Cursor AI IDE Setup Guide for Development and Security

Posted: April 14, 2026 to Technology.

Cursor is an AI-native code editor built on top of VS Code that integrates large language models directly into the editing experience. Rather than bolting AI onto an existing editor through an extension, Cursor rebuilds the interaction model from the ground up: predictive multi-line tab completions, inline code generation from natural language, a chat panel with full codebase awareness, and a multi-file composer that can refactor entire projects from a single prompt. If you write code for a living, whether that means application development, infrastructure automation, security tooling, or AI/ML pipelines, Cursor changes the speed and quality of your output in measurable ways.

At Petronella Technology Group, we build and maintain cybersecurity infrastructure, AI development systems, and managed IT environments. Our engineering team adopted Cursor in early 2025 after evaluating every major AI-assisted editor on the market. This guide covers everything we learned: installation, configuration, cybersecurity-specific workflows, AI/ML development patterns, and honest comparisons with competing tools. We wrote it because the official documentation covers features but not the practical decisions you face when integrating an AI editor into professional security and development work.

What Is Cursor and Why It Matters

Cursor is a fork of Visual Studio Code, created by Anysphere, that replaces the standard editing experience with one designed around AI from the start. Because it is a fork rather than an extension, the Cursor team can modify the editor’s core: how suggestions appear, how context is gathered, how edits are applied, and how the AI interacts with your file tree. This level of integration is not possible through the VS Code extension API alone.

The editor supports multiple AI models. You can use Claude (Anthropic’s model family, including Claude Sonnet and Claude Opus), GPT-4o, and other models depending on your subscription tier. The choice of model matters because different models excel at different tasks: Claude tends to produce more thorough, well-reasoned code for complex refactors, while GPT-4o is faster for simple completions. Cursor lets you switch between models per feature, so you might use a fast model for tab completions and a more capable model for chat and composer tasks.

The core value proposition is context. Traditional AI coding assistants see only the current file or a small window around your cursor. Cursor indexes your entire codebase and lets you reference specific files, folders, documentation, and even web content in your prompts. When you ask Cursor to refactor a function, it understands the callers, the tests, the type definitions, and the configuration files that depend on that function. This codebase-aware context is what separates Cursor from simpler autocomplete tools.

Installation on macOS, Linux, and Windows

macOS

Download the .dmg installer from cursor.com. Drag Cursor to your Applications folder and launch it. If you have VS Code installed, Cursor will offer to import your extensions, settings, and keybindings on first launch. Accept this import unless you want a clean start.

# Alternatively, install via Homebrew
brew install --cask cursor

On Apple Silicon Macs (M1 through M4), Cursor runs natively as a universal binary. There is no Rosetta overhead. The local model inference features, when available, benefit from the Neural Engine on Apple Silicon chips.

Linux

Cursor provides an AppImage for Linux distributions. Download it from the official site, make it executable, and run:

# Download the AppImage
wget https://downloader.cursor.sh/linux/appImage/x64 -O cursor.AppImage
chmod +x cursor.AppImage

# Run directly
./cursor.AppImage

# Or move to a standard location
mv cursor.AppImage ~/.local/bin/cursor
chmod +x ~/.local/bin/cursor

For NixOS users, Cursor is available through community packages. The AppImage approach works on NixOS with appimage-run, though a native Nix derivation provides better integration:

# NixOS with appimage-run
nix-shell -p appimage-run --run "appimage-run ./cursor.AppImage"

# Or add to your system configuration
environment.systemPackages = with pkgs; [
  appimage-run
];

On Arch Linux, the AUR provides cursor-bin for automatic updates:

yay -S cursor-bin

For Wayland compositor users, add the --ozone-platform=wayland flag to launch Cursor with native Wayland rendering. Without this flag, Cursor runs through XWayland, which works but produces suboptimal text rendering on HiDPI displays:

cursor --ozone-platform=wayland

Windows

Download the .exe installer from cursor.com and run it. Cursor installs per-user by default, so no administrator privileges are required. It will detect an existing VS Code installation and offer to import settings. If you use WSL (Windows Subsystem for Linux) for development, Cursor connects to WSL environments seamlessly through the Remote WSL extension, the same way VS Code does.

