to “use built-in ASIM parsers whenever possible,” you should call the generic Im_Dns() parser . To minimize overhead , ASIM provides a pack parameter that restricts the parser to a specific content pack (vendor/source) so it won’t iterate through all available source parsers under the hood. For Infoblox NIOS, you pass the Infoblox pack via the pack parameter, which limits parsing to the Infoblox implementation and reduces query cost/latency while keeping the query portable across environments.

Putting it together, the recomme nded pattern is:

Im_Dns(pack= " InfobloxNIOS " )

| where DnsResponseCodeName == " NXDOMAIN "

| summarize count()

This approach satisfies all requirements:

Uses built-in ASIM (Im_Dns).

Minimizes query overhead (uses pack to limit parsing to Infoblox).

Targets NXD OMAIN responses for counting DNS request failures from Infoblox1 .

To monitor and detect higher-than-normal volumes of password resets, you need to gather password reset event data both from Azure Active Directory (cloud identities) and from on-premises Active Directory (domain accounts) . Microsoft’s official Defender XDR and Sentinel integrat ion guidance describes that:

Azure AD Password Protection enforces and monitors password policies in both cloud and hybrid environments. It can detect weak, commonly used, or compromised passwords and logs related password change/reset activities. Deployin g Azure AD Password Protection extends password reset visibility to on-premises domain controllers through the Password Protection proxy and DC agent. This makes it the correct choice for implementing monitoring at the identity environment level.

In Microsoft Sentinel , to ingest and analyze password reset activities from on-premises servers (e.g., domain controllers), you must use the Windows Security Events via AMA connector . This connector collects Event ID 4723 (password change) , 4724 (password res et) , and related security logs directly from Windows Servers into the Sentinel Log Analytics workspace through the Azure Monitor Agent (AMA) . Once the events are available in Sentinel, they can be correlated with other identity or behavioral analytics to d etect abnormal reset volumes or potential compromise attempts.

The other options are not suitable:

Microsoft Defender for Identity focuses on identity compromise detection, not specifically on password reset volume monitoring.

Smart lockout protects agains t brute-force sign-in attempts but doesn’t generate detailed reset event telemetry.

Microsoft security rule and UEBA are higher-level analytic configurations, not data ingestion mechanisms.

Therefore, to meet the Sentinel requirements for monitoring passwo rd reset anomalies:

✅ Implement in the identity environment: Azure AD Password Protection

✅ Configure in Microsoft Sentinel: The Windows Security Events via AMA connector

| From the Syslog configuration, remove the facilities that send CEF messages. | CEF1 | | From the Log Analytics agent, disable Syslog synchronization. | Server2 |

The goal is to eliminate duplicate events in the Azure Sentinel workspace ( SW1 ). Duplication typically occurs when the same log source is sending data to Azure Sentinel via multiple collection methods .

Analysis of the Environment

SW1 is the Azure Sentinel (now Microsoft Sentinel ) workspace, which is the final destination for all logs.

CEF1 is a Linux server configured as a log forwarder (often called a CEF collector ) for Microsoft Sentinel. It uses the Log Analytics agent (or the newer Azure Monitor Agent) to ingest logs and is specifically configured to forward Common Eve nt Format (CEF) logs to SW1 .

Server1 sends CEF logs to CEF1 . This is the intended, single collection path for Server1 ' s CEF logs: Server1 CEF1 SW1. No duplication is inherent here.

Server2 sends Syslog logs to CEF1 . This path is: Server2 CEF1 SW1.

Since CEF1 is running the Log Analytics agent (required to forward logs to SW1) and is configured to collect Syslog data (to receive Server2 ' s logs), the Log Analytics agent on CEF1 will also attempt to ingest the Syslog messages it receives into SW1.

However, t he Log Analytics agent itself can also be used to collect Syslog/CEF logs directly from the source server.

Addressing Duplication

Duplication is most likely to occur if a server is sending the same logs to a forwarder AND also has the Log Analytics agent configured to send the same logs directly to SW1.

Action 1: From the Syslog configuration, remove the facilities that send CEF messages.

Resource: CEF1

Reasoning: CEF1 is a Linux server running the Log Analytics agent and is acting as the collector. Server1 sends CEF logs to CEF1. These CEF logs are transmitted using Syslog (specifically, a custom Syslog format). If the Log Analytics agent on CEF1 is configured to collect all Syslog facilities, it will ingest the raw CEF Syslog messages it receives from Server1 AND also ingest the parsed CEF messages via its custom forwarding logic. To prevent the Syslog collector on CEF1 from ingesting the raw CEF messages that it is supposed to be forwarding , you must modify its Syslog configuration (e.g., in /etc/rsyslog.conf or equivalent) to ignore the facilities/log files used by the incoming CEF messages from Server1. The primary purpose of CEF1 is to receive and forward CEF, not to have its Log Analytics agent ingest the raw Syslog that transports the CEF payload.

