geomap-visualization

name	geomap-visualization
description	Use this skill when asked to create geographic maps, visualize attack origins on a world map, show location-based data, or display IP geolocation. Triggers on keywords like "geomap", "world map", "geographic", "attack map", "show on map", "visualize locations", "attack origins", or when analyzing data with latitude/longitude coordinates.

Geomap Visualization Skill

Purpose

Generate interactive world map visualizations from Microsoft Sentinel data using the Sentinel Geomap MCP App. Geomaps display markers on a world map with coordinates, ideal for visualizing attack origins, geographic distribution of threats, or location-based security data.

---

📑 TABLE OF CONTENTS

1. Quick Start - Minimal example to get started
2. MCP Tool Reference - Parameters and schemas
3. Data Sources - Tables with native vs enriched geolocation
4. KQL Query Patterns - Ready-to-use queries by scenario
5. Enrichment Integration - Adding threat intel drill-down
6. Examples - End-to-end workflows
7. Follow-Up Investigation Queries - Queries for selected IPs
8. Interactive Selection Feature - Multi-select and chat integration

---

Quick Start

Minimal Geomap (3 Steps)

# 1. Query Sentinel for data with coordinates
mcp_sentinel-data_query_lake({
  "query": "W3CIISLog | where TimeGenerated > ago(7d) | where scStatus == '401' | summarize value = count(), lat = take_any(RemoteIPLatitude), lon = take_any(RemoteIPLongitude) by ip = cIP | where lat != 0 | project ip, lat, lon, value"
})

# 2. Display geomap
mcp_sentinel-geom_show-attack-map({
  "data": [<query results>],
  "title": "Attack Origins (Last 7 Days)",
  "valueLabel": "Failed Logins",
  "colorScale": "blue-red"
})

---

MCP Tool Reference

Tool: mcp_sentinel-geom_show-attack-map

Parameter	Required	Type	Description
data	✅	array	Array of {ip, lat, lon, value} objects
title	❌	string	Title displayed above map (default: "Attack Origin Map")
valueLabel	❌	string	Label for values (default: "Attacks")
colorScale	❌	string	blue-red (threats), green-red, or blue-yellow
enrichment	❌	array	IP enrichment data for click-to-expand panels

Data Schema

{
  "data": [
    {"ip": "101.36.107.228", "lat": 22.25, "lon": 114.15, "value": 44},
    {"ip": "193.142.147.209", "lat": 52.35, "lon": 4.92, "value": 13},
    {"ip": "170.64.158.196", "lat": -33.90, "lon": 151.19, "value": 9}
  ]
}

Enrichment Schema

{
  "enrichment": [
    {
      "ip": "101.36.107.228",
      "city": "Hong Kong",
      "country": "HK",
      "org": "AS135377 UCLOUD INFORMATION TECHNOLOGY",
      "is_vpn": true,
      "is_proxy": false,
      "is_tor": false,
      "abuse_confidence_score": 100,
      "total_reports": 4612,
      "last_reported": "2026-01-29",
      "threat_categories": ["SSH", "Brute-Force", "Web App Attack"]
    }
  

---

⚠️ CRITICAL: Complete Enrichment Requirement

When providing enrichment data, ALWAYS include ALL IPs - never a subset.

Rule: 100% Enrichment Coverage

Scenario	Correct Action
Queried 50 IPs from Sentinel	Include enrichment for ALL 50 IPs
Enriched 25 IPs	Include ALL 25 in enrichment array
Some IPs failed enrichment	Include them with empty fields, or filter from both data AND enrichment

Why This Matters

• Users click markers expecting threat intel panels
• Missing enrichment = empty panels = broken UX
• Partial enrichment misleads security analysts

Workflow to Ensure Complete Enrichment

1. Query Sentinel → Get N IPs with coordinates
2. Batch enrich IPs → python enrich_ips.py <all_ips> or python enrich_ips.py --file <ips.json>
3. Parse enrichment JSON → Extract ALL enriched entries
4. Build enrichment array → One entry per IP, matching data array exactly
5. Call geomap → Both data and enrichment arrays must have same IPs

