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SIEM Integration

Your SIEM Is Blind to AI Tools.
Fix That in Minutes.

Most SIEMs have zero visibility into 16,024+ AI tool domains employees use daily. Our domain intelligence feed plugs directly into Splunk, QRadar, Microsoft Sentinel, and Elastic SIEM for immediate shadow AI detection.

16,024+AI Domains for SIEM Lookup
4Major SIEM Platforms
18AI Tool Categories
Download Free Sample Enterprise Pricing
The Visibility Gap

Why Your SOC Cannot Detect What It Cannot Classify

Your SIEM excels at detecting malware C2 traffic, credential stuffing, and lateral movement. But it completely ignores the AI tools employees use to submit proprietary data, source code, and customer records every day.

The root cause is a classification problem. Your SIEM can only alert on what it can identify.

What Your SIEM Sees Today What Your SIEM Misses
Malware C2 callbacks to known-malicious IPs Employee pasting source code into AI chatbots
Data exfiltration to threat-intel flagged domains POST requests uploading customer data to AI analytics tools
Credential stuffing and brute-force attempts Developers using unauthorized AI code assistants
URLs categorized as "Malicious" or "Phishing" AI tools hidden under generic labels like "Technology" or "Business"

Standard URL categorization databases group AI tools under generic labels like "Technology." They cannot distinguish ChatGPT from GitHub, or an AI code assistant from a text editor.

The Missing Classification Layer

The AI Tools Blocklist provides 16,024+ domains, each tagged with one of 18 functional categories, updated daily. Load it into your SIEM as a lookup table, reference set, or threat indicator.

With AI Domain Intelligence, Your SOC Can:

Run a single query to surface every AI tool access event across all log sources
Filter by category to focus on high-risk tool types like code generation or data analytics
Correlate AI tool usage with sensitive data movement to identify data loss events
Detect unauthorized AI tool adoption within hours of first access

Unclassified AI Traffic

Standard URL databases label AI tools as "Technology" or "Information Technology." The AI Tools Blocklist provides 18-category granularity that turns noise into signal.

Data Submission Blind Spots

POST requests to AI tools carry the highest risk. Without domain classification, your SIEM cannot differentiate data submissions to AI tools from ordinary API calls, making shadow AI detection impossible.

Splunk Integration

Splunk: Lookup Tables, Detection Searches, and Dashboards

Splunk's CSV lookup mechanism is the fastest integration path. Import the domain feed, and every search across proxy, DNS, and firewall logs gets real-time AI classification enrichment.

Setup in 3 Steps

1
Upload the CSV lookup file Navigate to Settings > Lookups > Lookup table files and upload ai_tools_blocklist.csv.
2
Create a lookup definition Map the domain field so that any SPL search can use the lookup command for enrichment.
3
Enrich events with AI tool name, category, and risk score Use | lookup ai_tools_blocklist domain AS dest_domain in any search to add AI context. 
| --- Splunk: AI Tool Detection Searches ---
| 1. Enrich proxy logs with AI domain intelligence

index=proxy sourcetype="squid:access" OR sourcetype="bluecoat:proxysg"
| eval dest_domain=lower(coalesce(url_domain, dest_host, uri_host))
| lookup ai_tools_blocklist domain AS dest_domain
    OUTPUT tool_name, primary_category, risk_score
| where isnotnull(tool_name)
| stats count AS access_count,
    sum(bytes_out) AS total_bytes_sent,
    dc(src_user) AS unique_users,
    values(primary_category) AS categories,
    latest(_time) AS last_seen
    BY dest_domain, tool_name, risk_score
| sort - total_bytes_sent
| table dest_domain, tool_name, categories, risk_score,
    access_count, unique_users, total_bytes_sent, last_seen

| 2. Alert: High-risk data submission to AI tools
| Schedule: every 15 minutes | Alert: when results > 0

