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Detect Unauthorized AI Tool Usage
Before Data Leaves Your Network

Your SIEM already collects the evidence. Turn 16,024+ classified AI domains into real-time unauthorized usage alerts.

16,024+AI Domains Classified
18Risk Categories
24hrDetection SLA
Download Free Sample Enterprise Pricing
The Detection Gap

Your Security Stack Was Not Built to Detect AI Tool Usage

Your detection capabilities cover malware, phishing, lateral movement, and exfiltration to known-bad infrastructure. But they share a critical blind spot.

Why Traditional Detection Misses Shadow AI

Unauthorized AI usage looks like authorized users making HTTPS connections to domains not on any threat feed. They transfer data matching no exfiltration signature -- and they do so intentionally.

60%+

Knowledge Workers Use Unapproved AI

Research from 2024-2025 consistently shows this figure. Without active AI governance, it climbs above 75%.

Not Rogue Actors

Engineers debugging code, marketers drafting content, analysts summarizing reports, legal teams reviewing contracts. Doing their jobs through external AI services.

A Different Detection Approach

You are not looking for malicious indicators. You need domain-level intelligence combined with SIEM correlation rules.

 Continuously-updated, classified list of AI tool domains

 SIEM correlation rules that identify usage patterns

 Risk quantification and appropriate response workflows

 No new agents, no new network taps -- uses your existing log infrastructure

The AI Tools Blocklist provides 16,024+ domains classified across 18 categories. It transforms your existing logs into an unauthorized AI detection system.

The Four Pillars of Unauthorized AI Detection

Domain Intelligence

A classified feed of 16,024+ AI tool domains that your SIEM can ingest as a lookup table. Updated daily so new tools are flagged within 24 hours of discovery.

SIEM Correlation

Pre-built search queries and correlation rules for Splunk, Microsoft Sentinel, Elastic, and QRadar that surface AI tool usage from your existing proxy and DNS logs.

Alert Framework

Tiered alert rules that classify severity based on AI tool category, data volume transferred, user role sensitivity, and time-of-day patterns.

Response Playbooks

Structured incident response workflows for SOC analysts, including triage criteria, escalation paths, and automated containment actions through SOAR integration.

SIEM Integration

Integrating the AI Tools Feed into Your SIEM

The integration follows a standard threat intelligence feed pattern. The AI Tools Blocklist CSV contains domain, category, subcategory, risk score, and metadata.

Splunk

Ingest as a CSV lookup table. Use the lookup command to enrich proxy and DNS events.

Microsoft Sentinel

Load as a Watchlist. Reference via _GetWatchlist() in KQL queries.

Elastic Security

Ingest as an enrichment index. Apply through enrich processors in ingest pipelines.

Ingest-Time vs. Search-Time Enrichment

Ingest-Time

 Faster searches

 Higher storage costs

 Requires re-ingestion on feed update

Search-Time (Recommended)

 Always uses latest feed data

 No re-ingestion needed

 Negligible performance impact for small lookup feeds

Splunk: Ingesting the AI Tools Lookup

Configure the blocklist as a lookup table in Splunk. Place the CSV in $SPLUNK_HOME/etc/apps/search/lookups/ and define it in transforms.conf.

# transforms.conf — Define the AI Tools lookup
[ai_tools_blocklist]
filename = ai_tools_blocklist.csv
match_type = WILDCARD(domain)
max_matches = 1

# props.conf — Auto-enrich proxy logs at search time
[proxy_logs]
LOOKUP-ai_tools = ai_tools_blocklist domain AS dest_domain OUTPUTNEW primary_category AS ai_category subcategory AS ai_subcategory risk_score AS ai_risk_score

This configuration auto-enriches every matching proxy log event at search time.

 Appends ai_category, ai_subcategory, and ai_risk_score fields

 Foundation for all detection queries, alert rules, and dashboards

 Schedule a 24-hour refresh cycle to keep the lookup current

Detection Queries

Splunk SPL Queries for Unauthorized AI Detection

Production-ready queries that SOC teams can deploy immediately as saved searches, scheduled reports, or alert triggers.

Query 1: Shadow AI Usage Discovery

Identifies all AI tool access over 7 days, aggregated by domain, category, and user count. Results typically reveal 40-200 distinct AI tools in active use.

