Building Cloud SIEM with Sentinel
When to Use
- When establishing a centralized security operations center for multi-cloud environments
- When migrating from legacy SIEM platforms (Splunk, QRadar) to cloud-native architecture
- When building automated incident response workflows for cloud-specific threats
- When performing large-scale threat hunting across petabytes of security telemetry
- When integrating threat intelligence feeds with cloud security log analysis
Do not use for AWS-only environments where Security Hub and GuardDuty suffice, for endpoint detection requiring EDR capabilities (use Defender for Endpoint), or for compliance posture monitoring (see building-cloud-security-posture-management).
Prerequisites
- Azure subscription with Microsoft Sentinel enabled on a Log Analytics workspace
- Data connector permissions for target log sources (AWS CloudTrail, Azure Activity, GCP)
- Logic Apps or Azure Functions for automated response playbooks
- KQL (Kusto Query Language) proficiency for writing detection rules and hunting queries
Workflow
Step 1: Provision Sentinel Workspace and Data Connectors
Create a Log Analytics workspace optimized for security data and enable data connectors for multi-cloud ingestion.
# Create Log Analytics workspace
az monitor log-analytics workspace create \
--resource-group security-rg \
--workspace-name sentinel-workspace \
--location eastus \
--retention-time 365 \
--sku PerGB2018
# Enable Microsoft Sentinel on the workspace
az sentinel onboarding-state create \
--resource-group security-rg \
--workspace-name sentinel-workspace
# Enable AWS CloudTrail connector
az sentinel data-connector create \
--resource-group security-rg \
--workspace-name sentinel-workspace \
--data-connector-id aws-cloudtrail \
--kind AmazonWebServicesCloudTrail \
--aws-cloud-trail-data-connector '{
"awsRoleArn": "arn:aws:iam::123456789012:role/SentinelCloudTrailRole",
"dataTypes": {"logs": {"state": "Enabled"}}
}'
# Enable Azure AD sign-in and audit logs
az sentinel data-connector create \
--resource-group security-rg \
--workspace-name sentinel-workspace \
--data-connector-id azure-ad \
--kind AzureActiveDirectory \
--azure-active-directory '{
"dataTypes": {
"alerts": {"state": "Enabled"},
"signinLogs": {"state": "Enabled"},
"auditLogs": {"state": "Enabled"}
}
}'
Step 2: Write KQL Detection Rules
Create analytics rules using Kusto Query Language to detect cloud-specific threats. Map each rule to MITRE ATT&CK techniques.
// Detect impossible travel - sign-ins from geographically distant locations
let timeframe = 1h;
let distance_threshold = 500; // km
SigninLogs
| where TimeGenerated > ago(timeframe)
| where ResultType == 0 // Successful sign-ins only
| project TimeGenerated, UserPrincipalName, IPAddress, Location,
Latitude = toreal(LocationDetails.geoCoordinates.latitude),
Longitude = toreal(LocationDetails.geoCoordinates.longitude)
| sort by UserPrincipalName asc, TimeGenerated asc
| extend PrevLatitude = prev(Latitude, 1), PrevLongitude = prev(Longitude, 1),
PrevTime = prev(TimeGenerated, 1), PrevUser = prev(UserPrincipalName, 1)
| where UserPrincipalName == PrevUser
| extend TimeDiff = datetime_diff('minute', TimeGenerated, PrevTime)
| where TimeDiff < 60
| extend Distance = geo_distance_2points(Longitude, Latitude, PrevLongitude, PrevLatitude) / 1000
| where Distance > distance_threshold
| project TimeGenerated, UserPrincipalName, IPAddress, Location, Distance, TimeDiff
// Detect AWS IAM credential abuse from CloudTrail
AWSCloudTrail
| where TimeGenerated > ago(24h)
| where EventName in ("ConsoleLogin", "AssumeRole", "GetSessionToken")
| where ErrorCode == ""
| summarize LoginCount = count(), DistinctIPs = dcount(SourceIpAddress),
IPList = make_set(SourceIpAddress, 10)
by UserIdentityArn, bin(TimeGenerated, 1h)
| where DistinctIPs > 3
| project TimeGenerated, UserIdentityArn, LoginCount, DistinctIPs, IPList
// Detect mass S3 object deletion (potential ransomware)
AWSCloudTrail
| where TimeGenerated > ago(1h)
| where EventName == "DeleteObject" or EventName == "DeleteObjects"
| summarize DeleteCount = count(), BucketsAffected = dcount(RequestParameters_bucketName)
by UserIdentityArn, bin(TimeGenerated, 10m)
| where DeleteCount > 100
| project TimeGenerated, UserIdentityArn, DeleteCount, BucketsAffected
Step 3: Build SOAR Playbooks with Logic Apps
Create automated response playbooks that execute when analytics rules trigger incidents. Common actions include blocking users, isolating resources, and enriching alerts with threat intelligence.