index=proxy sourcetype="squid:access"
    http_method=POST bytes_out>5120 earliest=-15m
| eval dest_domain=lower(url_domain)
| lookup ai_tools_blocklist domain AS dest_domain
    OUTPUT tool_name, primary_category, risk_score
| where isnotnull(tool_name) AND risk_score>=70
| eval severity=case(
    bytes_out>102400 AND risk_score>=90, "critical",
    bytes_out>51200 OR risk_score>=80, "high",
    1==1, "medium")
| table _time, src_user, src_ip, dest_domain, tool_name,
    primary_category, risk_score, bytes_out, severity

| 3. Behavioral baseline: detect new AI tool adoption

index=proxy sourcetype="squid:access" earliest=-1d
| eval dest_domain=lower(url_domain)
| lookup ai_tools_blocklist domain AS dest_domain
    OUTPUT tool_name, primary_category
| where isnotnull(tool_name)
| stats earliest(_time) AS first_seen, dc(src_user) AS user_count
    BY dest_domain, tool_name, primary_category
| join type=left dest_domain
    [| inputlookup ai_tool_baseline.csv]
| where isnull(baseline_first_seen)
| eval alert_msg="New AI tool detected: ".tool_name
    ." (".primary_category.") accessed by "
    .tostring(user_count)." users"
| table dest_domain, tool_name, primary_category,
    first_seen, user_count, alert_msg

| 4. Executive dashboard panel: AI tool usage by category

index=proxy sourcetype="squid:access" earliest=-7d
| eval dest_domain=lower(url_domain)
| lookup ai_tools_blocklist domain AS dest_domain
    OUTPUT tool_name, primary_category, risk_score
| where isnotnull(tool_name)
| stats count AS events,
    dc(src_user) AS users,
    dc(dest_domain) AS tools,
    sum(eval(if(http_method="POST",1,0))) AS data_submissions,
    avg(risk_score) AS avg_risk
    BY primary_category
| eval avg_risk=round(avg_risk,1)
| sort - events
| rename primary_category AS "AI Category",
    events AS "Total Events",
    users AS "Unique Users",
    tools AS "Distinct Tools",
    data_submissions AS "Data Submissions",
    avg_risk AS "Avg Risk Score"

What Each Search Does

Search 1: Inventory View

Lists every AI tool accessed, access frequency, unique users, and total data sent. Your baseline visibility report.

Search 2: Data Submission Alert

Fires every 15 minutes when an employee POSTs >5 KB to a high-risk AI tool. Severity tiers by payload size and risk score.

Search 3: New Tool Detection

Detects AI tools not in your baseline. The earliest indicator of unauthorized AI tool adoption.

Search 4: Executive Dashboard

Summarizes usage by AI tool category for weekly reporting. Shows event counts, unique users, and data submission volume.

Splunk Enterprise Security (ES) Integration

Load the AI Tools Blocklist as a threat intelligence feed using the threatintel framework. AI tool access events automatically contribute to per-user and per-asset risk scores.

Configure the feed as a domain indicator type in ES's threat intelligence management. The correlation search Threat Activity Detected automatically flags users whose AI activity exceeds your risk threshold.

QRadar & Sentinel

IBM QRadar: Reference Sets, Custom Rules, and Offense Tuning

QRadar's in-memory reference sets are ideal for high-volume domain matching. They enable real-time evaluation of every event against the AI Tools Blocklist with minimal performance impact.

QRadar Setup Steps

1
Create the reference set and reference map Create AI_Tool_Domains (type ALNIC, case-insensitive) and AI_Tool_Metadata with keys for domain, category, and risk_score.
2
Populate from the daily-updated CSV feed Use the QRadar API or a scheduled script to load domains from the blocklist into both data structures.
3
Create custom detection rules In Use Case Manager: match log source group "Firewall and Proxy" AND destination URL host contained in AI_Tool_Domains.
4
Configure offense response and severity mapping Category "Policy Violation," severity mapped from risk_score. Index offenses by source user to prevent offense sprawl.
5
Add a data submission escalation rule When event payload >10,000 bytes AND matches the first rule, escalate severity to High with annotation "Potential data submission to AI tool."