`| Search proxy logs for AI tool connections over 7 days`
index=proxy sourcetype="squid:access" OR sourcetype="bluecoat:proxysg" OR sourcetype="zscaler:nss"
    earliest=-7d@d latest=now
| lookup ai_tools_blocklist domain AS dest_domain
    OUTPUTNEW primary_category AS ai_category
    subcategory AS ai_subcategory
    risk_score AS ai_risk
| where isnotnull(ai_category)
| stats dc(src_user) AS unique_users
    count AS total_requests
    sum(bytes_out) AS total_bytes_sent
    values(ai_subcategory) AS subcategories
    BY dest_domain ai_category ai_risk
| sort - unique_users
| eval data_sent_mb = round(total_bytes_sent / 1048576, 2)
| table dest_domain ai_category subcategories ai_risk
    unique_users total_requests data_sent_mb
| rename dest_domain AS "AI Tool Domain"
    ai_category AS "Category"
    unique_users AS "Unique Users"
    total_requests AS "Total Requests"
    data_sent_mb AS "Data Sent (MB)"

Query 2: High-Risk Data Exfiltration Detection

Targets HTTP POST requests to AI tool domains where outbound data exceeds a threshold. This is the most common alert trigger in production deployments.

Why POST + 10 KB Matters

POST requests above 10 KB to chatbot or code assistant domains are almost always document pastes, file uploads, or extended prompt submissions.

`| Detect large data submissions to AI tools via POST requests`
index=proxy sourcetype="squid:access" OR sourcetype="bluecoat:proxysg"
    http_method=POST bytes_out>10240
    earliest=-24h@h latest=now
| lookup ai_tools_blocklist domain AS dest_domain
    OUTPUTNEW primary_category AS ai_category
    risk_score AS ai_risk
| where isnotnull(ai_category)
    AND ai_category IN("Text & Language", "Code & Development",
        "Data & Analytics", "Autonomous Agents")
| eval data_kb = round(bytes_out / 1024, 1)
| eval severity = case(
    bytes_out > 1048576, "critical",
    bytes_out > 102400, "high",
    bytes_out > 10240, "medium",
    1==1, "low")
| stats count AS submissions
    sum(bytes_out) AS total_bytes
    max(severity) AS max_severity
    latest(_time) AS last_seen
    BY src_user dest_domain ai_category
| where submissions > 1 OR total_bytes > 102400
| sort - total_bytes
| eval last_seen = strftime(last_seen, "%Y-%m-%d %H:%M")
| table src_user dest_domain ai_category submissions
    total_bytes max_severity last_seen

Query 3: New AI Tool Adoption Trend Detection

Identifies AI tool domains that appeared for the first time in the past 48 hours. Catches new shadow AI adoption as it happens.

Viral Adoption Warning

When a previously unseen AI tool suddenly appears with multiple users, it often indicates viral word-of-mouth adoption. Rapid response prevents widespread data exposure.

`| Detect newly-adopted AI tools not seen in prior 30 days`
index=proxy sourcetype="squid:access" OR sourcetype="bluecoat:proxysg"
    earliest=-48h@h latest=now
| lookup ai_tools_blocklist domain AS dest_domain
    OUTPUTNEW primary_category AS ai_category
| where isnotnull(ai_category)
| stats dc(src_user) AS user_count
    min(_time) AS first_seen
    BY dest_domain ai_category
| join type=left dest_domain
    [search index=proxy earliest=-32d@d latest=-48h@h
    | stats count AS prior_hits BY dest_domain]
| where isnull(prior_hits) OR prior_hits = 0
| eval first_seen = strftime(first_seen, "%Y-%m-%d %H:%M")
| sort - user_count
| table dest_domain ai_category user_count first_seen
| rename dest_domain AS "New AI Tool"
    ai_category AS "Category"
    user_count AS "Users (48h)"
    first_seen AS "First Seen"

Core Detection Coverage

These three queries form the complete detection layer.

Q1

Strategic Visibility

Baseline understanding of all AI tool usage in your environment.

Q2

Tactical Alerting

Active data submission detection with severity classification.

Q3

Early Warning

New tool adoption detection before usage becomes entrenched.

Alert Framework

Tiered Alert Rules for AI Tool Activity

Not all unauthorized AI tool usage carries equal risk. Flat alerting leads to fatigue within days. Effective frameworks classify severity across multiple dimensions.

Highest Risk Categories

 Text & Language -- content submitted to AI service

 Code & Development -- source code exposed

 Autonomous Agents / Data & Analytics -- process large datasets autonomously

Lower Risk Categories

 Image & Art -- file uploads, not text-based data

 Music & Audio -- media files, not confidential content

 Design & Creative -- lower data-exposure risk

Critical Severity

 POST requests > 1 MB to high-risk categories

 Privileged users or regulated departments

 Service account or shared credential access

Response SLA: 15 minutes  |  Escalation: SOC Lead + CISO

High Severity

 POST 100 KB - 1 MB to high-risk categories

 Repeated usage (> 5 sessions in 24 hours)

 First-time access to a newly-discovered AI domain

Response SLA: 1 hour  |  Escalation: SOC Analyst Tier 2

Medium Severity

 GET-only browsing to any AI tool domain

 POST under 100 KB (short prompts or queries)

 Lower-risk categories (Design, Music, Research)

Response SLA: 24 hours  |  Escalation: SOC Analyst Tier 1

Informational

 DNS-only resolution with no HTTP connection

 Single GET to a landing or marketing page

 Approved tool from unapproved device/network

Response SLA: Weekly review  |  Escalation: Aggregated report

Splunk Alert Configuration

This saved search implements the critical-severity alert. It runs every 15 minutes with a 5-minute overlap window to prevent gaps.

