Best AI Coding Assistants FAQ: Expert Security & Implementation Guide
Every CTO I talk to has the same two questions about AI coding assistants: "Which one is actually secure?" and "How do I stop it from breaking everything?"
Fair questions. The difference between a coding assistant that 10x's your team and one that introduces critical vulnerabilities is almost entirely about context. Not which model it uses. Not how good its autocomplete is. Context.
Let me walk through the questions that matter, with the ugly truths most vendors won't tell you.
Which AI Coding Assistant Is Actually Secure?
None of them are "secure" by default. They're all language models that do what you train them to do with the context you provide.
GitHub Copilot sends code to GitHub's infrastructure. Cursor sends it to Anthropic or OpenAI depending on your model choice. Continue.dev can run locally but most teams use cloud models for performance. Cody routes through Sourcegraph's systems.
The security question isn't "which tool" but "what data are you sending and how?"
Here's what actually matters:
Context window management. Most AI assistants grab whatever's in your current file plus some related files. They don't understand what's sensitive. I've seen assistants pull API keys from config files, authentication logic from security modules, and PII from test fixtures—all to help you write a utility function.
Model selection. Cloud models are better. Local models are slower and dumber but your code never leaves your infrastructure. The performance gap is real. A local CodeLlama or StarCoder model will feel like coding with mittens on compared to GPT-4 or Claude Sonnet. But if you're in fintech or healthcare, that trade-off might be mandatory.
Telemetry. Most tools send usage metrics back home. That's usually fine—they're not sending your actual code. But read the privacy policy. Some tools send error messages (which can include code snippets), file names (which can reveal project structure), and interaction patterns (which can reveal what features you're building).
The honest answer: Cursor with Claude Sonnet 3.5 is the best technical choice for most teams right now. Fast, smart, good multi-file editing. But you need to configure it properly.
How Do I Configure AI Assistants to Not Leak Secrets?
.gitignore is not a security boundary. AI assistants don't check it before slurping up context.
You need explicit ignore rules. Every major assistant supports them.
But here's the problem: you're playing whack-a-mole. New secrets get added. Engineers create temp_api_keys.txt files. Someone puts AWS credentials in a commented section of code "just for testing."
This is where code intelligence platforms like Glue become critical. Glue indexes your entire codebase and can flag files containing secrets, sensitive patterns, or authentication logic. You can use that metadata to automatically generate ignore rules for your AI assistants. Instead of maintaining static lists, you're dynamically identifying what shouldn't be shared with external models.
More importantly, Glue's AI-powered feature discovery can identify security-critical code paths—auth modules, payment processing, encryption logic—so you can exclude those from AI context entirely or route them to local models only.
Should We Ban AI Assistants in Production Code?
Banning them doesn't work. Engineers will use them anyway, just without oversight.
Better approach: establish context boundaries.
Production services that handle payments, auth, or PII? Require human code review and ban AI-generated code in those specific modules. Everything else? Let AI assist but require testing.
The rule I recommend: AI can suggest, humans must verify. Never merge AI-generated code without understanding it.
This is harder than it sounds. AI is really good at writing plausible-looking code with subtle bugs. It will confidently generate SQL that's vulnerable to injection. It will write authentication checks that have race conditions. It will create error handling that leaks stack traces.
Example I saw last month: Cursor generated a React component that looked perfect. Clean code, proper hooks, TypeScript types. But it had a useEffect with a missing dependency that caused infinite re-renders under specific conditions. The engineer didn't catch it because the AI's explanation sounded right.
Code review catches this. But only if reviewers know to look for AI artifacts. Train your senior engineers to recognize AI patterns:
Overly verbose error messages
Unnecessarily complex abstractions
"Correct" code that doesn't match your codebase conventions
Solutions that work but ignore existing utilities
How Do We Measure AI Assistant ROI?
Most teams measure the wrong thing: lines of code generated. That's vanity metrics.
What actually matters:
Time to first working prototype. Can your team validate ideas faster? This is where AI shines. Spinning up a new service, building a proof of concept, refactoring a module—AI assistants can cut these tasks from days to hours.
Code review cycle time. This is the controversial one. If AI generates code that requires 5x more review scrutiny, you're slower not faster.
Bug escape rate. Are more bugs making it to production? Track this carefully in the first 90 days of AI adoption.