QRadar SOAR Integration

Map AI tool offenses to a dedicated playbook that automatically enriches with tool category, queries Active Directory for user department and manager, and creates a ticket for high-risk categories.

Auto-escalate for "Code Generation," "Data Analytics," and "AI Agents" categories. Reduces mean time from detection to investigation from hours to seconds.

Microsoft Sentinel: KQL Analytics Rules and Workbooks

Sentinel's native integration with Defender for Endpoint, Entra ID, and Purview makes it ideal for AI tool detection in Microsoft-centric environments. The AI Tools Blocklist imports as a watchlist with sub-second KQL query performance.

Sentinel Watchlist Setup

1
Create the watchlist Navigate to Sentinel > Configuration > Watchlist. Create AIToolsBlocklist with SearchKey set to the domain column.
2
Upload the CSV Once ingested, the watchlist is available to all KQL queries via _GetWatchlist('AIToolsBlocklist').
3
Deploy the analytics rule below Detects AI tool access and data submissions across DNS, proxy, and Defender for Endpoint device network events. 
// Microsoft Sentinel Analytics Rule: AI Tool Access Detection
// Frequency: 15 minutes | Lookback: 20 minutes | Severity: Medium-High

let AIToolDomains = _GetWatchlist('AIToolsBlocklist')
    | project Domain=tolower(domain),
             ToolName=tool_name,
             Category=primary_category,
             RiskScore=toint(risk_score);
// Query 1: DNS-based detection (broad coverage)
let DNSMatches = DnsEvents
    | where TimeGenerated > ago(20m)
    | extend QueryDomain = tolower(Name)
    | join kind=inner AIToolDomains
        on $left.QueryDomain == $right.Domain
    | project TimeGenerated, Computer, ClientIP,
             QueryDomain, ToolName, Category, RiskScore,
             DetectionSource="DNS";
// Query 2: Proxy/web traffic (richer context)
let ProxyMatches = CommonSecurityLog
    | where TimeGenerated > ago(20m)
    | where DeviceVendor in ("Palo Alto Networks",
        "Zscaler", "Fortinet", "Blue Coat")
    | extend DestDomain = tolower(
        coalesce(DestinationHostName, RequestURL))
    | join kind=inner AIToolDomains
        on $left.DestDomain == $right.Domain
    | extend DataSubmission = (RequestMethod == "POST"
        and SentBytes > 5120)
    | project TimeGenerated, SourceUserName, SourceIP,
             DestDomain, ToolName, Category, RiskScore,
             RequestMethod, SentBytes, DataSubmission,
             DetectionSource="Proxy";
// Query 3: Defender for Endpoint device network events
let MDEMatches = DeviceNetworkEvents
    | where Timestamp > ago(20m)
    | extend DestDomain = tolower(RemoteUrl)
    | join kind=inner AIToolDomains
        on $left.DestDomain == $right.Domain
    | project TimeGenerated=Timestamp, DeviceName,
             InitiatingProcessAccountName,
             DestDomain, ToolName, Category, RiskScore,
             DetectionSource="MDE";
// Union all sources and generate alerts
DNSMatches
| union ProxyMatches, MDEMatches
| summarize
    AccessCount = count(),
    DataSubmissions = countif(DataSubmission == true),
    FirstSeen = min(TimeGenerated),
    LastSeen = max(TimeGenerated),
    Sources = make_set(DetectionSource)
    by ToolName, Category, RiskScore, DestDomain
| where RiskScore >= 60
    or DataSubmissions > 0
    or AccessCount >= 10
| extend AlertSeverity = case(
    DataSubmissions > 0 and RiskScore >= 80, "High",
    RiskScore >= 80, "Medium",
    AccessCount >= 50, "Medium",
    "Informational")
| sort by AlertSeverity asc, DataSubmissions desc

What This Analytics Rule Does

Queries three log sources: DNS events, proxy/firewall logs (Common Security Log), and Defender for Endpoint device network telemetry
Unions results for maximum detection coverage regardless of which log source captures the access
Flags POST requests with payloads >5 KB as DataSubmission — the highest-risk interactions
Tiers severity: data submissions to high-risk tools → High; general access → Informational
Prevents alert fatigue while ensuring zero critical events are missed

Sentinel Workbook: 4 Recommended Tabs

Create an "AI Tool Monitoring" workbook for shadow AI detection investigations and regulatory reporting.