Acti on 2: From the Log Analytics agent, disable Syslog synchronization.

Resource: Server2

Reasoning: Server2 is configured to send Syslog logs to CEF1 (Server2 CEF1 SW1). Since Server2 is a Linux server, it may also have the Log Analytics agent installed for other monitoring purposes. If the Log Analytics agent on Server2 is installed, it is configured by default to collect Syslog logs directly and send them to SW1 (Server2 SW1). This creates a duplicate path for the Syslog data:

Path A (Intended): Server2 Syslog CEF1 SW1

Path B (Duplication): Server2 Log Analytics Agent Syslog SW1

According to Microsoft Sentinel documentation on log ingestion, when using a dedicated forwarder (like CEF1) for Syslog/CEF, you must disable the Syslog collection on the Log Analytics agent of the source machine (Server2) to prevent this duplication. This is typically done by disabling Syslog synchronization in the Log Analytics agent configuration or removing the Syslog entry from the agent ' s data sources.

To hunt across multiple subscriptions/workspaces from a single Microsoft Sentinel workspace, you don’t need to re-ingest data; you can query it where it already lives using cross-workspace queries . In KQL, this is done with the workspace() expression combined with union to stitch results from several workspaces in one result set (for example: union workspace( ' WS1 ' ).SecurityEvent, workspace( ' WS2 ' ).SecurityEvent … ). This approach is the supported method for hunting across many projects/subscriptions while keeping data in its original Log Analytics workspaces, and it avoids duplicating ingestion or moving data. In addition, to integrate those remote workspaces with Sentinel features and ensure consistent schema/solution components, you add the Microsoft Sentinel solution to each workspace you want to include. Installing the solution enables Sentinel’s content pack, schemas, and permissions model on those workspaces so they can fully participate in Sentinel scenarios while you run cross-workspace hunts from your central workspace. Therefore: (B) Create a query that uses the workspace expression and the union operator and (E) Add the Azure Sentinel solution to each workspace .

In Microsoft Sentinel , the Advanced Security Information Model (ASIM) provides a standardized schema and parser layer fo r common telemetry types (DNS, Authentication, NetworkSession, etc.). To investigate DNS-related activity, you should use the ASIM-normalized table ASim_Dns , which unifies data from multiple DNS sources (Microsoft Defender for Endpoint, Azure Firewall, DNS servers, etc.) under a consistent schema.

The ASim_Dns parser standardizes key fields such as:

TimeGenerated → timestamp of the DNS event

ResponseCodeName → name of the DNS response code (e.g., NXDOMAIN , NOERROR , etc.)

QueryName → domain name queried

SrcIpAddr and DstIpAddr → IP addresses involved

To investigate recent DNS failures (e.g., non-existent domains), the query filters for events where:

TimeGenerated > ago(7d) — limits to the last 7 days of DNS logs.

ResponseCodeName == " NXDOMAIN " — identifie s DNS lookups that returned “Non-Existent Domain,” a common indicator of suspicious or misconfigured activity.

Therefore, the correct and compliant ASIM-based query syntax is:

ASim_Dns

| where TimeGenerated > ago(7d)

| where ResponseCodeName == " NXDOMAIN "

This approach ensures your investigation leverages ASIM normalization and aligns with Microsoft Sentinel best practices for DNS event analysis.

To release quarantined items on a Windows device protected by M icrosoft Defender for Endpoint/Antivirus , you use the Microsoft Defender command-line utility MpCmdRun.exe . This tool supports maintenance actions including scanning, definition management, and quarantine operations. For quarantine specifically, the supported switch is -Restore , which restores items from quarantine. The typical syntax for restoring all items for a specific threat name is:

MpCmdRun.exe -Restore -Name " < ThreatName > " -All

Here, the -Name parameter specifies the detected threat family or name (for custom indicators it reflects the custom threat label, e.g., EUS:Win32/CustomEnterpriseBlock ), and -All applies the restore operation to all items associated with that name—ideal when multiple files (e.g., 20) were quarantined by the same indicat or.

Other options are not appropriate for this task:

-GetFiles collects diagnostic logs for support.

-RemoveDefinitions and -ResetPlatform handle antimalware engine/definitions, not quarantine.

MsMpEng.exe is the Defender service binary (not invoked direct ly), and Start-MpRollback is a separate PowerShell cmdlet for rolling back remediations, not for restoring quarantined files by name.

Therefore, to release the 20 files: MpCmdRun.exe -Restore -Name " EUS:Win32/CustomEnterpriseBlock " -All .