First Launch Configuration

After installation on any platform, Cursor walks you through initial setup:

1. Import VS Code settings: Transfer extensions, themes, keybindings, and settings.json. This is non-destructive and does not modify your VS Code installation.
2. Sign in: Create a Cursor account or sign in with GitHub. The free tier includes limited AI completions. The Pro tier ($20/month) includes unlimited completions, premium model access, and higher rate limits.
3. Choose your AI model: Select a default model for chat and completions. You can change this at any time from the model picker in the bottom status bar.
4. Privacy settings: Choose whether to enable or disable codebase indexing for AI features. In Privacy Mode, your code is not stored on Cursor’s servers beyond the duration of a request.

Key Features Deep Dive

Tab Completion: Predictive Multi-Line Suggestions

Cursor’s tab completion goes beyond single-line autocomplete. It predicts entire blocks of code based on what you are writing, the context of the current file, and patterns from your codebase. Start typing a function and Cursor suggests the complete implementation, including error handling and edge cases that match your project’s existing patterns.

The completions appear as ghost text (dimmed, inline). Press Tab to accept the full suggestion, or press Ctrl+Right Arrow to accept one word at a time. This word-by-word acceptance is useful when the suggestion is 80% correct and you want to take the good parts while modifying the rest.

# Example: Start typing a function signature and Cursor suggests the body
def validate_security_config(config_path: str) -> dict:
    # Cursor suggests the complete implementation:
    """Validate a security configuration file against baseline guidelines."""
    if not os.path.exists(config_path):
        raise FileNotFoundError(f"Config not found: {config_path}")

    with open(config_path, "r") as f:
        config = yaml.safe_load(f)

    required_keys = ["tls_version", "cipher_suites", "hsts_max_age"]
    missing = [k for k in required_keys if k not in config]
    if missing:
        raise ValueError(f"Missing required keys: {missing}")

    return config

The completion quality depends heavily on codebase context. In a project with consistent patterns, Cursor learns your conventions: your error handling style, your logging approach, your naming patterns. In a new or inconsistent codebase, suggestions are more generic.

Cmd+K (Ctrl+K on Linux/Windows): Inline Editing

Select a block of code and press Cmd+K (or Ctrl+K) to open an inline prompt. Describe what you want in natural language, and Cursor edits the selected code in place. This is different from generating new code; it modifies existing code according to your instruction while preserving context.

Practical examples that we use daily:

• Select a function: “Add input validation for SQL injection patterns”
• Select a class: “Convert this to use async/await instead of callbacks”
• Select a configuration block: “Harden these TLS settings for PCI compliance”
• Select a test file: “Add edge case tests for empty input and unicode strings”

The inline edit shows a diff view so you can review changes before accepting. Press Cmd+Shift+K to reject the edit and revert to the original code.

Chat Panel with Full Codebase Context

Open the AI chat panel with Cmd+L (or Ctrl+L). This is not a generic chatbot; it is a coding assistant with deep awareness of your project. The power comes from context references:

• @codebase: Search your entire indexed codebase for relevant context. Ask “@codebase how does authentication work in this project?” and Cursor finds the auth modules, middleware, configuration files, and tests.
• @file: Reference a specific file. “@file:src/auth/middleware.py explain the token validation logic.”
• @folder: Reference an entire directory. “@folder:src/api/ what error handling patterns are used?”
• @web: Search the web for current documentation or examples. Useful for referencing library APIs that may have changed since the model’s training cutoff.
• @docs: Reference indexed documentation. You can add documentation URLs for libraries you use, and Cursor indexes them for reference.

The chat panel maintains conversation history within a session, so you can iterate on solutions. Ask for a first draft, review the output, then refine: “Good approach, but use connection pooling instead of creating a new connection per request.”

Composer: Multi-File Edits

Composer is Cursor’s most powerful feature for large refactors. Open it with Cmd+I (or Ctrl+I). Unlike the chat panel, which discusses code and suggests changes, Composer directly creates and modifies files across your project.

Example prompt: “Create a REST API endpoint for user authentication with JWT tokens. Include the route handler in src/api/auth.py, the JWT utility functions in src/utils/jwt.py, the Pydantic models in src/models/auth.py, and tests in tests/test_auth.py.”

Composer generates all four files with consistent imports, shared types, and proper test coverage. It shows a diff for each file, and you can accept or reject changes per file. For security teams, this is particularly powerful for scaffolding compliance-related code: security controls, audit logging, access control layers, and their associated tests.