Context switch reduction. Does AI let engineers stay in flow longer? This is hard to measure but engineers will tell you. If they're constantly stopping to validate AI suggestions, that's friction not acceleration.
Glue's team insights actually help here. We track code churn, complexity trends, and ownership patterns. When teams adopt AI assistants, you should see:
Reduced churn in utility code (AI writes it once, correctly)
Increased churn in feature code (faster iteration)
More consistent patterns across modules (AI learns from examples)
If you're seeing the opposite—increasing complexity or more scattered ownership—your AI implementation isn't working.
What Context Should AI Assistants Have Access To?
This is the critical question that most teams get wrong.
The default behavior of most AI assistants is: grab the current file, pull in some related files based on imports, maybe check the last few files you edited. That's not enough context for good suggestions but it's too much context for security.
You need layered context architecture:
Layer 1: Local context. Current file, immediately related files, types/interfaces. This is fast and safe. Most autocomplete happens here.
Layer 2: Project context. The full module or service. Used for refactoring, understanding architectural patterns, generating tests. This is where you need to be careful about secrets and sensitive code.
Layer 3: Organizational context. Internal libraries, shared utilities, coding standards. This is where AI assistants become genuinely valuable—they can learn your team's patterns and enforce them automatically.
Most AI assistants only do Layer 1 well. They're getting better at Layer 2. Layer 3 is where platforms like Glue come in. We provide the organizational context layer—documentation, feature maps, code health metrics, architectural patterns—that makes AI assistants actually understand your codebase instead of just pattern-matching against open source code.
How Do We Handle AI-Generated Technical Debt?
You will accumulate AI-generated technical debt. Plan for it.
AI assistants optimize for "working now" not "maintainable later." They'll generate code that solves the immediate problem without considering:
How this fits into your architecture
Whether this duplicates existing functionality
What the performance implications are
How this will be tested
Who will maintain this
Create a technical debt review cadence specifically for AI-generated code. Monthly or quarterly, have senior engineers review modules with high AI contribution. Look for:
Duplicated logic that should be abstracted
Over-engineered solutions to simple problems
Code that works but violates team conventions
Missing error handling or edge cases
Glue's code health mapping is designed for exactly this. We show you churn patterns, complexity hotspots, and ownership gaps. AI-generated code tends to show specific patterns: low ownership (no one "owns" it), high initial complexity (AI over-engineers), and either very high or very low churn (perfect code that never changes, or problematic code that constantly gets patched).
What's the Actual Implementation Timeline?
Most teams underestimate this by 10x.
Week 1: Setup and configuration. Install tools, configure ignore rules, set up model routing. Easy.
Week 2-4: Learning curve chaos. Engineers are slower, not faster. They're learning what AI is good at, fighting with suggestions, debugging AI-generated bugs. Productivity drops.
Week 5-8: Pattern stabilization. Teams figure out what to use AI for and what to avoid. Productivity returns to baseline.
Week 9-12: Real gains. Engineers know when to accept suggestions, when to ignore them, and how to prompt effectively. This is where 2-3x productivity improvements appear, but only for specific tasks.
The teams that succeed do three things:
Explicit training. Don't just give engineers access and hope for the best. Show them good prompts, bad prompts, and how to verify suggestions.
Context boundaries. Define what AI can touch and what it can't. Document this. Enforce it in code review.
Feedback loops. Weekly retros on AI usage. What worked? What caused problems? Adjust configuration based on reality.
The Context Problem Nobody Talks About
Here's the uncomfortable truth: AI coding assistants are only as good as the context you give them. If your codebase is poorly documented, inconsistently structured, and full of implicit knowledge that exists only in engineers' heads, AI will amplify those problems.
Before implementing AI assistants, you need code intelligence infrastructure. Not as a nice-to-have, as a prerequisite. You need to know:
Where your security boundaries are
What your architectural patterns are
What code is healthy and what's rotting
Who owns what and where the knowledge gaps are
This is why we built Glue. AI assistants are powerful, but they need foundation. Glue provides that foundation—indexing your codebase, discovering features, mapping code health, generating context-aware documentation. When Cursor or Copilot asks "what should I know about this codebase?" Glue gives the right answer.
The teams winning with AI assistants aren't just using better models. They're using better context.