Log noise hides real attacks
SQL injection strings, XSS payloads, bad bots, and credential attacks are buried inside normal API traffic.
AI Intrusion Detector for web apps and APIs.
IntrusionDetector.ai helps you detect intrusions in real time using AI, including attacks traditional IDS tools can miss. Your team can miss real attacks when dangerous requests are buried inside noisy server logs
AI
Real-time detection
0
Missed critical attacks
20
Free early users
93
Suspicious login abuse
Multiple failed sign-ins, unusual user agent, and risky IP behavior. Alert created. Real-time event monitoring
Application-aware attack context
Redacted request telemetry
Alert-only investigation queue
The problem
Most intrusion detection is too slow, too noisy, or too expensive.
Attackers do not wait for your team to manually read logs. A useful AI intrusion detector must separate real risk from junk fast.
Manual reviews do not scale
If a human has to inspect every suspicious request, your intrusion detector is already behind.
Generic alerts are weak
"Suspicious activity detected" is not enough without severity, evidence, category, and context.
The guide
IntrusionDetector.ai gives your API team the signal they need.
We help builders, SaaS teams, and security-conscious product teams detect suspicious API events without turning their application into a brittle blocking maze.
Send structured API activity, request metadata, user context, and suspicious payload details.
Receive risk score, severity, attack category, explanation, and alert status.
Prioritize actual attack patterns instead of drowning in raw logs.
Severity
Critical
Risk
93/100
AI summary
Likely login abuse targeting an authentication endpoint. The request pattern shows repeated failures, suspicious metadata, and behavior that should be investigated.
Why this exists
Traditional IDS sees traffic. IntrusionDetector.ai sees application behavior.
Modern web and API attacks often look like normal requests. A simple GET /api/orders/9281 is harmless until the application context says User A tried to access User B's order, was denied, and kept probing object IDs.
That is the blind spot this product attacks: suspicious behavior inside your app, not just scary strings in traffic.
Traffic-only tools miss context
They may see a route, IP, method, and payload. Useful, but not enough for SaaS abuse.
Application-aware detection adds meaning
User, tenant, object ownership, permissions, sessions, tokens, response status, route sensitivity, and behavior over time.
Runtime telemetry
Send safe request context. Get risk, severity, category, and recommendations.
The lightweight SDK or direct API ingestion captures security-relevant HTTP and API activity without forcing your application to wait on the detector.
Request signal
HTTP method, path, query parameters, selected headers, source IP, user agent, response status, response time, service, environment, and project.
Business context
User, tenant, object, permission, ownership, token, session, origin, and workflow metadata when your app provides it.
Safe by design
Passwords, authorization headers, cookies, tokens, credit cards, and secrets should be redacted or excluded before storage.
Fail-open SDK
Django middleware, Flask integration, manual Python client, direct HTTP ingestion, and custom integrations for non-Python apps.
Threat coverage
Detect the attacks that hide inside normal-looking web and API traffic.
The detector combines local security rules, behavioral detection, application metadata, historical activity, risk scoring, optional AI analysis, alert grouping, and human-readable recommendations.
Injection and XSS
SQL injection, command injection, template injection, code evaluation payloads, reflected XSS, script tags, JavaScript URI payloads, SVG and iframe indicators.
Path, file, and CMS probing
Path traversal, .env, .git, backup files, database dumps, phpinfo, WordPress, Joomla, Drupal, Magento, PrestaShop, and TYPO3 probing.
API reconnaissance and SSRF
Swagger, OpenAPI, GraphQL, private routes, internal APIs, localhost targeting, internal IPs, cloud metadata endpoints, and non-HTTP protocol abuse indicators.
Auth, token, and session abuse
Login probing, repeated failures, credential stuffing indicators, password reset probing, token endpoint abuse, replay indicators, and unusual session reuse.
Authorization and business logic abuse
BOLA, IDOR, cross-tenant access attempts, object ownership mismatch, permission bypass attempts, object enumeration, and post-auth workflow abuse.
Bots, scanners, and chained signals
SQLMap, Nikto, Nmap, Masscan, WPScan, Gobuster, FFUF, Burp indicators, repeated 404/401/403 probing, route fan-out, and low-and-slow attacks.
Who it is for
Built for teams that cannot afford enterprise bloat or blind production apps.
Developers
Get clear alerts with what happened, why it matters, risk score, indicators, route, source IP, and the next investigation step.
