A lot of developers treat API security as:
“Add JWT authentication and you’re done.”
That’s usually where the problems begin.
JWT only solves one part of the system:
Who is making the request?
It does not solve:
what they’re allowed to access
how often they can hit the API
whether their behavior is suspicious
how abuse is detected across services
One production mistake I still see often is applying rate limiting before authentication.
That usually means rate limits are tied to IP addresses instead of authenticated users.
At small scale this seems fine.
At scale, it causes problems:
users behind shared IPs get blocked
mobile networks trigger false positives
attackers rotate IPs and bypass limits anyway
A better request flow is:
Request
→ JWT Validation
→ Extract User Identity
→ User-Based Rate Limiting
→ Authorization
→ API Logic
Another issue is over-trusting API gateways.
Many systems validate JWTs only at the gateway layer and assume internal services are safe.
That assumption breaks fast in distributed systems.
Every service should still validate:
token signature
expiration
issuer/audience
permissions/scopes
This becomes even more important in microservices architectures where services communicate internally across multiple boundaries.
I also think AI tools are becoming genuinely useful for API security reviews, not as replacements for security architecture, but as assistants.
For example, AI can quickly spot:
missing expiration checks
weak JWT validation logic
unsafe middleware patterns
improper error handling
The important thing is understanding that API security is not one feature.
It’s a layered system:
authentication
authorization
rate limiting
monitoring
observability
I recently put together a more detailed breakdown covering:
JWT authentication
user-based rate limiting
API gateways
Zero Trust concepts
AI-assisted monitoring
common production security mistakes
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