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Introduction:
In the modern threat landscape, security teams are overwhelmed by a deluge of alerts from disparate sources. Microsoft Sentinel’s EntityAnalyzer playbook templates provide a powerful, automated solution, leveraging artificial intelligence to synthesize data from across your digital estate. By correlating signals from identity logs, cloud events, threat intelligence, and behavioral analytics, these playbooks deliver a comprehensive, AI-driven risk assessment for critical entities like users and URLs, transforming raw data into actionable security intelligence.
Learning Objectives:
- Understand the architecture and data sources integrated by the EntityAnalyzer playbook for holistic risk assessment.
- Learn to deploy and configure the playbook, including critical settings for incident-triggered analysis and loop concurrency management.
- Implement operational best practices to integrate automated entity risk scoring into your SOC’s investigation and response workflows.
You Should Know:
1. The Architecture of AI-Driven Entity Risk Assessment
The EntityAnalyzer playbook operates as an orchestration hub within Microsoft Sentinel’s Logic Apps environment. It doesn’t generate new signals but acts as a force multiplier for your existing investments in Microsoft 365 Defender, Azure AD, Microsoft Defender for Cloud Apps, and other integrated sources. Its core function is to query, correlate, and analyze telemetry across these platforms using built-in machine learning models to identify anomalies and threats that may be invisible when looking at any single data source. For instance, a benign Azure AD sign-in from a new location becomes high-risk when the playbook correlates it with a suspicious Power BI data export event and a known malicious URL clicked by the same user hours earlier.
Step‑by‑step guide:
Step 1: Access the Template. Navigate to your Microsoft Sentinel instance in the Azure portal. Go to Automation > Playbook templates. Search for “EntityAnalyser” (note the British English spelling) or “Analyze User and URL Risk with EntityAnalyser.”
Step 2: Review Prerequisites. The template details pane lists required data connectors (e.g., Azure AD, Microsoft 365 Defender, Threat Intelligence) and API permissions. Ensure these are enabled and that the Managed Identity for the Logic App will have the necessary roles (like Microsoft Sentinel Responder and reader roles on relevant workspaces).
Step 3: Create and Deploy. Click “Create playbook.” This opens a configuration blade. Provide a name (e.g., EntityAnalyser-UserURL-RiskAssessment), select the appropriate Resource Group and Sentinel workspace, and review the defined actions and connections before deploying.
- Configuring the Incident Trigger and Critical Loop Concurrency
The playbook can be triggered manually on a selected entity or, more powerfully, automatically when an incident is created. The linked documentation highlights a crucial tip: managing loop concurrency. When triggered by an incident containing multiple users and URLs, the playbook will iterate through each entity. Without concurrency control, it might spawn dozens of parallel API calls, potentially hitting throttling limits or causing performance issues.
Step‑by‑step guide:
Step 1: Enable Incident Trigger. After deployment, in your playbook’s Logic Apps Designer, the trigger will be “When a Microsoft Sentinel incident creation rule is triggered.” You must create an automation rule in Sentinel to call this playbook. Go to Automation > + Create > Automation rule. Set conditions (e.g., trigger on medium or high-severity incidents) and add the Run playbook action, selecting your deployed EntityAnalyzer.
Step 2: Implement Concurrency Control. In the Logic App designer, find the “For each user in the incident” loop. Click the Settings icon (three dots) on the loop container. Set Concurrency Control to On. Set Degree of Parallelism to a safe limit, such as 5. This ensures the playbook processes entities in manageable batches. Repeat this for the URL analysis loop.
3. Interpreting the Enriched Incident Output
Once executed, the playbook appends its findings directly to the Sentinel incident as comments and updates entity details. The analysis is not a simple “high/medium/low” score but a compiled summary of evidence. For a user, this may include: anomalous travel flags from sign-in logs, pending risky user state from Identity Protection, associated alerts from Microsoft Defender for Endpoint, and unusual administrative consent grants. For a URL, it will display its threat intelligence reputation (malicious, phishing, etc.), who clicked it, and associated incidents.
