DOVE Model & KQL: Stop Detection Noise Before It Kills Your SOC – A Step-by-Step Guide To Evaluating Rule Usefulness + Video

Introduction

Security teams often mistake more detections for better security, but duplicate alerts and overlapping rules create noise that buries genuine threats. The DOVE (Detection Overlap & Value Evaluation) model, paired with Kusto Query Language (KQL), provides a data-driven approach to identify redundant alerts across your SIEM, helping you keep only the rules that add real value.

Learning Objectives

Apply the DOVE model to quantify detection overlap and rule effectiveness in Microsoft Sentinel or any KQL‑compatible log analytics platform.
Execute a production‑ready KQL query that highlights duplicate alerts on the same assets within a one‑hour window.
Implement actionable noise‑reduction strategies, including rule tuning, suppression, and source deduplication.

You Should Know

Understanding the DOVE Model – Detection Overlap & Value Evaluation

The DOVE model answers one critical question: Is this detection adding value or just noise? It evaluates two dimensions – overlap (how many other rules fire on the same activity) and value (whether the rule catches unique threats). A rule that always fires alongside three others rarely contributes new information; it simply increases alert volume.

Step‑by‑step guide:

Collect an inventory of all active detection rules (custom and out‑of‑box) in your SIEM.
Define a time window (e.g., 1 hour) – the shorter the window, the higher the confidence in correlation.
Group alerts by common assets – user account, device name, IP address, file name, email subject, or application.
Calculate overlap score = number of distinct detection sources firing for the same asset group.
Set a threshold – for example, rules with overlap > 2 and no unique indicators should be candidates for deprecation.
Setting Up Your KQL Environment for Overlap Analysis

KQL is native to Azure Log Analytics, Microsoft Sentinel, and Azure Data Explorer. You can also run it via PowerShell or the Azure CLI. No Linux or Windows commands are needed for the query itself, but you must ensure your log sources are ingested.

Prerequisites:

Access to a Log Analytics workspace or Sentinel instance with `AlertInfo` and `AlertEvidence` tables populated.
Required permissions: `Log Analytics Reader` or equivalent.

Verification command (Azure CLI):

 List workspaces and verify Sentinel-enabled ones
az monitor log-analytics workspace list --query "[].{Name:name, Location:location}" -o table

PowerShell alternative:

 Connect to Azure and get workspace ID
Connect-AzAccount
Get-AzOperationalInsightsWorkspace

Running the Core DOVE KQL Query – Step by Step

The original query from Sergio Albea is the heart of this model. Below is an extended version with comments explaining each step.

// 1. Join alert metadata with evidence details
AlertInfo
| join kind=inner AlertEvidence on AlertId
// 2. Round timestamps to 1-hour bins for correlation
| extend DateHour = bin(Timestamp, 1h)
// 3. Aggregate per asset group and hour
| summarize 
Group_Alert_Titles = make_set(),
Different_Detection_Sources = make_set(DetectionSource),
Number_Detection_Sources = dcount(DetectionSource)
by DateHour, AccountUpn, EmailSubject, FileName, DeviceName, RemoteIP, RemoteUrl, Application
// 4. Filter for overlapping custom detections on non-empty assets
| where Number_Detection_Sources > 1 
and Different_Detection_Sources contains "Custom detection"
and (isnotempty(AccountUpn) or isnotempty(RemoteIP) or isnotempty(EmailSubject) 
or isnotempty(DeviceName) or isnotempty(RemoteUrl) or isnotempty(Application))
// 5. Sort by highest overlap first
| order by Number_Detection_Sources desc

How to use it:

Paste the query into Sentinel’s Logs blade or Log Analytics.
Replace `”Custom detection”` with your own detection source naming convention (e.g., "Microsoft Defender", "Azure ATP").
Adjust the `bin` interval to 15m for high‑volume environments or 24h for weekly reviews.
Export the results to CSV for a manual review of overlapping rule pairs.

Analyzing the Output – From Raw Data to Actionable Insights

The query returns rows where the same asset (e.g., DeviceName) triggered multiple detection sources within the same hour. For each row, you see the list of alert titles and source names.

Example row interpretation:

||–|–|–|

Step‑by‑step analysis:

Identify the asset – here, a finance department PC.
Check the alert titles – all three point to PowerShell‑based script activity.
Validate uniqueness – Do any alerts contain indicators the others miss? If no, the custom detection might be redundant.
Confirm with a sample event – Use `AlertEvidence` to retrieve the exact process command line:

AlertEvidence
| where AlertId == "your_alert_id"
| where EvidenceRole == "ProcessCommandLine"
| project CommandLine

5. Decision matrix:

Overlap = 1 : Valuable detection (no duplicates).
Overlap > 1 with unique evidence : Keep all but tune thresholds.
Overlap > 1 with identical evidence : Disable the least specific rule.

Mitigation Strategies – Tuning, Suppression, and Source Deduplication

Once you’ve identified overlapping rules, take action to reduce noise.