Example: Building Complete Enrichment

import json

# Load enrichment from batch operation
with open('temp/ip_enrichment_<timestamp>.json', 'r') as f:
    raw_enrichment = json.load(f)

# Build geomap enrichment array - INCLUDE ALL
enrichment = []
for e in raw_enrichment:
    threat_cats = []
    for c in e.get('recent_comments', [])[:5]:
        threat_cats.extend(c.get('categories', []))
    
    enrichment.append({
        'ip': e['ip'],
        'city': e.get('city', 'Unknown'),
        'country': e.get('country', '??'),
        'org': e.get('org', 'Unknown'),
        'is_vpn': e.get('is_vpn') or e.get('vpnapi_security_vpn', False),
        'is_proxy': e.get('is_proxy') or e.get('vpnapi_security_proxy', False),
        'is_tor': e.get('is_tor') or e.get('vpnapi_security_tor', False),
        'abuse_confidence_score': e.get('abuse_confidence_score', 0),
        'total_reports': e.get('total_reports', 0),
        'last_reported': e.get(, [{}])[].get(, )[:]  e.get()  ,
        : ((threat_cats))[:]
    })


()  

❌ NEVER Do This

# BAD: Only including first 25 IPs
enrichment = enrichment[:25]  # WRONG

# BAD: Skipping IPs without abuse scores
enrichment = [e for e in enrichment if e['abuse_confidence_score'] > 0]  # WRONG

✅ ALWAYS Do This

# GOOD: Include all IPs, even if some fields are empty
enrichment = [transform(e) for e in raw_enrichment]  # All entries

# GOOD: If filtering, filter BOTH data and enrichment consistently
valid_ips = set(e['ip'] for e in enrichment if e.get('city'))
data = [d for d in data if d['ip'] in valid_ips]  # Filter both

---

Data Sources

Tables with Native Geolocation

Some Sentinel tables include lat/lon directly from Microsoft's GeoIP enrichment:

Table	Latitude Column	Longitude Column	Country Column
W3CIISLog	RemoteIPLatitude	RemoteIPLongitude	RemoteIPCountry
CommonSecurityLog	DeviceGeoLatitude	DeviceGeoLongitude	DeviceGeoCountry
AzureDiagnostics	varies by source	varies by source	varies by source
AzureNetworkAnalytics	SrcGeoLatitude	SrcGeoLongitude	SrcGeoCountry

Use these when available - no enrichment needed for coordinates.

Tables Requiring IP Enrichment

These tables have IP addresses but no coordinates:

Table	IP Column	Enrichment Required
SigninLogs	IPAddress	Yes - use enrich_ips.py
SecurityEvent	IpAddress	Yes - use enrich_ips.py
Syslog	extract from message	Yes - use enrich_ips.py
DeviceNetworkEvents	RemoteIP	Yes - use enrich_ips.py
OfficeActivity	ClientIP	Yes - use enrich_ips.py

Enrichment script now captures latitude and longitude from ipinfo.io.

---

KQL Query Patterns

Pattern 1: Native Geolocation (W3CIISLog)

W3CIISLog
| where TimeGenerated between (datetime(<start>) .. datetime(<end>))
| where <filter_condition>
| summarize 
    value = count(),
    lat = take_any(RemoteIPLatitude),
    lon = take_any(RemoteIPLongitude),
    country = take_any(RemoteIPCountry)
    by ip = cIP
| where lat != 0 and lon != 0  // Filter unknown locations
| project ip, lat, lon, value
| order by value desc

Pattern 2: Native Geolocation (CommonSecurityLog)

CommonSecurityLog
| where TimeGenerated between (datetime(<start>) .. datetime(<end>))
| where <filter_condition>
| summarize 
    value = count(),
    lat = take_any(DeviceGeoLatitude),
    lon = take_any(DeviceGeoLongitude)
    by ip = SourceIP
| where lat != 0 and lon != 0
| project ip, lat, lon, value
| order by value desc

Pattern 3: Enrichment Required (Extract IPs Only)

<Table>
| where TimeGenerated between (datetime(<start>) .. datetime(<end>))
| where <filter_condition>
| summarize value = count() by ip = <IP_column>
| order by value desc
| take 100

Then run enrich_ips.py to get lat/lon.