{
"definition": {
"triggers": {
"Microsoft_Sentinel_incident": {
"type": "ApiConnectionWebhook",
"inputs": {
"body": {"incidentArmId": "subscriptions/@{triggerBody()?['workspaceInfo']?['SubscriptionId']}/resourceGroups/@{triggerBody()?['workspaceInfo']?['ResourceGroupName']}/providers/Microsoft.OperationalInsights/workspaces/@{triggerBody()?['workspaceInfo']?['WorkspaceName']}/providers/Microsoft.SecurityInsights/Incidents/@{triggerBody()?['object']?['properties']?['incidentNumber']}"},
"host": {"connection": {"name": "@parameters('$connections')['microsoftsentinel']['connectionId']"}}
}
}
},
"actions": {
"Get_incident_entities": {
"type": "ApiConnection",
Step 4: Configure Sentinel Data Lake for Long-Term Hunting
Enable the Sentinel data lake for petabyte-scale log retention and advanced threat hunting using both KQL and SQL endpoints.
// Threat hunting query: detect lateral movement across AWS accounts
let suspicious_roles = AWSCloudTrail
| where TimeGenerated > ago(7d)
| where EventName == "AssumeRole"
| extend AssumedRoleArn = tostring(parse_json(RequestParameters).roleArn)
| where AssumedRoleArn contains "cross-account" or AssumedRoleArn contains "admin"
| summarize AssumeCount = count(), UniqueSourceAccounts = dcount(RecipientAccountId)
by UserIdentityArn, AssumedRoleArn
| where AssumeCount > 10 and UniqueSourceAccounts > 2;
suspicious_roles
| join kind=inner (
AWSCloudTrail
| where TimeGenerated > ago(7d)
| where EventName in ("RunInstances", "CreateFunction", "PutBucketPolicy")
) on UserIdentityArn
| project TimeGenerated, UserIdentityArn, AssumedRoleArn, EventName, SourceIpAddress
Step 5: Integrate Threat Intelligence
Connect threat intelligence providers and create indicator-based matching rules to detect communication with known malicious infrastructure.
