AI Security

"We got breached because an attacker logged in from Russia at 3am using stolen credentials. Our auth system said 'valid password' and let them in."

— CISO, after a credential stuffing attack

AI that knows your users.
Catches what rules can't.

Every login scored. Every request baselined. Every container watched. Every cloud credential analyzed. ML models trained on YOUR data, running in YOUR binary. No data leaves your infrastructure.

Security without AI

  • Attacker with stolen creds logs in — system says 'valid password'
  • 3am login from new country: no alert
  • Credential stuffing: rate limiting is the only defense
  • Behavior anomalies: nobody's watching
  • Post-breach forensics: days of log correlation
  • False positives: either too many alerts or none

With AuthFI AI

  • Stolen creds from new location at 3am: blocked, MFA required
  • Every login scored by per-tenant ML model
  • Isolation Forest catches anomalies rules can't
  • k-means clusters normal behavior, flags outliers
  • Real-time threat timeline: answers in seconds, not days
  • ML tuned on YOUR data — low false positive rate

AI is native to every layer

Not a separate product. Not an add-on. Every layer of AuthFI has intelligence built in.

AUTH LAYER Every login is scored
Rules
Credential stuffing (same IP, many users)
Brute force (10+ failures per user)
Dormant accounts (auto-deactivate 180d)
ML
Impossible travel (haversine + time)
Login anomaly (Isolation Forest per user)
Behavior clustering (k-means, detect drift)
Risk score 0-100 per user
LLM
"This login is unusual because..."
Weekly auth security digest
Recommended: enforce MFA, verify user
SERVICE LAYER Every request is baselined
Rules
Unprotected services flagged
Missing policies detected
API route auto-discovery
ML
Traffic anomaly (z-score per service per hour)
New service-to-service connections flagged
Per-service baseline, updated hourly
LLM
Auto-suggest policies from traffic patterns
"Suggest: block PUT /admin for viewers"
NL policies: English → eBPF rules
INFRASTRUCTURE LAYER Every container and process is watched Kubernetes Docker
Rules
Privileged containers detected
SSH password auth flagged
No network policies = risk
Open ports on 0.0.0.0
ML
Process anomaly (unknown binary, high CPU)
Container behavior drift (redis using 2GB?)
Network topology anomaly (new edges)
LLM
"Vault container is privileged — recommend removing"
Infra security digest in plain English
RAG: "which pods have no resource limits?"
CLOUD LAYER Every cloud credential exchange is analyzed
Rules
Unused role mappings (90+ days)
Overprivileged roles (Owner/Admin)
Multi-cloud blast radius
Stale WIF config (180+ days)
ML
Unusual cloud credential time
New provider access (first time AWS)
Credential request spike
LLM
"2 users have admin in both GCP and AWS — blast radius risk"
"Remove 3 unused mappings to reduce attack surface"

The ML models — pure Go, zero dependencies

No Python. No TensorFlow. No external APIs. Models implemented in Go, trained daily on your data, cached in memory for real-time scoring.

Isolation Forest

Detects anomalies by isolating observations. Anomalies are isolated in fewer random splits → shorter path length → higher score. Trained on 30 days of login data per tenant.

How it scores a login:
Features: [hour=3, country=BR, device=new, failures=0]
100 random trees, 256 sample size
Average path length: 3.2 (short = anomaly)
Score: 0.92 (threshold: 0.8)
→ ANOMALY — step-up MFA

k-means Clustering

Groups users into behavior clusters. When a user's session looks like a different cluster, it's flagged as behavior drift. k-means++ initialization for stable clusters.

Example clusters:
Cluster 0: "Office workers" — 9-5, same IP, 1-2 devices
Cluster 1: "Remote devs" — all hours, many IPs
Cluster 2: "Admins" — irregular, high privilege
Cluster 3: "Bots/scripts" — exact patterns, same time daily
→ Dr. Priya in Cluster 0, acting like Cluster 3?
→ DRIFT DETECTED

The feedback loop — AI gets smarter every day

Admin overrides don't just dismiss alerts — they retrain the model. "This was legitimate" adds weight to training data. Fewer false positives over time.

1
Detect
ML scores event
Score: 0.87
2
Act
Step-up MFA
or block login
3
Review
Admin sees alert
in console
4
Feedback
"This was OK"
or "Confirmed threat"
5
Retrain
Model adjusts
daily batch

Admin controls thresholds: flag at 0.6 · step-up MFA at 0.8 · block at 0.9 · feedback weight 1x-10x · per-tenant config

Security posture score — one number

All 4 layers scored together. Computed daily. Tracks trends over time.

Auth
22/25
MFA adoption, password strength, SSO coverage
Services
18/25
Protection coverage, policy completeness
Infrastructure
12/25
Container security, network policies, SSH
Cloud
20/25
Permission hygiene, config freshness
72/100

Penalties: critical -10, high -5, medium -2, low -1. Improve by fixing findings.

What's real. What's honest.

90% of AuthFI's AI runs without any LLM. Isolation Forest, k-means, z-score — these are mathematical models that train on your data. No API calls. No cost. No data leaving your infra.
10% uses Gemini for language tasks: generating policies from English, writing security digests, explaining ML findings. This is optional (Business+) and costs ~$2/month per tenant.
0% of our AI is marketing hype. Every detection type listed on this page is implemented, deployed, and running. The models train daily. The feedback loop works. We don't claim "AI-powered" — we show you the Isolation Forest scoring your login.

AI that's included. Not upsold.

What other platforms charge extra for or don't offer at all.

ML anomaly detection

Pro plan
Splunk/custom ML: $50K+ setup

Models run in your binary

All plans
Cloud ML services: data leaves your infra

Per-tenant trained models

Pro plan
Shared models: same rules for everyone

Zero data exfiltration

By design
Most vendors: your data in their cloud

Natural language policies

Business plan
Others: write Rego/OPA by hand

Admin feedback retraining

Pro plan
Most: static rules, no learning

Real scenario

S
Sarah, CISO
Fintech, 500K users
The problem

Credential stuffing attack hit 50K login attempts in one night. Rate limiting blocked most, but 200 got through with valid stolen credentials. Took 3 days to find which accounts were compromised.

The result

AuthFI ML flagged all 200 logins within seconds — anomalous location, device, time pattern. Auto-blocked and forced MFA re-enrollment. Zero customer data exposed. Incident closed in 4 hours, not 3 days.

3 days → 4 hours
incident response
Start free → AI security docs

Related features

eBPF Infrastructure

Kernel-level enforcement — identity-aware networking at the packet level

Observability

Real-time dashboards, audit logs, and threat timelines

Authentication

Every auth method, every security layer, every plan

One platform. Every identity layer.
Free to start.

Free for 5,000 users. Upgrade when you're ready.

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