---

Scenario-Specific KQL Queries

Scenario: W3CIISLog - Failed Logins (Native Geo)

W3CIISLog
| where TimeGenerated > ago(90d)
| where Computer startswith "<honeypot_name>"
| where scStatus == "401"  // Failed auth
| where cIP != "127.0.0.1"
| summarize 
    value = count(),
    lat = take_any(RemoteIPLatitude),
    lon = take_any(RemoteIPLongitude),
    country = take_any(RemoteIPCountry)
    by ip = cIP
| where lat != 0 and lon != 0
| project ip, lat, lon, value
| order by value desc

Scenario: W3CIISLog - Web Attacks (Native Geo)

W3CIISLog
| where TimeGenerated > ago(30d)
| where tolong(scStatus) >= 400
| where csUriStem has_any ("'", "union", "select", "script", "../", "cmd.exe")
| where cIP != "127.0.0.1"
| summarize 
    value = count(),
    lat = take_any(RemoteIPLatitude),
    lon = take_any(RemoteIPLongitude)
    by ip = cIP
| where lat != 0
| project ip, lat, lon, value
| order by value desc
| take 100

Scenario: CommonSecurityLog - Firewall Blocks (Native Geo)

CommonSecurityLog
| where TimeGenerated > ago(7d)
| where DeviceAction == "Deny" or Activity has "blocked"
| summarize 
    value = count(),
    lat = take_any(DeviceGeoLatitude),
    lon = take_any(DeviceGeoLongitude)
    by ip = SourceIP
| where lat != 0 and lon != 0
| project ip, lat, lon, value
| order by value desc
| take 100

Scenario: SigninLogs - Failed Sign-ins (Requires Enrichment)

Step 1: Query IPs and values

SigninLogs
| where TimeGenerated > ago(7d)
| where ResultType != 0  // Failed
| summarize value = count() by ip = IPAddress
| order by value desc
| take 50

Step 2: Enrich IPs

python enrich_ips.py <ip1> <ip2> <ip3> ...

Step 3: Build map data from enrichment JSON (includes lat/lon)

Scenario: SecurityEvent - RDP Brute Force (Requires Enrichment)

SecurityEvent
| where TimeGenerated > ago(7d)
| where EventID == 4625
| where LogonType == 10  // RDP
| where IpAddress != "-" and IpAddress != "127.0.0.1"
| summarize value = count() by ip = IpAddress
| order by value desc
| take 50

Then enrich to get coordinates.

Scenario: DeviceNetworkEvents - Inbound Attacks (Requires Enrichment)

DeviceNetworkEvents
| where TimeGenerated > ago(7d)
| where DeviceName =~ "<device_name>"
| where ActionType in ("ConnectionSuccess", "InboundConnectionAccepted")
| where LocalPort in (3389, 22, 445, 80, 443)
| where RemoteIP !startswith "192.168." and RemoteIP !startswith "10."
| summarize value = count() by ip = RemoteIP
| order by value desc
| take 50

---

Enrichment Integration

When Coordinates Are Not in Sentinel

For tables without native geo fields, use the enrichment script:

Step 1: Run your KQL query to get IPs and values

Step 2: Enrich IPs:

python enrich_ips.py 203.0.113.42 198.51.100.10 192.0.2.1
# Or from file:
python enrich_ips.py --file temp/attack_ips.json

Step 3: Load enrichment JSON and build map data:

import json

# Load enrichment (now includes latitude/longitude from ipinfo.io)
with open('temp/ip_enrichment_<timestamp>.json', 'r') as f:
    enrichment = json.load(f)