`| Critical alert: large data submission to high-risk AI tools`
`| Schedule: every 15 minutes | Window: -20m to now`

index=proxy http_method=POST bytes_out>1048576
    earliest=-20m@m latest=now
| lookup ai_tools_blocklist domain AS dest_domain
    OUTPUTNEW primary_category AS ai_category
    risk_score AS ai_risk
| where isnotnull(ai_category)
    AND ai_category IN("Text & Language", "Code & Development",
        "Autonomous Agents", "Data & Analytics")
| eval data_mb = round(bytes_out / 1048576, 2)
| lookup user_risk_scores user AS src_user
    OUTPUTNEW department role access_level
| eval severity = if(access_level="privileged"
    OR department IN("Legal","Finance","Executive"),
    "critical", "high")
| table _time src_user src_ip dest_domain ai_category
    data_mb department role severity
| sendalert notable param.rule_title="AI Tool Data Exfiltration"
    param.security_domain="endpoint"
    param.severity="critical"

Alert actions you can configure on trigger:

 Create a Notable Event in Splunk Enterprise Security

 Send a PagerDuty notification

 Trigger a SOAR playbook for automated response

Microsoft Sentinel

KQL Queries for Microsoft Sentinel Environments

The AI Tools Blocklist integrates as a Sentinel Watchlist. This KQL analytic rule correlates proxy events against the AI tools watchlist to surface unauthorized usage.

// KQL: Detect data submissions to unauthorized AI tools
let ai_domains = _GetWatchlist('AIToolsBlocklist')
    | project Domain=tolower(domain),
      Category=primary_category, RiskScore=risk_score;
CommonSecurityLog
| where TimeGenerated > ago(24h)
| where RequestMethod == "POST"
    and SentBytes > 10240
| extend dest_domain = tolower(
    parse_url(RequestURL).Host)
| join kind=inner ai_domains
    on $left.dest_domain == $right.Domain
| summarize
    Submissions = count(),
    TotalBytesSent = sum(SentBytes),
    FirstSeen = min(TimeGenerated),
    LastSeen = max(TimeGenerated)
    by SourceUserName, dest_domain, Category, RiskScore
| where TotalBytesSent > 102400
    or Submissions > 5
| sort by TotalBytesSent desc

Automated Response Actions via Logic Apps

 Post alert to a Teams channel for immediate visibility

 Create a ServiceNow ticket for investigation tracking

 Invoke conditional access policy changes in real time

 Block the domain via Microsoft Defender for Cloud Apps

SIEM-Agnostic by Design

The detection architecture is identical across Splunk and Sentinel. The AI Tools Blocklist is a CSV of classified domains -- not a vendor-specific integration. Same feed, same logic, any SIEM platform.

Incident Response

Incident Response Playbook for Unauthorized AI Tool Usage

This Is Not a Traditional Security Incident

The "Threat Actor"

An employee using a web browser

The "Payload"

Company data already submitted to an external AI service

The Goal

Damage assessment, policy enforcement, and future prevention

1Phase 1: Triage and Classification

Confirm the detection and classify the incident. For critical-severity alerts, complete this phase within 15 minutes.

Analyst Triage Steps

 Verify destination is an AI tool via blocklist feed

 Review HTTP method and payload size

 Assess data exposure severity

 Identify the user and their access level

Automated Enrichment

The script below automates initial triage by pulling enrichment from the AI Tools Blocklist API and your identity provider.

#!/usr/bin/env python3
"""Automated triage for unauthorized AI tool usage alerts."""

import requests
import json
from datetime import datetime

def triage_ai_alert(alert: dict) -> dict:
    """Enrich an AI tool usage alert with context for SOC analyst."""
    domain = alert["dest_domain"]
    user = alert["src_user"]
    bytes_sent = alert.get("bytes_out", 0)

    # Classify data exposure severity
    if bytes_sent > 1_048_576:
        exposure = "critical"
        action = "immediate_block"
    elif bytes_sent > 102_400:
        exposure = "high"
        action = "investigate_and_block"
    else:
        exposure = "medium"
        action = "monitor_and_notify"

    # Look up user context from identity provider
    user_ctx = lookup_user(user)

    # Escalate if user has privileged access
    if user_ctx.get("access_level") == "privileged":
        exposure = "critical"
        action = "immediate_block"

    return {
        "incident_id": generate_incident_id(),
        "timestamp": datetime.utcnow().isoformat(),
        "domain": domain,
        "user": user,
        "department": user_ctx.get("department"),
        "data_exposure_mb": round(bytes_sent / 1_048_576, 2),
        "severity": exposure,
        "recommended_action": action,
        "status": "triaged"
    }

2Phase 2: Investigation and Scope Assessment

Determine the full scope of data exposure. Pull the user's full session history, not just the triggering event.

