Building a internal tool with Python? Let us review it.
Expert code review for internal tools built with Python. We fix Python-specific security gaps, optimize performance, and handle deployment. From $19.
Common Python issues we find
Real problems from Python codebases we've reviewed.
Django debug mode in production
DEBUG=True left enabled in production, exposing stack traces, database queries, and configuration to attackers.
Missing CSRF protection
CSRF middleware disabled or bypassed for convenience, allowing cross-site request forgery attacks.
Insecure deserialization
Using pickle or yaml.load with untrusted data, enabling remote code execution.
Slow database queries
ORM queries that generate inefficient SQL, N+1 query patterns, and missing database indexes.
Internal Tool challenges to solve
Key internal tool concerns that AI-generated code often misses.
Access control
Who can see what? Internal tools need role-based access — finance sees revenue data, support sees customer data, engineering sees system metrics. AI tools build the dashboard but rarely implement granular permissions.
Data sensitivity
Internal tools often connect directly to production databases. A bug that deletes records or a missing auth check that exposes customer PII can have serious legal and business consequences.
Network security
Internal tools should be behind a VPN or protected network, not on the public internet. AI tools deploy to public URLs by default. Proper network configuration prevents external access.
Audit logging
When someone modifies data through an internal tool, you need to know who did what and when. This is essential for debugging, compliance, and accountability.
What we check
Key areas we review for Python internal tool projects.
Access control — role-based permissions enforced server-side
Database security — read-only where possible, protected write operations
Network configuration — not publicly accessible, VPN or auth gateway
Audit logging — who did what, when
Not sure if your app passes? Our code audit ($19) checks all of these and more.
Start with a self-serve audit
Get a professional review of your Python internal tool project at a fixed price.
External Security Scan
Black-box review of your public-facing app. No code access needed.
- OWASP Top 10 vulnerability check
- SSL/TLS configuration analysis
- Security header assessment
- Expert review within 24h
Code Audit
In-depth review of your source code for security, quality, and best practices.
- Security vulnerability analysis
- Code quality review
- Dependency audit
- Architecture review
- Expert + AI code analysis
Complete Bundle
Both scans in one package with cross-referenced findings.
- Everything in both products
- Cross-referenced findings
- Unified action plan
100% credited toward any paid service. Start with an audit, then let us fix what we find.
How it works
Tell us about your app
Share your project details and what you need help with.
Expert + AI audit
A human expert assisted by AI reviews your code within 24 hours.
Launch with confidence
We fix what needs fixing and stick around to help.
Frequently asked questions
Can you review a internal tool built with Python?
Yes. We regularly audit Python internal tool projects and understand the specific patterns and pitfalls of this combination. Our review covers security, performance, and deployment readiness.
What issues do you find in Python internal tools?
Common issues include django debug mode in production and missing csrf protection on the Python side, combined with internal tool-specific concerns like access control and data sensitivity. We check for all of these and more.
How do I make my Python internal tool production-ready?
Start with our code audit ($19) to get a prioritized list of issues. For Python internal tool projects, the typical path is: fix security gaps, address internal tool-specific requirements, optimize Python performance, then configure deployment. We provide a fixed quote after the audit.
How long does it take to audit a Python internal tool?
Our code audit delivers a full report within 24 hours. For Python internal tool projects, we check security, architecture, performance, and deployment readiness across all Python-specific patterns. Fixes are scoped separately with a fixed quote.
Related resources
Python by Use Case
Need help with your Python internal tool?
Tell us about your project. We'll respond within 24 hours with a clear plan and fixed quote.