# Build map data
map_data = []
enrichment_out = []

for e in enrichment:
    ip = e['ip']
    lat = e.get('latitude')
    lon = e.get('longitude')
    
    if lat is None or lon is None:
        continue  # Skip IPs without coordinates
    
    # Get value from your KQL results (create a lookup dict)
    value = attack_counts.get(ip, 1)
    
    map_data.append({
        'ip': ip,
        'lat': lat,
        'lon': lon,
        'value': value
    })
    
    # Build enrichment for drill-down
    threat_cats = []
    for c in e.get('recent_comments', [])[:5]:
        threat_cats.extend(c.get('categories', []))
    
    enrichment_out.append({
        'ip': ip,
        'city': e.get('city', 'Unknown'),
        'country': e.get('country', '??'),
        'org': e.get('org', 'Unknown'),
        'is_vpn': e.get('is_vpn') or e.get(, ),
        : e.get(, ),
        : e.get(, ),
        : e.get(, [{}])[].get(, )[:]  e.get()  ,
        : ((threat_cats))[:]
    })

Interactive Features with Enrichment

When enrichment is provided:

• Click any marker → Opens threat intel panel showing:
  • 📍 Location (city, country)
  • 🏢 Organization/ISP
  • 🏷️ VPN/Proxy/Tor badges
  • 📊 AbuseIPDB confidence meter
  • 📈 Total reports count
  • 🔴 Threat category tags

---

Color Scale Guide

Scale	Low Value	High Value	Best For
blue-red	Blue	Red	Threats (attacks, failures) - DEFAULT
green-red	Teal	Green	Positive activity (benign traffic)
blue-yellow	Blue	Yellow	Neutral data distributions

For threat/attack maps, always use blue-red.

---

Complete Examples

Example 1: 90-Day Honeypot Attack Map (Native Geo)

# 1. Query with native lat/lon from W3CIISLog
mcp_sentinel-data_query_lake({
  "query": "W3CIISLog | where TimeGenerated > ago(90d) | where Computer startswith '<HONEYPOT_SERVER>' | where scStatus == '401' | summarize value = count(), lat = take_any(RemoteIPLatitude), lon = take_any(RemoteIPLongitude), country = take_any(RemoteIPCountry) by ip = cIP | where lat != 0 and lon != 0 | project ip, lat, lon, value | order by value desc"
})

# 2. Enrich top IPs for threat intel drill-down
python enrich_ips.py 101.36.107.228 193.142.147.209 80.190.82.185

# 3. Display geomap
mcp_sentinel-geom_show-attack-map({
  "data": [
    {"ip": "101.36.107.228", "lat": 22.25, "lon": 114.15, "value": 44},
    {"ip": "80.190.82.185", "lat": 50.97, "lon": 6.83, "value": 44},
    {"ip": "193.142.147.209", "lat": 52.35, "lon": 4.92, "value": 13},
    {"ip": "170.64.158.196", "lat": -33.9, "lon": 151.19, "value": 9}
  ],
  "title": "Honeypot Attack Origins - 90 Day Analysis",
  "valueLabel": "Failed Logins",
  "colorScale": "blue-red",
  "enrichment": [
    {"ip": "101.36.107.228", "city": "Hong Kong", "country": "HK", "org": "AS135377 UCLOUD", "is_vpn": true, "abuse_confidence_score": 100, "total_reports": 4612, "threat_categories": ["SSH", "Brute-Force"]},
    {"ip": "193.142.147.209", "city": "Amsterdam", "country": "NL", "org": "AS213438 ColocaTel", "is_vpn": true, "abuse_confidence_score": 100, "total_reports": 30973, "threat_categories": ["Web App Attack", "Hacking"]}
  ]
})

Example 2: SigninLogs Attack Map (Enrichment Required)

# 1. Query IPs with failed sign-ins
mcp_sentinel-data_query_lake({
  "query": "SigninLogs | where TimeGenerated > ago(7d) | where ResultType != 0 | summarize value = count() by ip = IPAddress | order by value desc | take 50"
})

