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Arslon Erkinov
Arslon Erkinov

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Day 17 – Building SaaS-Grade Analytics for My AI Phishing Defense Platform

Today I focused entirely on backend robustness.

Instead of jumping to UI, I strengthened the analytics layer.

What I Added
1️⃣ API Usage Monitoring
Status codes
Request latency (ms)
Per-API-key tracking
Error rate calculation

2️⃣ Risk Intelligence Metrics
High / Medium / Low distribution
Risk percentage breakdown
7-day usage trends
Average risk score over time

3️⃣ Performance Metrics
Average latency
Error rate (%)
Per-plan breakdown
Real Bug I Faced
I hit this error:

sqlite3.OperationalError: no such column: analyzer_apiusage.latency_ms
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The model was updated, but the database schema wasn’t.
Classic migration issue.

The fix:
Run makemigrations
Run migrate
Ensure correct module imports in models/init.py

Lesson:
Your ORM and your DB schema must always stay aligned.
Why This Matters
Before building UI, backend must be stable.

Now the system:
Tracks performance
Measures abuse
Prepares for production
Supports SaaS analytics dashboards

Next step: Backend hardening before frontend.
Security is not just detection — it’s architecture.

django #ai #cybersecurity #backend #saas

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