# Enable Microsoft Threat Intelligence connector
az sentinel data-connector create \
--resource-group security-rg \
--workspace-name sentinel-workspace \
--data-connector-id microsoft-ti \
--kind MicrosoftThreatIntelligence \
--microsoft-threat-intelligence '{
"dataTypes": {"microsoftEmergingThreatFeed": {"lookbackPeriod": "2025-01-01T00:00:00Z", "state": "Enabled"}}
}'
// Match network indicators against cloud flow logs
let TI_IPs = ThreatIntelligenceIndicator
| where TimeGenerated > ago(30d)
| where isnotempty(NetworkIP)
| distinct NetworkIP;
AzureNetworkAnalytics_CL
| where TimeGenerated > ago(24h)
| where DestIP_s in (TI_IPs)
| project TimeGenerated, SrcIP_s, DestIP_s, DestPort_d, FlowType_s
Key Concepts
| Term | Definition |
|---|
| KQL | Kusto Query Language, the primary query language for Microsoft Sentinel used to search, analyze, and visualize security data |
| Analytics Rule | Detection logic in Sentinel that evaluates log data on a schedule and creates incidents when conditions match |
| SOAR Playbook | Automated workflow triggered by incidents that performs response actions such as blocking accounts, enriching alerts, or notifying teams |
| Data Connector | Integration module that ingests security logs from cloud services, identity providers, and third-party tools into Sentinel |
| Sentinel Data Lake | Petabyte-scale storage layer providing long-term log retention with KQL and SQL query interfaces for advanced hunting |
| Workbook | Interactive dashboard in Sentinel displaying visualizations of security data, trends, and operational metrics |
| Watchlist | Reference data tables in Sentinel used to enrich alerts with context such as VIP user lists or approved IP ranges |
| Fusion Detection | Machine learning-powered correlation engine that automatically detects multi-stage attacks across data sources |
Tools & Systems
- Microsoft Sentinel: Cloud-native SIEM/SOAR platform built on Azure Log Analytics with AI-powered threat detection
- Azure Logic Apps: Low-code automation platform for building SOAR playbooks triggered by Sentinel incidents
- Microsoft Threat Intelligence: Integrated threat feeds providing IP, domain, and URL indicators for matching against security logs
- Azure Data Explorer: High-performance analytics engine underlying Sentinel KQL queries for large-scale data exploration
- MITRE ATT&CK Navigator: Framework for mapping Sentinel detection rules to adversary tactics and techniques
Common Scenarios
Scenario: Detecting Cross-Cloud Credential Theft Campaign
Context: An attacker compromises an Azure AD account through phishing, then uses the account to access AWS resources via federated identity. Sentinel needs to correlate the Azure sign-in anomaly with unusual AWS API activity.
Approach:
- Create an analytics rule detecting Azure AD impossible travel or anomalous sign-in risk
- Write a KQL query correlating the compromised Azure AD identity with AWS CloudTrail AssumeRoleWithSAML events
- Build a Fusion detection rule that links Azure AD risk events with subsequent AWS privilege escalation activity
- Deploy a SOAR playbook that automatically disables the Azure AD account and revokes AWS STS sessions
- Create a workbook showing the timeline from initial compromise through lateral movement to AWS
- Run a hunting query across the data lake to check for similar patterns affecting other accounts
Pitfalls: Not correlating identity across cloud providers misses the full attack chain. Setting analytics rule frequency too low (e.g., 24 hours) allows attackers hours of undetected access.
Output Format
Microsoft Sentinel SOC Operations Report
==========================================
Workspace: sentinel-workspace
Data Sources: 14 connectors active
Report Period: 2025-02-01 to 2025-02-23
DATA INGESTION:
Azure AD Sign-in Logs: 2.3 TB (23 days)
AWS CloudTrail: 1.8 TB (23 days)
Azure Activity: 0.9 TB (23 days)
Defender for Cloud Alerts: 45 GB (23 days)
Total Ingestion: 5.1 TB
DETECTION SUMMARY:
Active Analytics Rules: 87
Incidents Created: 234
Critical: 8 | High: 34 | Medium: 89 | Low: 103
Mean Time to Detect (MTTD): 4.2 minutes
Mean Time to Respond (MTTR): 18 minutes
TOP INCIDENT TYPES:
Impossible Travel Detected: 42 incidents
AWS Unauthorized API Call Pattern: 28 incidents
Mass File Deletion in S3: 3 incidents
Suspicious Azure AD App Registration: 12 incidents
AUTOMATION:
Playbooks Executed: 156
Accounts Auto-Disabled: 23
Incidents Auto-Enriched: 198
False Positive Rate: 12%