# 2. Enrich all IPs (script now captures lat/lon)
python enrich_ips.py <ip1> <ip2> ...

# 3. Load enrichment JSON and build map data
# (See Python code in Enrichment Integration section)

# 4. Display geomap
mcp_sentinel-geom_show-attack-map({
  "data": [<map_data from enrichment>],
  "title": "Failed Sign-In Origins (Last 7 Days)",
  "valueLabel": "Failed Attempts",
  "colorScale": "blue-red",
  "enrichment": [<enrichment_out>]
})

Example 3: Firewall Blocks (Native Geo)

# 1. Query blocked traffic with geo
mcp_sentinel-data_query_lake({
  "query": "CommonSecurityLog | where TimeGenerated > ago(24h) | where DeviceAction == 'Deny' | summarize value = count(), lat = take_any(DeviceGeoLatitude), lon = take_any(DeviceGeoLongitude) by ip = SourceIP | where lat != 0 | project ip, lat, lon, value | order by value desc | take 100"
})

# 2. Display geomap
mcp_sentinel-geom_show-attack-map({
  "data": [<query results>],
  "title": "Blocked Traffic Origins (Last 24h)",
  "valueLabel": "Blocked Connections",
  "colorScale": "blue-red"
})

---

Follow-Up Investigation Queries

When users select IPs from the geomap and click "🔍 Investigate in Chat", run these queries to provide comprehensive threat analysis. Execute queries in parallel where possible.

Multi-IP Filter Pattern

All queries use this dynamic IP filter:

let target_ips = dynamic(["<IP1>", "<IP2>", "<IP3>", ...]);

Replace with the actual IPs selected from the geomap.

---

Query 1: DeviceNetworkEvents (Network Activity)

Purpose: Show all network connections from selected IPs to any device in the environment.

let target_ips = dynamic(["<IP1>", "<IP2>", "<IP3>"]);
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
DeviceNetworkEvents
| where TimeGenerated between (start .. end)
| where RemoteIP in (target_ips)
| summarize 
    ConnectionCount = count(),
    FirstSeen = min(TimeGenerated),
    LastSeen = max(TimeGenerated),
    TargetDevices = make_set(DeviceName, 10),
    TargetPorts = make_set(LocalPort, 20),
    Actions = make_set(ActionType, 5)
    by RemoteIP
| extend Duration = LastSeen - FirstSeen
| order by ConnectionCount desc

Columns returned:

• RemoteIP: Attacker IP
• ConnectionCount: Total connections
• FirstSeen/LastSeen: Activity time range
• TargetDevices: Devices contacted
• TargetPorts: Ports targeted (LocalPort = service ports on your devices)
• Actions: Connection types (Success, Blocked, etc.)

---

Query 2: SecurityEvent (Windows Authentication)

Purpose: Show Windows authentication attempts from selected IPs.

let target_ips = dynamic(["<IP1>", "<IP2>", "<IP3>"]);
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
SecurityEvent
| where TimeGenerated between (start .. end)
| where IpAddress in (target_ips)
| where EventID in (4624, 4625, 4648, 4771, 4776)
| summarize 
    EventCount = count(),
    FirstSeen = min(TimeGenerated),
    LastSeen = max(TimeGenerated),
    TargetComputers = make_set(Computer, 10),
    TargetAccounts = make_set(Account, 20),
    LogonTypes = make_set(LogonType, 5)
    by IpAddress, EventID
| extend EventType = case(
    EventID == 4624, "Successful Logon",
    EventID == 4625, "Failed Logon",
    EventID == 4648, "Explicit Credentials",
    EventID == 4771, "Kerberos Pre-Auth Failed",
    EventID == 4776, "NTLM Auth Attempt",
    "Other")
| project IpAddress, EventType, EventCount, TargetComputers, TargetAccounts, LogonTypes, FirstSeen, LastSeen
| order by EventCount desc

Key Event IDs:

