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Introduction:
The December 2025 update for Microsoft Sentinel introduces a suite of powerful new features, fundamentally enhancing how security operations centers (SOCs) detect, investigate, and respond to threats. By deeply integrating advanced AI and expanding automation, this release transforms the platform from a SIEM into a proactive security co-pilot. This guide will dissect the key technical enhancements and provide actionable steps to leverage them for immediate security posture improvement.
Learning Objectives:
- Deploy and configure the new Fusion ML analytics rule for sophisticated attack sequence detection.
- Master the expanded Threat Hunting page with its advanced querying and investigation capabilities.
- Implement enhanced Automation rules and playbooks for streamlined, AI-informed incident response.
- Activating the New Fusion Machine Learning Analytics Rule
This new rule represents a paradigm shift from static correlation to dynamic, AI-driven detection of multi-stage attacks. It uses machine learning models to analyze seemingly disparate low-fidelity alerts (like a failed login followed by a PowerShell execution) and surfaces them as a single, high-confidence incident when the sequence indicates a potential kill chain.
Step-by-step guide:
- Navigate to Analytics: Within your Microsoft Sentinel workspace, go to Configuration > Analytics.
- Create New Rule: Click Create and select Fusion ML rule (Preview) from the dropdown.
- Configure Scope: Define the rule’s scope by selecting the relevant data sources. For broad coverage, ensure SecurityEvent (Windows logs), CommonSecurityLog (firewall/network), and AuditLogs (Azure AD) are included.
- ️ Tune Sensitivity: Adjust the Alert threshold slider. Start with “Medium” and monitor the incident queue for a week before adjusting to reduce false positives or increase sensitivity.
- Enable & Deploy: Finalize the configuration and enable the rule. It will now run continuously in the background.
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Mastering the Expanded Threat Hunting Page for Proactive Searches
The redesigned Threat Hunting page is now a centralized investigation cockpit. It integrates custom and pre-built Livestream queries, allowing you to run persistent, real-time searches across logs and pivot directly into entity profiles (users, hosts, IPs) without leaving the interface.
Step-by-step guide:
- Access the Hub: Go to Threat management > Threat Hunting.
- Utilize Livestream: To hunt for specific real-time activity, click Livestream. For example, to monitor for new service creation—a common persistence tactic—you could use and save this KQL query:
SecurityEvent | where EventID == 4697 | where TimeGenerated > ago(10m) | project TimeGenerated, Computer, SubjectUserName, ServiceName, ServiceFileName
- Bookmark & Investigate: Run your query. If you find interesting results, click Bookmark to save the investigation context. Click on any Computer or SubjectUserName entity to open its detailed profile pane, revealing all related activity.
3. Implementing Enhanced Automation with Incident Triggers
Automation rules now support triggers based on incident severity changes and new alert additions. This allows for dynamic response workflows, such as escalating a case only when its severity is manually raised by an analyst or automatically enriching an incident when a related alert from a different source appears.
Step-by-step guide:
- Create Automation Rule: Navigate to Configuration > Automation and click Create.
- Set Dynamic Conditions: Under “Apply this rule when,” select Incident is updated. Add conditions: `Incident severity changed to High` AND
Alert product name contains "Azure Advanced Threat Protection". - Configure Actions: Add the following actions in order:
Run playbook: Select a pre-built playbook like “Block IP – Firewall” or a custom one for enrichment.
Assign owner: Automatically assign the incident to your tier-2 SOC team queue.
Change status: Set status to Active.
- Test Logic: Create a test incident, add a matching alert, and change its severity to High to validate the rule’s execution in the Incident’s “Activity log”.
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Hardening Cloud Assets with the New AWS S3 Connector
A critical addition for multi-cloud environments, this new data connector provides native ingestion of AWS CloudTrail logs for S3 buckets directly into Sentinel. This closes a visibility gap, allowing you to detect suspicious bucket policy changes, access from anomalous locations, or potential data exfiltration attempts.