SaaS companies
Monitor tenant-aware abuse, object access abuse, suspicious authenticated behavior, and risky workflows across dashboards and APIs.
Small teams
Add runtime security visibility without deploying a heavy enterprise API security platform or drowning in raw logs.
Agencies
Install the SDK across client projects and give each client their own dashboard, API key, events, alerts, and visibility.
Three-step setup
Go from noisy logs to live intrusion alerts.
Install the SDK, connect your project, and watch suspicious web and API activity turn into alerts your team can act on fast.
Install our SDK
Add the IntrusionDetector.ai SDK to your web app so security-relevant requests and authentication events can be monitored.
Set up your API key
Create a project, add your API key, and send events securely from your backend, middleware, edge function, or gateway.
Monitor the live dashboard
See events, risk scores, and alerts in one place so your team can act fast against today’s smart attackers.
Use cases
Where an intrusion detector pays for itself.
Use it where API abuse can cost you data, trust, uptime, or money.
Injection attempts
Detect suspicious SQL-like payloads, malformed queries, and attack strings in request fields.
Login abuse
Spot brute force patterns, repeated failures, strange user agents, and risky authentication traffic.
Scanner behavior
Identify bots probing sensitive paths, admin routes, environment files, and unsupported methods.
XSS payloads
Flag script injection attempts and unsafe payload patterns before they become customer-facing damage.
Product experience
From noisy traffic to a calm investigation queue.
The app is organized around the moments that matter: monitoring, triage, grouped alerts, investigation detail, and redacted evidence.
See risk trends before the incident channel gets noisy.
The dashboard gives teams a fast read on event volume, open alerts, severity distribution, and active projects without forcing every request through a blocking path.
Pair raw event streams with alerts that matter.
Operators compare recent risky events against deduplicated alerts, then move from noisy telemetry to the items that deserve review.
Explain the suspicious request, not just that it exists.
Every event detail page shows the AI model, latency, alert-only action, grouped status, summary, and recommendation.
Interactive demo
Test the AI intrusion detector API.
Use mock mode for a safe preview, or paste a test project API key to send a real event and see how AI turns suspicious activity into an alert.
response.json
Click "Mock analysis" or send a real event.
API-first
Works through a direct event endpoint, so you can integrate from almost any stack.
Signal > noise
The goal is not more dashboards. The goal is fewer blind spots.
Launch free
The first 20 users get free access while the product grows.
FAQ
AI intrusion detector FAQ
Straight answers for teams choosing what to monitor next.
What is an AI intrusion detector?
An AI intrusion detector analyzes event data, request metadata, payload patterns, and behavior signals to detect likely attacks. Instead of only storing logs, it scores risk and explains why an event looks suspicious.
How is IntrusionDetector.ai different from a normal intrusion detector?
A traditional intrusion detector often relies heavily on static signatures and generic rules. IntrusionDetector.ai focuses on API events, AI intrusion detection, risk scoring, attack classification, and human-readable summaries.
Is this an AI intrusion detection system or a firewall?
It is an AI intrusion detection system for visibility and alerting. It helps you understand suspicious API activity before you decide what to block, rate-limit, or investigate.
Can I integrate it with any backend?
Yes. Use Django middleware, Flask integration, a manual Python client, direct HTTP ingestion, or a custom integration for non-Python apps. The core workflow is API-first: send a JSON event to /api/v1/events/ with your project key in the X-AID-Key header.
Does it block traffic?
No. IntrusionDetector.ai is alert-only by design. That is a safer adoption path because teams can observe, investigate, tune thresholds, and then decide what to block or rate-limit in their own stack.
What sensitive data should I avoid sending?
Do not send passwords, authorization headers, cookies, raw tokens, credit cards, secrets, or private data that your team does not need for security investigation. Redact or exclude those fields before storage.
Is "ai intruion detection system" the same thing?
That phrase is usually a misspelling of "AI intrusion detection system." IntrusionDetector.ai is built for that exact use case: detecting suspicious API events with AI-assisted risk analysis.
Is there a free plan?
Yes. IntrusionDetector.ai is free for the first 20 users during the early access launch. After that, pricing can evolve based on usage, event volume, and team needs.
Early access
Stop pretending raw logs are a security strategy.
Use an AI intrusion detector that gives you risk, context, and a reason to act.