• 4624: Successful logon (ALERT: attacker got in!)
• 4625: Failed logon (brute force indicator)
• 4648: Explicit credentials used (lateral movement)
• 4771: Kerberos pre-auth failed
• 4776: NTLM credential validation

---

Query 3: W3CIISLog (Web Attacks)

Purpose: Show HTTP requests from selected IPs including attack patterns.

let target_ips = dynamic(["<IP1>", "<IP2>", "<IP3>"]);
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
W3CIISLog
| where TimeGenerated between (start .. end)
| where cIP in (target_ips)
| summarize 
    RequestCount = count(),
    FirstSeen = min(TimeGenerated),
    LastSeen = max(TimeGenerated),
    TargetServers = make_set(Computer, 10),
    URIs = make_set(csUriStem, 20),
    StatusCodes = make_set(tolong(scStatus), 10),
    Methods = make_set(csMethod, 5),
    UserAgents = make_set(csUserAgent, 5)
    by cIP
| extend AttackPatterns = case(
    URIs has_any ("'", "union", "select"), "SQL Injection",
    URIs has "script", "XSS",
    URIs has_any ("../", "..\\"), "Path Traversal",
    URIs has_any ("cmd.exe", "powershell"), "Command Injection",
    "Reconnaissance")
| project IP = cIP, RequestCount, AttackPatterns, TargetServers, StatusCodes, Methods, URIs, FirstSeen, LastSeen
| order by RequestCount desc

---

Query 4: SigninLogs (Azure AD Activity)

Purpose: Show Azure AD sign-in attempts from selected IPs.

let target_ips = dynamic(["<IP1>", "<IP2>", "<IP3>"]);
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
SigninLogs
| where TimeGenerated between (start .. end)
| where IPAddress in (target_ips)
| summarize 
    SignInCount = count(),
    SuccessCount = countif(ResultType == 0),
    FailureCount = countif(ResultType != 0),
    FirstSeen = min(TimeGenerated),
    LastSeen = max(TimeGenerated),
    TargetUsers = make_set(UserPrincipalName, 20),
    TargetApps = make_set(AppDisplayName, 10),
    ErrorCodes = make_set(ResultType, 10),
    ClientApps = make_set(ClientAppUsed, 5)
    by IPAddress
| extend SuccessRate = round(100.0 * SuccessCount / SignInCount, 1)
| project IPAddress, SignInCount, SuccessCount, FailureCount, SuccessRate, TargetUsers, TargetApps, ErrorCodes, FirstSeen, LastSeen
| order by SignInCount desc

CRITICAL: Check SuccessCount > 0 - This indicates the attacker successfully authenticated!

---

Query 5: ThreatIntelIndicators (Known Threats)

Purpose: Check if selected IPs match threat intelligence databases.

let target_ips = dynamic(["<IP1>", "<IP2>", "<IP3>"]);
ThreatIntelIndicators
| extend IndicatorType = replace_string(replace_string(replace_string(tostring(split(ObservableKey, ":", 0)), "[", ""), "]", ""), "\"", "")
| where IndicatorType in ("ipv4-addr", "ipv6-addr", "network-traffic")
| extend NetworkSourceIP = toupper(ObservableValue)
| where NetworkSourceIP in (target_ips)
| where IsActive and (ValidUntil > now() or isempty(ValidUntil))
| extend Description = tostring(parse_json(Data).description)
| where Description !contains_cs "State: inactive;" and Description !contains_cs "State: falsepos;"
| extend TrafficLightProtocolLevel = tostring(parse_json(AdditionalFields).TLPLevel)
| extend ActivityGroupNames = extract(@"ActivityGroup:(\S+)", 1, tostring(parse_json(Data).labels))
| summarize arg_max(TimeGenerated, *) by NetworkSourceIP
| project 
    IPAddress = NetworkSourceIP,
    ThreatDescription = Description,
    ActivityGroupNames,
    Confidence,
    ValidUntil,
    TrafficLightProtocolLevel,
    IsActive,
    TimeGenerated
| order by Confidence desc

Key Fields:

• Confidence: 0-100 threat confidence score
• ActivityGroupNames: APT/threat actor attribution (e.g., "PHOSPHORUS", "NOBELIUM")
• ThreatDescription: Details about the threat

---

Query 6: SecurityAlert with Incident Status

Purpose: Find security alerts that reference selected IPs, with the actual status from SecurityIncident (not the immutable alert status).