Composer also supports an “agent mode” where it can run terminal commands, install dependencies, and iterate on errors automatically. If a test fails after a code change, Composer can read the error output and attempt to fix the issue without manual intervention.

Privacy Mode and Model Options

Cursor offers a Privacy Mode toggle in settings. When enabled, your code is not stored on Cursor’s servers after the request completes, and it is not used for model training. For organizations handling sensitive code (government contracts, healthcare data, financial systems), Privacy Mode is a baseline requirement.

For teams with strict data residency requirements, Cursor supports configuring custom API endpoints. You can point Cursor at your own model deployment (such as a self-hosted model on your AI development infrastructure) instead of using Cursor’s cloud models. This keeps all code processing within your network perimeter.

.cursorrules: Project-Specific AI Behavior

The .cursorrules file is a plaintext file placed in the root of your project that instructs Cursor’s AI on project-specific conventions, constraints, and preferences. Think of it as a system prompt that is automatically included with every AI interaction in that project. This is one of Cursor’s most underutilized features, and getting it right dramatically improves suggestion quality.

Basic Structure

# .cursorrules

## Project Context
This is a Python FastAPI application for managing cybersecurity compliance assessments.
The backend uses PostgreSQL with SQLAlchemy ORM. Authentication uses JWT tokens.
The frontend is a separate React application (not in this repo).

## Code Style
- Use type hints on all function signatures
- Use Pydantic models for all API request/response schemas
- Follow Google Python style guide for docstrings
- Prefer explicit error handling over bare except clauses
- All database operations must use async SQLAlchemy sessions

## Security Requirements
- Never log sensitive data (passwords, tokens, PII)
- All user input must be validated through Pydantic models before processing
- SQL queries must use parameterized statements (never string formatting)
- File uploads must validate content type and scan for malicious content
- API endpoints must include rate limiting decorators

## Testing
- Write pytest tests for all new functions
- Use factories (factory_boy) for test data, not fixtures with hardcoded values
- Test both success and failure paths
- Include at least one test for each input validation rule

Security-Focused .cursorrules

For projects where security is the primary concern, a more detailed .cursorrules file prevents the AI from generating vulnerable patterns:

# .cursorrules for security-focused development

## OWASP Top 10 Awareness
When generating code, actively prevent these vulnerability classes:
1. Injection: Always use parameterized queries. Never construct SQL from user input.
2. Broken Authentication: Tokens must have expiration. Use bcrypt for password hashing.
3. Sensitive Data Exposure: Never log credentials, tokens, or PII. Use environment
   variables for secrets, never hardcoded values.
4. XML External Entities: Disable DTD processing in all XML parsers.
5. Broken Access Control: Every endpoint must check authorization, not just authentication.
6. Security Misconfiguration: Default deny. Explicit allowlists over blocklists.
7. XSS: Sanitize all output rendered in HTML. Use Content-Security-Policy headers.
8. Insecure Deserialization: Never deserialize untrusted data with unsafe methods or eval.
9. Using Components with Known Vulnerabilities: Flag any import of deprecated libraries.
10. Insufficient Logging: All authentication events and access control failures must be logged.

## Cryptography
- Minimum TLS 1.2 for all connections
- AES-256-GCM for symmetric encryption
- RSA-4096 or Ed25519 for asymmetric operations
- Never implement custom cryptographic algorithms

## Dependencies
- Flag any suggestion to add a new dependency. Prefer stdlib solutions.
- If a dependency is necessary, suggest the most actively maintained option.
- Never suggest packages with known CVEs.

The .cursorrules file is checked into version control, so every team member gets the same AI behavior. This is important for consistency: without it, each developer’s AI suggestions reflect their individual prompting style rather than the team’s standards.

Configuration for Cybersecurity Workflows

Our team at Petronella uses Cursor daily for security work. Here is how we configure it for the specific demands of cybersecurity engineering.

Code Review and Vulnerability Scanning

Use the chat panel with @codebase to perform AI-assisted security reviews:

# In Cursor chat:
@codebase Review the authentication module for security vulnerabilities.
Check for: hardcoded secrets, SQL injection, improper input validation,
insecure token handling, missing rate limiting, and privilege escalation paths.

Cursor scans the relevant files, identifies potential issues, and provides specific line references with recommended fixes. This is not a replacement for dedicated SAST tools like Semgrep or Bandit, but it catches logical vulnerabilities that pattern-matching tools miss: business logic flaws, improper authorization checks, race conditions in concurrent code.

