Overview

Checkpoint provides comprehensive analytics for understanding the AI agent and bot traffic hitting your applications. View real-time detections, analyze trends, and export data for reporting.

Detection Monitor

The Monitor tab provides a real-time feed of detections as they happen.

Each entry shows:

Field	Description
Timestamp	When the detection occurred
Detection Class	`human`, `ai_agent`, `bot`, or `incomplete_data`
Confidence	Score from 0–100
Agent Name	Identified agent (e.g., ChatGPT, Googlebot)
IP Address	Source IP of the request
User Agent	The request's user agent string
Path	URL path that was accessed
Session	Consolidated session identifier

Filtering

Filter the monitor feed by:

Detection class (AI agent, bot, human)
Confidence range
Time period
Agent name
IP address

Detections are grouped into sessions. Checkpoint consolidates requests from the same visitor into a single session, even if the client-side session ID resets (which happens every 30 minutes for privacy). This gives you an accurate count of unique visitors rather than inflated session numbers.

Session consolidation uses multiple signals (fingerprint, IP address, user agent, agent type) to group related requests within 30-minute windows.

Analyze

The Analyze tab provides aggregated analytics across your detection data.

Key Metrics

Metric	Description
Total Sessions	Unique visitor sessions in the selected period
Detection Rate	Percentage of sessions classified as agents or bots
AI Agent Sessions	Sessions classified as AI agents
Bot Sessions	Sessions classified as traditional bots
Human Sessions	Sessions classified as human visitors

Agent Type Breakdown

A visual breakdown of traffic by detection class:

AI Agents — ChatGPT, Claude, Perplexity, Gemini, and other AI assistants
Bots — Googlebot, Bingbot, scrapers, and automated tools
Humans — Regular browser traffic
Incomplete Data — Requests with insufficient signals

Traffic Trends

Historical charts showing detection patterns over time:

Detection volume by day/week/month
Classification distribution over time
Confidence score distribution
New vs returning agents

Top Agents

Ranked list of the most frequently detected agents, showing:

Agent name
Detection count
Average confidence score
First and last seen dates

Date Range Selection

All analytics views support custom date ranges:

Last 24 hours — Recent activity
Last 7 days — Weekly overview
Last 30 days — Monthly trends
Custom range — Pick specific start and end dates

Detection Rate = (AI Agent Sessions + Bot Sessions) / Total Sessions × 100

A typical detection rate varies by industry:

Industry	Typical Detection Rate
E-commerce	30–50%
Content/Media	40–60%
SaaS	20–40%
API	50–70%

Confidence Distribution

Review the distribution of confidence scores to tune your enforcement policies:

High cluster (80–100) — Strong detections, safe to enforce
Medium cluster (50–79) — Review manually, consider logging only
Low cluster (0–49) — Likely noise, avoid enforcing

Use the confidence distribution to choose your enforcement threshold. Start by logging everything, then set your block threshold based on where the clusters fall.

Policy Tuning Workflow

Start in detect mode (no enforcement)
Review Monitor for false positives
Analyze confidence score distribution
Set enforcement threshold (typically 80+)
Enable enforcement with tuned policy
Monitor continuously for changes

Next Steps

Dashboard Overview — Navigate the full dashboard
Managing Projects — Project setup and configuration
Policies — Configure enforcement based on analytics

Analytics & Reporting