Option 1 – Tune rule logic (KQL / Splunk SPL / Sigma)

For a custom KQL rule that fires too often, tighten the condition. Example – original rule that catches any PowerShell execution:

// Overly broad
ProcessCreationEvents
| where ProcessName == "powershell.exe"

Better (add low‑base‑line indicators):

ProcessCreationEvents
| where ProcessName == "powershell.exe"
| where CommandLine contains "-EncodedCommand" or CommandLine contains "downloadstring"
| where not(CommandLine contains "Microsoft.UpdateServices")

Option 2 – Suppression in Microsoft Sentinel

Navigate to Analytics → Active rule → Edit → Set alert suppression.
Choose a suppression window (e.g., 6 hours, same asset).

Option 3 – Source deduplication with ingestion transformation

Use ingestion time transformation to drop duplicate events before they become alerts. Example for redundant Windows Event IDs (4624 and 4648 together):

source
| where EventID == 4624
| where not(EventID == 4648 and Account == "TrustedInstaller")

Option 4 – Decommission overlapping custom rules

If your SIEM’s out‑of‑box rules already cover a scenario (e.g., Azure ATP detects anomalous logons), disable your own custom rule that fires on the same signals.

6. Extending DOVE to Linux and Heterogeneous Environments

KQL can query Syslog, auditd, and Osquery logs from Linux endpoints. Below is a modified query for a mixed environment where `DeviceName` includes Linux hosts and `RemoteIP` captures SSH sources.

Linux‑aware overlap query:

AlertInfo
| join AlertEvidence on AlertId
| extend DateHour = bin(Timestamp, 1h)
| where OSType == "Linux" or OSType == "Windows"
| summarize Overlap = dcount(DetectionSource), Sources = make_set(DetectionSource) 
by DateHour, DeviceName, RemoteIP, ProcessName
| where Overlap > 1

Simulating an overlapping detection on Linux for testing:

Use Atomic Red Team (ART) to generate attack patterns that trigger multiple detection sources (e.g., Falco + Osquery + EDR).

 Install Atomic Red Team
git clone https://github.com/redcanaryco/atomic-red-team
cd atomic-red-team/atomics/T1059.004
 Simulate Unix shell detection
./T1059.004.yaml -execution

On Windows, use PowerShell to invoke the same technique:

Invoke-AtomicTest T1059.001 -TestNumbers 1  PowerShell download cradle

After execution, run the DOVE KQL query to see how many detection sources fired (Sysmon, Defender for Endpoint, Custom Sigma rules).

Cloud Hardening & API Security – Applying DOVE to Cloud Detections

Cloud misconfigurations and API abuse often generate overlapping alerts across CSPM, CWPP, and SIEM. Extend DOVE to Azure, AWS, or GCP logs.

Example for Azure Activity Logs (overlap between Azure Policy, Microsoft Defender for Cloud, and custom KQL):

AzureActivity
| where Category == "Administrative"
| extend DateHour = bin(TimeGenerated, 1h)
| summarize AlertSources = make_set(ResourceProvider), OverlapCount = dcount(ResourceProvider) 
by DateHour, Caller, ResourceGroup, OperationName
| where OverlapCount > 1 and AlertSources contains "Microsoft.Security"

API security overlap – If you have custom rules alerting on failed API authentication (HTTP 401) and an out‑of‑box WAF rule also triggers, apply the same logic:

SigninLogs
| where ResultType == 50057 // API authentication failure
| extend DateHour = bin(TimeGenerated, 1h)
| summarize Overlap = dcount(ConditionalAccessStatus) by DateHour, AppDisplayName, UserPrincipalName
| where Overlap > 1

Step‑by‑step cloud hardening action:

Identify overlapping cloud alerts (e.g., 3 rules firing on “Public storage account”).
Keep the most specific rule (e.g., the one that includes PublicAccess == "Container"), disable the others.
Create a suppression exception for routine administrative operations (e.g., Terraform runs).

What Undecode Say

Key Takeaway 1: Alert duplication is a silent productivity killer – the DOVE model gives you a repeatable, data‑driven method to measure it.
Key Takeaway 2: KQL is not just for hunting; it is an essential governance tool for detection engineering. The provided query can be adapted to any SIEM with join capabilities (Splunk subsearch, SQL on logs).
Key Takeaway 3: Noise reduction is a continuous process – schedule the DOVE query weekly and automatically tag overlapping rules for review.
Analysis: Many SOCs blindly accumulate rules without evaluating overlap. This leads to analyst burnout and missed true positives buried under duplicates. The DOVE+KQL approach shifts from reactive firefighting to proactive detection hygiene, lowering MTTD (Mean Time to Detect) by reducing false positive volume. Moreover, it aligns with Zero Trust’s “assume breach” philosophy – every alert should earn its place by providing unique visibility. As threat actors reuse TTPs across multiple attack surfaces, overlapping rules become inevitable; DOVE helps you keep only the high‑quality, low‑overlap signals.

Prediction

The future of detection engineering will be AI‑assisted overlap analysis – where LLMs parse rule logic (YARA, Sigma, KQL) and predict duplication before the rule is even deployed. DOVE will evolve into an automated CI/CD gate for SIEM content, rejecting rules that exceed a configurable overlap threshold. As cloud and hybrid environments generate petabytes of logs, manual rule evaluation will become impossible. Teams that adopt structured models like DOVE today will be the ones equipped to train the next generation of autonomous detection agents.

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