⚠️ IMPORTANT: SecurityAlert.Status is immutable ("New" at creation time). The actual status is on the SecurityIncident table. This query joins to get the real incident status.

let target_ips = dynamic(["<IP1>", "<IP2>", "<IP3>"]);
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
// Step 1: Find alerts containing target IPs as entities
let matched_alerts = SecurityAlert
| where TimeGenerated between (start .. end)
| extend EntitiesParsed = parse_json(Entities)
| mv-expand Entity = EntitiesParsed
| where Entity.["Type"] == "ip"
| extend EntityIP = tostring(Entity.Address)
| where EntityIP in (target_ips)
| summarize MatchedIPs = make_set(EntityIP) by SystemAlertId;
// Step 2: Get latest incident status for these alerts (keep AlertIds)
let incident_status = SecurityIncident
| where TimeGenerated between (start .. end)
| summarize arg_max(TimeGenerated, Status, Classification, IncidentNumber, AlertIds) by IncidentName
| mv-expand AlertId = AlertIds
| extend AlertId = tostring(AlertId)
| project AlertId, IncidentStatus = Status, Classification, IncidentNumber;
// Step 3: Join alerts with matched IPs and incident status
SecurityAlert
| where TimeGenerated between (start .. end)
| where SystemAlertId in (matched_alerts)
| join kind=leftouter matched_alerts on $left.SystemAlertId == $right.SystemAlertId
| join kind=leftouter incident_status on $left.SystemAlertId == $right.AlertId
| summarize arg_max(TimeGenerated, AlertName, AlertSeverity, Status, ProviderName, Tactics, Description, MatchedIPs, IncidentStatus, Classification, IncidentNumber) by SystemAlertId
| extend FinalStatus = coalesce(IncidentStatus, Status)  // Use incident status if available
| project 
    TimeGenerated,
    AlertName,
    AlertSeverity,
    Status = FinalStatus,
    Classification,
    IncidentNumber,
    ProviderName,
    Tactics,
    MatchedIPs,
    Description
| order by TimeGenerated desc
| take 25

Why This Matters:

• SecurityAlert.Status = "New" is the creation status (immutable)
• SecurityIncident.Status shows the current status (New/Active/Closed)
• SecurityIncident.Classification shows the closure reason (TruePositive/FalsePositive/BenignPositive)
• Alerts without incidents keep their original "New" status

Entities JSON Structure Example:

[
  {"$id":"3","HostName":"contoso-server","Type":"host"},
  {"$id":"4","Address":"203.0.113.10","Type":"ip"},
  {"$id":"5","Address":"198.51.100.20","Type":"ip"}
]

---

Query 7: DeviceProcessEvents (Process Execution Post-Compromise)

Purpose: If attacker IPs had successful connections, check for suspicious process execution.

let target_ips = dynamic(["<IP1>", "<IP2>", "<IP3>"]);
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
// First, find devices that had connections from target IPs
let compromised_devices = DeviceNetworkEvents
| where TimeGenerated between (start .. end)
| where RemoteIP in (target_ips)
| where ActionType in ("ConnectionSuccess", "InboundConnectionAccepted")
| distinct DeviceName;
// Then check for suspicious processes on those devices
DeviceProcessEvents
| where TimeGenerated between (start .. end)
| where DeviceName in (compromised_devices)
| where FileName in~ ("powershell.exe", "cmd.exe", "wscript.exe", "cscript.exe", "mshta.exe", "certutil.exe", "bitsadmin.exe", "regsvr32.exe", "rundll32.exe")
    or ProcessCommandLine has_any ("Invoke-", "IEX", "DownloadString", "WebClient", "-enc", "-encoded", "bypass", "hidden")
| project TimeGenerated, DeviceName, FileName, ProcessCommandLine, AccountName, InitiatingProcessFileName
| order by TimeGenerated desc
| take 50