Overview

Daily AI tool access trends with time range selectors and category filters

User Breakdown

Which employees access the most AI tools and how frequently

Category Analysis

Which types of AI tools are most popular across the organization

Data Submissions

Highest-risk interactions with payload details and user attribution

Elastic SIEM

Elastic Security: Detection Rules, Enrichment Pipelines, and Anomaly Detection

Elastic Security's detection engine evaluates network events against enrichment indices. The recommended approach enriches events at index time for zero query-time overhead and sub-second alerting at scale.

Elastic Setup Steps

1
Index the blocklist data Load the AI Tools Blocklist into ai-tools-blocklist index. Schedule daily downloads via the blocklist API.
2
Create an enrich policy Map the domain field to AI tool metadata (tool name, category, risk score).
3
Attach to an ingest pipeline Process proxy and DNS logs through the pipeline. Every matching event gets AI metadata before indexing.
4
Create detection rules Deploy both deterministic query rules and ML anomaly detection jobs for layered coverage.

Detection Approach Comparison

Aspect Custom Query Rules ML Anomaly Detection
Type Deterministic alerting on known patterns Behavioral anomaly detection
Use case POST requests to high-risk AI tools Users suddenly accessing new AI tool categories
High-severity rule ai_tool.risk_score >= 70 AND http.request.method: "POST" AND http.request.bytes > 5120 — every 5 min high_count detector on ai_tool.name, partitioned by user.name, 1-hour bucket span
Low-severity rule ai_tool.name: * — every 30 min, risk score 21 Per-user baselines for access frequency
Strength Catches every known-risk event Surfaces subtle behavioral shifts deterministic rules miss

Ingest Enrichment

Enrich events at index time using an Elasticsearch enrich policy. Every proxy, DNS, and firewall event is tagged with AI metadata before indexing.

ML Anomaly Detection

Per-user and per-department baselines surface behavioral shifts: new tool adoption, data upload spikes, and off-hours access patterns.

Detection Rule Tiering

Critical for data exfiltration, high for data submissions, medium for new tool adoption, low for access monitoring. Zero critical events missed.

Log Source Configuration

Configuring Log Sources for Maximum AI Tool Visibility

AI tool detection quality depends on your log sources. Most default logging configurations exclude the fields needed for effective detection.

Three critical data elements must be captured: the full destination domain or URL, the HTTP method (GET vs POST), and the request payload size.

Proxy Logs: The Primary Detection Source

Web proxy logs are the richest data source — full URL, HTTP methods, payload sizes, and user identity. Ensure these fields are logged:

Squid: %>a (client IP), %un (username), %rm (HTTP method), %ru (URL), %>st (bytes sent)
Zscaler ZIA: Enable "Full URL Logging" under Administration > Nanolog Streaming Service. Include hostname, url, requestmethod, requestsize, login
Palo Alto: Forward URL Filtering logs with the misc field (full URL) and bytes_sent. Requires SSL decryption.

DNS Logs: Broad Coverage Without Decryption

DNS logs capture every AI tool access attempt, since every connection starts with a DNS resolution. Limited to domain visibility only — no payload data.

Microsoft DNS: Enable analytical logging via Event Tracing for Windows (ETW). Forward via Windows Event Forwarding or SIEM agent.
Cisco Umbrella: Configure S3 log export and ingest via the SIEM's S3 input module.
InfoBlox: Configure the SIEM syslog forwarder with query log category enabled.

Endpoint Telemetry: Last-Mile Visibility

EDR platforms capture AI tool access even when network controls are bypassed — personal hotspots, VPN split tunneling, etc.

