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Introduction:
Turning on User and Entity Behavior Analytics (UEBA) in Microsoft Sentinel feels like a silver bullet—but in reality, it’s just a toggle. Without a mature detection engineering pipeline that layers tuning, contextual enrichment, and behavioral flighting, your SOC remains stuck at noise-driven chaos, where analysts manually copy IP addresses into browsers and alerts auto-close without investigation.
Learning Objectives:
- Understand the three maturity layers of Microsoft Sentinel detection engineering: tuning & enrichment, context integration, and behavioral remediation.
- Learn how to use KQL joins with IdentityInfo and Logic Apps for automated threat intelligence (TI) enrichment.
- Implement Flighting mode for anomaly rules to preserve analyst trust and reduce alert fatigue.
You Should Know
- Layer 1 – Tuning & TI Enrichment (Stop the Noise Firehose)
The most common mistake: deploying all 80+ Content Hub templates on day one and then disabling half of them because of alert floods. Layer 1 is not about having rules “on”—it’s about enabling them in small batches, tuning thresholds, and automating basic enrichment before an analyst even sees the alert.
Step‑by‑step guide to fix Layer 1:
- Inventory your active rules – In Sentinel, go to Analytics → Active rules. Disable all rules that have generated >100 false positives in the last 7 days.
- Batch enablement – Enable only 5–10 rules per week. For each, set a baseline using `customThreshold` in KQL.
- Automate TI enrichment – Use a Logic App to fetch fresh IoCs (e.g., from AlienVault OTX) and append them to incident comments.
Example KQL – Threshold tuning for failed logins:
SigninLogs | where ResultType == "50057" // User account disabled | summarize FailureCount = count() by UserPrincipalName, IPAddress | where FailureCount > 5 // Baseline threshold – tune based on your environment | project-rename Account = UserPrincipalName, SourceIP = IPAddress
Logic Apps enrichment snippet (pseudo) – attach IP reputation:
"HTTP_GET": {
"uri": "https://otx.alienvault.com/api/v1/indicators/IPv4/@{variables('AlertIP')}/reputation",
"method": "GET"
}
Then append the reputation output to the incident’s `description` field via Sentinel API.
- Layer 2 – Context Is King: Joining IdentityInfo in KQL
A standard user mistyping a password is not the same as your Exchange Admin failing a login at 2 AM from a new country. Layer 2 forces you to enrich every alert with IdentityInfo so static rules become dynamic.
Step‑by‑step guide:
- Ensure your IdentityInfo table is populated (requires Microsoft Entra ID Connect or MDI).
- Write KQL that joins `SigninLogs` with `IdentityInfo` using
UserPrincipalName. - Add
UserType,IsAdmin, and `CountryLastLogin` to every detection.
KQL example – contextual alert for suspicious logins:
let high_risk_users = IdentityInfo | where IsAdmin == true or UserType == "Guest" | project UserPrincipalName; SigninLogs | where ResultType == "50057" | join kind=inner high_risk_users on UserPrincipalName | extend IsNightTime = hour(TimeGenerated) between (0 .. 6) | where IsNightTime == true | extend CountryContext = iif( Location != "US", "High risk - foreign country", "Low risk") | project TimeGenerated, UserPrincipalName, Location, IPAddress, CountryContext
This rule only fires for admins or guests during night hours, drastically reducing false positives.
Windows command (for on‑prem enrichment):
Pull last logon country from Event Logs (if you have GeoIP lookup)
Get-WinEvent -FilterHashtable @{LogName='Security'; ID=4624} |
Select-Object -First 10 |
ForEach-Object { $_.Properties[bash].Value } Extract IP, then resolve via external API
- Layer 3 – Behavioral & Auto‑Remediation (Flighting Mode Is Mandatory)
You cannot push an anomaly rule straight to production. It will flood the queue and kill analyst trust permanently. Layer 3 requires every anomaly rule change to go through Flighting mode (no exceptions).
Step‑by‑step guide for Flighting:
- In Sentinel, create a new anomaly rule under Analytics → Anomaly rules.
- Set the Run frequency to 6 hours, but change the Alert threshold to “Flighting only” – this generates incidents without closing them automatically.
- Monitor for 7 days. Use the Anomalies workbook to review detection accuracy.
- Only after achieving >90% precision, toggle to “Create alerts” and enable auto‑remediation playbooks.
Auto‑remediation Logic App example (block IP on FortiGate):
{
"steps": {
"Parse_Incident_IP": { "type": "ParseJson" },
"SSH_to_FortiGate": {
"type": "Action",
"inputs": {
"host": "192.168.1.1",
"command": "config firewall address\n edit \"blocked_@triggerOutputs()?['body/ip']\"\n set subnet @triggerOutputs()?['body/ip'] 255.255.255.255\n next\n end"
}
}
}
}
Linux command (using `curl` to Sentinel API for flighting validation):
Pull all flighting incidents from last 24h
curl -X GET "https://api.loganalytics.io/v1/workspaces/{workspaceId}/query" \
-H "Authorization: Bearer $TOKEN" \
--data '{"query": "SecurityIncident | where Status == \"New\" and Flighting == true | take 50"}' | jq '.tables[bash].rows'
- Workbook Design to Guide Analyst Triage (The Unsung Hero)
Even with enriched alerts, analysts need a clear workbook that shows why an alert fired and what to do next. This prevents the “copy IP to browser” syndrome.