Step-by-step guide:
- Deploy Connector: In Configuration > Data connectors, search for and select the Amazon Web Services S3 connector. Click Open connector page.
- Configure AWS Side: Follow the in-console instructions to create an AWS IAM Role with a policy that grants `s3:GetObject` and `s3:ListBucket` permissions for your specific CloudTrail log bucket.
- Provide ARN in Sentinel: Copy the AWS Role ARN and paste it into the connector configuration in the Azure portal.
- Verify Data Flow: Create a KQL query to confirm logs are arriving:
AWSCloudTrail | take 10 | project EventTime, EventSource, EventName, SourceIPAddress, UserIdentityArn
Set up a scheduled analytics rule to alert on high-risk events like
DeleteBucketPolicy.
5. Leveraging Advanced AI Enrichment in Incident Timeline
The incident timeline now automatically enriches raw alerts with AI-generated context. For an alert on a malicious PowerShell command, the timeline might now also show the associated risk score, linked MITRE ATT&CK tactic (e.g., T1059.001 – Command and Scripting Interpreter), and a summary of why the activity is considered suspicious.
Step-by-step guide:
- Review an Incident: Open any incident with multiple alerts. Click on the Timeline tab.
- Analyze Enrichment: Observe the new “Enriched Context” cards attached to alerts. They provide a plain-text explanation and key extracted entities.
- Utilize for Triage: Use this synthesized information to quickly understand the attack narrative without manually cross-referencing multiple log tables, dramatically speeding up initial triage and decision-making.
6. Configuring Custom Detections with Scheduled Analytics Rules
While the Fusion rule provides AI-driven detection, custom scenarios require tailored analytics. The updated scheduled rule interface offers improved query performance monitoring and more granular scheduling, allowing for precise control over rule execution and resource use.
Step-by-step guide:
- Create Scheduled Rule: In Analytics, create a new Scheduled query rule.
- Build a Detection Query: Write a KQL query for a specific threat. Example: Detecting potential LSASS memory dumping for credential access (MITRE T1003):
SecurityEvent | where EventID == 4688 // Process creation | where ProcessName contains "procdump" or CommandLine contains "lsass" | join ( SecurityEvent | where EventID == 4624 // Successful logon | where AccountType == "User" | summarize LogonCount = count() by TargetUserName, IpAddress | where LogonCount > 5 // Anomalous logon count threshold ) on $left.SubjectUserName == $right.TargetUserName
- Set Entity Mapping: Correctly map query fields to Account, Host, and IP entities for proper incident grouping.
- Configure Alert Details: Set a relevant title, description, and map to the MITRE tactic T1003.
What Undercode Say:
The SOC Analyst’s Workload is Shifting. The emphasis on AI-driven Fusion alerts, automated enrichment, and integrated hunting tools signifies a move away from manual alert sifting. Analysts must now develop skills to interpret AI conclusions, manage automated workflows, and conduct more proactive, hypothesis-driven hunting.
Data Unification is Non-Negotiable. The value of the new Fusion rule and cross-platform connectors (like AWS S3) is entirely dependent on the quality and breadth of ingested logs. Organizations that continue to operate with siloed data sources will gain only a fraction of the update’s defensive potential, leaving critical blind spots.
Prediction:
The trajectory set by this update points toward a future where Microsoft Sentinel evolves into an autonomous security operations platform. We predict that within 18-24 months, AI will not only correlate alerts but will autonomously execute contained response actions (like isolating endpoints or rotating credentials) via playbooks, presenting them to analysts for approval. The role of the human analyst will increasingly become that of a strategic overseer and incident commander, validating AI decisions and handling the most complex, novel attacks that fall outside of trained models. This will raise critical new questions around AI accountability, explainability in incident reports, and the ethics of automated countermeasures.
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Reported By: Pjchacon Microsoftsentinel – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