Defender for Endpoint: DeviceNetworkEvents table captures outbound connections with process-level context
CrowdStrike Falcon: DNS Request and HTTP Request events provide similar network-level visibility
Apply the same enrichment: Use the AI Tools Blocklist lookup on endpoint telemetry for consistent detection

The combination of proxy, DNS, and endpoint logs creates a three-layer detection mesh. AI tool usage becomes extremely difficult to evade regardless of network path.

Network-Based Sources

Proxy logs (Squid, BlueCoat, Zscaler), firewall URL filtering logs (Palo Alto, Fortinet, Check Point), and DNS query logs. Best with centralized egress and SSL decryption.

Endpoint-Based Sources

EDR network telemetry (Defender for Endpoint, CrowdStrike Falcon, SentinelOne), browser extension telemetry, and OS-level DNS client logs. Captures access even when network controls are bypassed.

Correlation & Triage

Multi-Event Correlation Rules and Alert Triage Workflows

Single-event detection generates noise that overwhelms any SOC. Effective monitoring requires multi-event correlation that combines domain intelligence, network telemetry, and identity context into high-confidence alerts.

The goal: surface data submissions to high-risk tools, sudden adoption changes, and policy-violating usage by privileged accounts — while suppressing low-risk browsing activity.

Correlation Rule Examples

Rule 1: Sensitive Role + High-Risk AI Tool

Triggers when Finance, Legal, HR, or Executive users access AI tools with risk score >75. Medium severity by default, escalates to High on POST >10 KB.

Rule 2: Bulk Data Submission

Triggers when a single user submits >100 KB to AI tools within one hour. Detects systematic document or dataset uploads requiring DLP investigation.

Rule 3: New Category Adoption

Triggers when a user accesses an AI tool category they've never used before. A developer using AI healthcare tools may indicate credential compromise or policy violation.

Rule 4: After-Hours AI Tool Access

Triggers on AI access outside normal working hours. Combined with high-risk categories or large data submissions, this is a strong insider threat indicator.

Analyst Triage Playbook

When an AI tool alert fires, analysts should follow a structured workflow for consistent investigation quality and compliance documentation.

Phase 1: Context

Identify the user, department, role, and data access level. Query AD and HR systems. Check if the tool is sanctioned or prohibited.

Phase 2: Risk Assessment

Evaluate risk score and category from blocklist metadata. Assess data volume submitted. Cross-reference with risk assessment scoring.

Phase 3: Respond & Document

Informational → log. Medium → notify user. High → escalate to manager. Critical → immediate access revocation and incident response.

Document the playbook in your SOAR platform and link it directly from the SIEM alert. Tier 1 analysts should access it in one click for consistent triage across shifts and skill levels.

Dashboard Design

Building SIEM Dashboards for AI Tool Monitoring

Your dashboard should serve three audiences: SOC analysts (real-time triage), security managers (weekly trends), and compliance teams (regulatory reporting evidence).

Top-Level: 5 Core Panels

Total AI tool access events counter with 7-day trend sparkline — primary adoption velocity indicator
Data submission events counter — only POST requests above the payload threshold (highest-risk interactions)
Unique users counter with period-over-period comparison to highlight growth
Category breakdown chart showing access volume by AI tool category for adoption analysis
Top AI tools table ranked by access volume, data submissions, and unique users

Detailed Analysis: 3 Drill-Down Views

User activity heatmap — AI tool access by hour-of-day and day-of-week to reveal after-hours usage patterns
Department breakdown chart — maps usage to organizational units for targeted training or controls
Blocked vs. allowed panel — the most important compliance metric, proving active enforcement across 16,024+ AI tool domains

Access Trends

Daily and weekly AI tool access volume with category breakdown sparklines

Data Submissions

POST request tracking with payload size distribution and user attribution

User Analytics

Per-user and per-department AI tool adoption metrics with role-based risk scoring

Enforcement Metrics

Blocked vs. allowed ratio, policy hit rates, and exception tracking for audit evidence

Integrate AI Tool Detection Into Your SIEM

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