Cursor is incredible. We use it every day. The productivity gains are real. What used to take an afternoon now takes twenty minutes.
But after shipping thousands of lines of AI-assisted code, we've noticed patterns. Blind spots that show up consistently. Not bugs exactly. More like gaps in awareness. Gaps that become problems when you're trying to pass a SOC 2 audit or prove HIPAA compliance.
This isn't a criticism of Cursor or any AI coding tool. It's just the reality of how these systems work. They're trained on code, not on the context around that code. They don't know your threat model. They don't know what data you're handling. They don't know what your auditor will ask about in six months.
Pattern 1: The Missing Audit Trail
AI tools are great at implementing features. Less great at thinking about observability.
You ask for a user deletion endpoint. You get clean, functional code. What you often don't get: logging of who deleted what, when, and why. The kind of audit trail that seems optional until someone asks "who deleted this account?" and you have no answer. This is a core requirement for both SOC 2 and HIPAA.
This isn't a Cursor problem. It's a training data problem. Most code samples don't include comprehensive logging. So AI learns that logging is optional garnish, not essential infrastructure.
What to watch for: Any endpoint that modifies data should log the action, the actor, and the timestamp. If your AI-generated code doesn't include this, add it.
Pattern 2: Hardcoded Configuration
AI tools love to make things work. The fastest path to working code often involves hardcoding values that should be configurable.
Database connection strings. API endpoints. Feature flags. Timeout values. These show up as string literals in AI-generated code far more often than they should.
In development, this works fine. In production, it becomes a security issue and an operational headache. Hardcoded secrets in your codebase are an automatic SOC 2 finding. Your auditor will flag it. Your penetration test will flag it. And if those secrets end up in version control, you have a much bigger problem.
What to watch for: Any string that looks like configuration. URLs, connection strings, credentials (obviously), but also things like rate limits and retry counts. These should come from environment variables or a config service.
Pattern 3: Optimistic Error Handling
Ask AI to build an integration with an external API. You'll get code that handles the happy path beautifully. The error handling? Often an afterthought.
Generic catch blocks that swallow errors. Missing retry logic. No circuit breakers. No graceful degradation. The code works perfectly right up until it doesn't.
From a compliance perspective, swallowed errors mean missing audit logs. If something fails silently, you have no record of what happened. When an auditor asks "what happens when this integration fails?", you need an answer better than "it catches the exception."
What to watch for: Every external call should have explicit error handling. What happens when the service is down? What happens when it's slow? What happens when it returns unexpected data? And critically: is the failure logged?
Pattern 4: Access Control Assumptions
This one is subtle. AI generates code based on patterns in its training data. Those patterns often assume that authentication equals authorization.
You get code that checks if a user is logged in but not if that user should have access to this specific resource. The classic "IDOR" vulnerability (Insecure Direct Object Reference) shows up in AI-generated code regularly.
This is one of the most common findings in penetration tests, which are required for SOC 2 Type II. It's also a HIPAA violation if it exposes protected health information. IDOR vulnerabilities are easy to introduce and easy to miss in code review.
What to watch for: Every endpoint that accesses user data should verify the requesting user has permission to access that specific resource. Not just that they're authenticated. That they're authorized.
Pattern 5: The Incomplete Transaction
AI is great at implementing individual operations. Less great at thinking about what happens when operations need to be atomic.
You ask for code that updates a user's subscription and sends a confirmation email. You might get two separate operations with no transaction boundary. If the email fails, did the subscription update? If the update fails but the email sent, what state is the user in?
Data integrity issues like this create compliance problems. Auditors expect your systems to maintain consistent state. If you're handling financial data under PCI-DSS or health records under HIPAA, inconsistent data isn't just a bug. It's a compliance finding that raises questions about your entire system.
What to watch for: Operations that should succeed or fail together need explicit transaction handling. Database transactions, idempotency keys, saga patterns. AI often generates the operations without the coordination.
What This Means for Your Workflow
None of these patterns are deal-breakers. AI coding tools are still a massive net positive for productivity. But they require a different kind of code review.
The traditional code review asks: does this work? Is it clean? Is it maintainable?
The AI-assisted code review needs to add: what did the AI miss? What context was it lacking? What assumptions did it make? Did it introduce a SOC 2 violation? A HIPAA gap? A PCI-DSS issue?
This is where automated checks become valuable. Not as a replacement for understanding your code, but as a safety net. A way to catch the patterns that AI tools consistently miss.
The Takeaway
AI coding tools are like a brilliant junior developer who can write code faster than anyone you've ever worked with. They need guidance. They need review. They need someone who understands the broader context of what they're building.
Use the tools. Ship faster. But build in the guardrails that catch what the tools miss.
This is why we built Nodura. It catches these exact patterns automatically: missing audit trails, hardcoded secrets, access control gaps, incomplete error handling. Every PR gets scanned against SOC 2, HIPAA, and PCI-DSS requirements. Issues get flagged before they merge, while the code is still fresh in your head. No audit-time surprises. No remediation sprints. Just compliant code, shipped fast.