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Query 8: DeviceFileEvents (Malware Drops)

Purpose: Check for file creation/modification on devices contacted by attacker IPs.

let target_ips = dynamic(["<IP1>", "<IP2>", "<IP3>"]);
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
// Find devices that had connections from target IPs
let compromised_devices = DeviceNetworkEvents
| where TimeGenerated between (start .. end)
| where RemoteIP in (target_ips)
| where ActionType in ("ConnectionSuccess", "InboundConnectionAccepted")
| distinct DeviceName;
// Check for suspicious file activity
DeviceFileEvents
| where TimeGenerated between (start .. end)
| where DeviceName in (compromised_devices)
| where ActionType in ("FileCreated", "FileModified")
| where FileName endswith_cs ".exe" or FileName endswith_cs ".dll" or FileName endswith_cs ".ps1" 
    or FileName endswith_cs ".bat" or FileName endswith_cs ".vbs" or FileName endswith_cs ".js"
| where FolderPath has_any ("\\Temp\\", "\\AppData\\", "\\Downloads\\", "\\ProgramData\\", "\\Users\\Public\\")
| project TimeGenerated, DeviceName, FileName, FolderPath, ActionType, InitiatingProcessFileName, SHA256
| order by TimeGenerated desc
| take 50

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Recommended Execution Order

When user selects IPs and clicks "Investigate in Chat":

Phase 1 (Parallel):

• Query 1: DeviceNetworkEvents
• Query 2: SecurityEvent
• Query 3: W3CIISLog
• Query 4: SigninLogs
• Query 5: ThreatIntelIndicators
• Query 6: SecurityAlert

Phase 2 (If connections found):

• Query 7: DeviceProcessEvents (post-compromise activity)
• Query 8: DeviceFileEvents (malware indicators)

Response Format:

Summarize findings with:

1. Threat Level Assessment (Critical/High/Medium/Low)
2. Attack Summary - What the IPs did, which devices/users were targeted
3. Successful Access - ALERT if any successful logins (4624) or Azure AD success (ResultType=0)
4. Threat Intel Matches - Known APT groups, malware campaigns
5. Recommendations - Block IPs, investigate users, isolate devices

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Interactive Selection Feature

The geomap supports multi-select mode for follow-up investigations:

How to Use

1. Click "☑ Select" button (top of map) to enter selection mode
2. Click markers to add/remove IPs from selection (green checkmark ✓)
3. Review selection panel showing selected IPs with enrichment summary
4. Click "🔍 Investigate in Chat" to send selected IPs for investigation

What Happens

When you click "Investigate in Chat":

1. All selected IPs are formatted with enrichment context
2. Message is sent to chat as a user message
3. LLM runs the follow-up queries above automatically
4. Results are summarized with threat assessment

Selection Panel Shows

For each selected IP:

• IP address
• City, Country
• Abuse confidence score (color-coded badge)
• Attack value from the map

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Technical Notes

• Projection: Robinson projection for accurate world map display
• Map Source: SimpleMaps.com world SVG (MIT license)
• Bundle Size: ~650 KB (includes embedded world map)
• CSP Compliance: No external resources - all assets embedded inline
• Coordinate System: Standard WGS84 (latitude: -90 to 90, longitude: -180 to 180)

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When to Use Geomaps

✅ Good Use Cases:

• Attack origin visualization (honeypots, firewalls)
• Geographic threat distribution
• Anomalous sign-in locations
• VPN/anonymization analysis across regions
• Executive briefings on global threats

❌ Skip Geomaps When:

• Fewer than 3 unique locations (too sparse)
• All IPs from same region (use heatmap instead)
• Time-based patterns needed (use heatmap)
• No geographic data available and enrichment not feasible

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Last Updated: January 29, 2026