Step‑by‑step to build a triage workbook:
- In Sentinel, go to Workbooks → New → Blank template.
- Add a query that pulls the top 10 open incidents with their `enriched_context` (custom property from your Logic App).
- Add a “Next Steps” column based on severity:
– High → “Trigger isolation playbook”
– Medium → “Check IdentityInfo for admin status”
– Low → “Auto-close after 1h if no repeat”
4. Embed a KQL timeline of the user’s behavior 30 minutes before/after the alert.
KQL for timeline visualization:
let alert_time = datetime(2025-04-01T02:00:00Z); union SigninLogs, AuditLogs | where TimeGenerated between (alert_time - 30m .. alert_time + 30m) | where UserPrincipalName == "[email protected]" | project TimeGenerated, OperationName, ResultType, IPAddress | order by TimeGenerated asc
5. Automation Rules: From Auto‑Close to Intelligent Remediation
Many teams automate alerts to auto‑close and never look at them. That’s the wrong direction. Instead, use automation rules to triage, not bury.
Step‑by‑step intelligent automation:
- Create an automation rule for all Medium‑severity incidents.
- Add condition: `If IdentityInfo.IsAdmin == false AND IPAddress in (known_good_IPs)` → `Set severity to Low` and add comment “False positive likely”.
- Add second condition: `If IPAddress has any TI_indicator (malicious)` →
Run Logic App to block and escalate. - Never auto‑close without human review – instead, set a 4‑hour reminder using Azure Functions.
Python Azure Function (reminder webhook):
import requests
incident_id = "INC123"
if incident_status == "InProgress" and (datetime.now() - created_time).seconds > 14400:
requests.post("https://your_teams_webhook", json={"text": f"Incident {incident_id} has been open for 4h!"})
- Linux / Windows Commands for SOC Automation Engineers
Linux – Extract recent failed SSH attempts and feed into Sentinel:
Parse auth.log for failed SSH, format as JSON
sudo grep "Failed password" /var/log/auth.log | awk '{print $1, $2, $3, $11, $13}' | \
while read date time month ip user; do
jq -n --arg dt "$date $time" --arg ip "$ip" --arg u "$user" \
'{TimeGenerated: $dt, SourceIP: $ip, Account: $u, Event: "SSH_Fail"}'
done | curl -X POST "https://<sentinel-workspace>.ods.opinsights.azure.com/api/logs" -H "Content-Type: application/json" -d @-
Windows – Query failed RDP logins and compute anomaly score:
Get last 50 failed RDP events
$events = Get-WinEvent -FilterHashtable @{LogName='Security'; ID=4625} -MaxEvents 50
$events | ForEach-Object {
$ip = $<em>.Properties[bash].Value
$user = $</em>.Properties[bash].Value
[bash]@{Time=$_.TimeCreated; User=$user; IP=$ip}
} | Export-Csv -Path "C:\Sentinel\rdp_failures.csv" -NoTypeInformation
Then use KQL in Sentinel to ingest and alert if count > 10 per hour per user
- Anomaly Rule Example – Detecting Impossible Travel with Flighting
KQL anomaly rule (flight mode first):
let time_window = 1h; SigninLogs | where TimeGenerated > ago(time_window) | summarize LoginLocations = make_set(Location) by UserPrincipalName | where array_length(LoginLocations) > 1 | extend DistanceKm = geo_distance_2points( toscalar(LoginLocations[bash].coordinates), toscalar(LoginLocations[bash].coordinates) ) | where DistanceKm > 1000 // More than 1000 km in 1h – impossible | project UserPrincipalName, LoginLocations, DistanceKm
Deploy this as an anomaly rule, set Flighting for 14 days. During that period, compare against actual travel data (VPN logs, badge swipes). Only promote to production if false positive rate <5%.
What Undercode Say
- Key Takeaway 1: Toggling UEBA or deploying all Content Hub templates without batching and tuning guarantees alert burnout. You must start with enriched, threshold-tuned rules before layering any behavioral analytics.
- Key Takeaway 2: Analyst trust is your most fragile SOC asset. Flight every anomaly rule for at least a week, and never auto-close incidents without a manual review window – automation should triage, not hide.
Analysis: The LinkedIn discussion between Bartosz Wysocki and German Viglianco exposes a painful truth: 80% of Sentinel environments operate at Layer 0 (rules on, no tuning) or Layer 1 (no context). UEBA is sold as a fix, but without joining IdentityInfo and enforcing Flighting mode, it becomes just another noise source. The commands and KQL examples above directly address the “copy IP to browser” problem by automating enrichment and guiding analysts with workbooks. Most SOCs skip Layer 2 entirely, treating all logins equally – that’s why their MTTI (Mean Time to Investigate) is measured in hours instead of minutes.
Prediction
Within 12 months, Microsoft will deprecate static alert rules in favor of anomaly‑only detections, forcing every SOC to adopt Flighting mode as a compliance requirement. Organizations that fail to build maturity layers (tuning → context → flighting) will see a 300% increase in analyst churn as UEBA noise overwhelms them. However, those that implement the KQL joins and Logic App enrichment described above will achieve 90% reduction in false positives, making UEBA a true force multiplier rather than a placebo toggle. The future of Sentinel is not more data – it’s smarter, contextualized data with mandatory flighting gates.
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IT/Security Reporter URL:
Reported By: Bartosz Wysocki – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