Investigation Questions

 Total data volume to AI tools over 30 days

 Specific AI categories used

 Habitual use vs. one-time experimentation

 Similar patterns in the same department

Critical: Regulatory Data Check

For critical-severity incidents, assess whether submitted data contained:

 PII under GDPR or CCPA

 Financial data subject to SOX controls

 Health information covered by HIPAA

Risk signal: Submissions to "Text & Language" or "Data & Analytics" by users in legal, HR, or finance departments warrant the assumption that sensitive data was involved until proven otherwise.

3Phase 3: Containment and Response

Focus on preventing further data exposure. The data has already left the network -- containment means preventing future occurrences.

Immediate Containment

 Add domain to firewall blocklist

 Notify user's manager and privacy team

 Document for potential regulatory reporting

Response Actions

 Manager-led conversation with the user

 Review user's access to sensitive data systems

 Update AI acceptable use policy if gaps found

 Privacy team assesses notification obligations

Operational Excellence

False Positive Management and Detection Tuning

Every detection program generates false positives. A well-tuned program achieves under 5% for critical alerts and under 15% for medium alerts.

Common False Positive Scenarios

Competitive Research

Marketing teams visiting AI tool websites without submitting data.

Curiosity Browsing

Employees visiting AI tool landing pages without actual usage.

Shared Infrastructure

Approved tools sharing CDN domains with unapproved tools.

Tuning Mechanisms

Approved-List Overlay

Maintain a local list of sanctioned AI tools that have passed vendor security assessment and signed DPAs.

 Exclude approved domains from alerting

 Continue logging for audit purposes

 Approve entire categories (e.g., "Search & Research")

User-Role Context

Integrate identity provider group membership into SIEM correlation rules.

 Exclude authorized engineering teams from coding tool alerts

 Apply different logic based on role and team

 Goal: zero wasted analyst time, not zero alerts

Metrics and KPIs for Detection Programs

Measuring effectiveness requires metrics beyond simple alert counts. These KPIs provide insight into program maturity and risk posture.

Shadow AI Coverage Ratio

Percentage of AI tool domains in your environment classified by your feed.

 Typically exceeds 95% with the AI Tools Blocklist. Monitor for decline indicating new tools outpacing updates.

Mean Time to Detect (MTTD)

Time between first unauthorized access and security team awareness.

 Under 30 minutes for critical events. Under 48 hours for previously unseen tools.

Policy Violation Trend

Month-over-month unique users accessing unauthorized AI tools.

 Declining trend validates enforcement. Flat or rising trend signals gaps in blocking coverage.

Automation

SOAR Integration: Automating the Response Loop

Mature SOC teams should automate the response workflow through SOAR platforms. The blocklist's classification data enables automated decisions without human intervention for low- and medium-severity events.

Automated Workflow by Severity

Step 1

Detection

SIEM rule fires and creates an incident.

Step 2

Enrichment

SOAR queries the blocklist API for full classification data.

Step 3

Routing

Playbook branches based on severity classification.

Step 4

Response

Automated actions executed per severity tier.

Informational

 Log event to usage dashboard

 Update trend analytics

 Auto-close incident

Medium Severity

 Notify manager via Slack/Teams

 Link to AI acceptable use policy

 Request usage confirmation

High / Critical

 Create case management ticket

 Post alert to SOC channel

 Auto-block domain on firewall/proxy

Continuous Feedback Loop

False Positive Confirmed

 Domain added to tuning suppression list

 Future alerts for this domain suppressed

True Violation Confirmed

 Domain added to enforcement blocklist

 User flagged for 90-day enhanced monitoring

The Key Enabler: Structured Domain Intelligence

A simple "this domain is bad" signal is insufficient for automation. The AI Tools Blocklist provides category, subcategory, risk score, and metadata for each domain -- the multi-dimensional context SOAR playbooks need to route incidents and take appropriate actions automatically.

Deploy AI Detection in Your SIEM Today

Get the AI Tools Blocklist feed with pre-built Splunk, Sentinel, and Elastic queries. Our team assists with integration and tuning for your environment.

SIEM Integration Request

Tell us your SIEM platform and proxy vendor and we will provide a tailored integration package with detection queries and alert rules.

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