Step‑by‑step guide:
Step 1: Locate the Enrichment. Open a Microsoft Sentinel incident that has triggered the playbook. Navigate to the Comments timeline. Look for entries titled “User Risk Analysis Summary” or “URL Risk Analysis Summary” posted by the system.
Step 2: Triage with Enriched Data. Use the compiled evidence to prioritize response. A user flagged for “leaked credentials” and “impossible travel” requires immediate password reset and session revocation. Click the enriched user entity within the incident to see all correlated alerts and logged actions in a unified timeline.
4. Extending Analysis with Custom Hunting Queries
The out-of-the-box playbook is powerful, but you can extend its value by integrating custom hunting queries from your environment. If you have proprietary applications or niche threat detections, their logs can be fed into the analysis.
Step‑by‑step guide:
Step 1: Create a Relevant KQL Query. In Sentinel’s Logs section, author a query that returns risk-relevant data. For example, a query detecting excessive failed access to a sensitive on-premises file server: SecurityEvent | where EventID == 4625 | where Account contains "targetUser" | summarize FailedAttempts = count() by Account, Computer.
Step 2: Integrate into the Playbook. In the Logic App designer, add a new action within the user analysis loop. Use the Execute Azure Log Analytics Query action. Paste your KQL, dynamically injecting the iterated user’s principal name (@{items('For_each_user')?['properties']?['accountName']}). Format the results and append them to the playbook’s output variable.
5. Operationalizing Findings: Automating Response Actions
Risk assessment is only half the battle. The true power of automation is realized when you chain the EntityAnalyzer’s output to conditional response actions. Based on the aggregated risk score, you can trigger automated containment measures.
Step‑by‑step guide:
Step 1: Add Conditional Logic. After the “Compose risk summary” action for a user, add a Condition control. Set the condition to check if the user’s aggregated risk level is “High.”
Step 2: Define Automated Responses. Inside the If yes branch, add response actions. These could include:
Disable User (via Microsoft Graph API): Add an HTTP action to PATCH https://graph.microsoft.com/v1.0/users/{userID}` with a JSON body{“accountEnabled”: false}`.
Require Password Reset (via Azure AD Portal): Use the `Send an HTTP request to Azure AD` action to invoke the `invoke-AzureADMSUserResetPassword` method.
Create a Ticket in IT Service Management (ITSM): Use the Sentinel ITSM connector to log a high-priority ticket with all investigation details.
What Undercode Say:
- Automation is Force Multiplication, Not Replacement: The EntityAnalyzer exemplifies how AI and automation elevate human analysts. It performs the tedious, cross-platform data gathering and initial correlation in seconds, freeing SOC analysts to focus on strategic investigation and decision-making. The critical “loop concurrency” tip underscores that automation requires thoughtful engineering to be robust and reliable.
- Context is King in Modern Detection: This tool’s value lies in its ability to break down data silos. A medium-fidelity alert from one system, when combined with a low-fidelity anomaly from another, can create a high-confidence compromise indicator. The playbook operationalizes the “assume breach” mindset by proactively hunting for secondary evidence around every primary alert.
The integration of such automated entity analysis marks a shift from alert-centric to entity-centric security operations. It moves the SOC from asking “What happened with this alert?” to “What is this entity’s overall security posture and what actions should we take?” The careful management of API calls and concurrency is a minor technical detail with major operational implications, preventing automation from inadvertently causing an outage. As threat actors increasingly automate their own attacks, tools like the EntityAnalyzer are essential for defenders to match the scale and speed of the adversary.
Prediction:
Within two years, AI-driven entity risk scoring will evolve from a triggered playbook to the core organizational framework of the SIEM. Sentinel and its competitors will move towards real-time, continuous entity risk dashboards that update with every logged event, rendering static, alert-based incidents obsolete. This will facilitate predictive security postures, where systems can automatically enact graduated security controls (like requiring step-up authentication) based on a live, dynamically calculated risk score, fundamentally changing response from reactive to pre-emptive.
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IT/Security Reporter URL:
Reported By: Stefanopescosolido Microsoftsentinel – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