For reviewing third-party code or pull requests, select the changed files and use Cmd+K:

# Select the diff in a pull request review
"Review this change for security implications. Focus on:
- New attack surface introduced
- Changes to authentication or authorization logic
- Data validation changes
- Error handling that might leak information"

Analyzing Security Configurations

Infrastructure configuration files (Terraform, Ansible, Kubernetes manifests, firewall rules) are where many security misconfigurations originate. Cursor’s codebase awareness makes it effective at catching inconsistencies across config files:

# In Cursor chat:
@folder:terraform/ Audit these Terraform configurations for CIS benchmark compliance.
Check: S3 bucket encryption, security group rules allowing 0.0.0.0/0,
IAM policies with wildcard permissions, unencrypted RDS instances,
and CloudTrail logging status.

For organizations pursuing CMMC compliance or working under NIST 800-171 requirements, Cursor can cross-reference your implementation against control requirements when given proper context through .cursorrules or chat prompts.

Incident Response Scripting

During security incidents, speed matters. Cursor’s Composer mode lets you rapidly scaffold incident response scripts:

# Composer prompt:
"Create a Python script that:
1. Collects all failed SSH login attempts from /var/log/auth.log
2. Extracts source IPs, timestamps, and usernames
3. Generates a frequency analysis of attacking IPs
4. Checks each IP against AbuseIPDB API
5. Outputs a JSON report and a human-readable summary
Include proper error handling and logging."

The generated script is a starting point, not a finished product. Review it, test it in a sandboxed environment, and validate the output before using it on production systems. The value is in the speed of the first draft, not in blind trust of the output.

Configuration for AI/ML Development

AI and machine learning development has specific requirements that Cursor handles well when properly configured.

Python Environment Setup

Cursor inherits VS Code’s Python support, including virtual environment detection, interpreter selection, and linting. Configure your Python interpreter in .vscode/settings.json (which Cursor reads):

{
    "python.defaultInterpreterPath": "${workspaceFolder}/.venv/bin/python",
    "python.analysis.typeCheckingMode": "basic",
    "python.analysis.autoImportCompletions": true,
    "python.testing.pytestEnabled": true,
    "python.testing.pytestArgs": ["tests/"],
    "[python]": {
        "editor.formatOnSave": true,
        "editor.defaultFormatter": "charliermarsh.ruff"
    }
}

Use uv for package management in AI/ML projects. It is dramatically faster than pip for installing large packages like PyTorch, transformers, and CUDA toolkits:

# Create a virtual environment with uv
uv venv .venv
source .venv/bin/activate

# Install ML dependencies
uv pip install torch torchvision transformers datasets accelerate
uv pip install jupyter ipykernel matplotlib seaborn

Jupyter Notebook Integration

Cursor supports Jupyter notebooks with the same AI features available in regular files. Open a .ipynb file, and you get tab completions, inline editing, and chat within notebook cells. This is particularly useful for exploratory data analysis and model prototyping, where the interactive notebook workflow benefits from AI assistance at every cell.

Use Cmd+K within a notebook cell to transform data manipulation code: “Convert this pandas groupby to use polars for better performance on this 10M row dataset.” Cursor generates the polars equivalent while preserving the logic.

GPU Server Remote Development via SSH

For teams running AI workloads on remote GPU servers, Cursor connects to remote machines over SSH using the Remote SSH extension (inherited from VS Code). This lets you edit files, run terminals, and use AI features while your code executes on a remote machine with NVIDIA GPUs.

# ~/.ssh/config for GPU server access
Host gpu-server
    HostName 10.10.10.50
    User developer
    IdentityFile ~/.ssh/id_ed25519
    ForwardAgent yes
    LocalForward 8888 localhost:8888  # Jupyter
    LocalForward 6006 localhost:6006  # TensorBoard

In Cursor, press Cmd+Shift+P, type “Remote-SSH: Connect to Host,” and select your GPU server. Cursor opens a new window connected to the remote machine. File operations, terminal commands, and AI features all operate on the remote filesystem. The AI context indexing happens locally, so your codebase is indexed even when working remotely.

Model Configuration File Editing

AI/ML projects involve numerous configuration files: training hyperparameters, model architectures, dataset configurations, and deployment manifests. Cursor’s AI understands these formats and can suggest modifications based on your goals:

# In Cursor chat, referencing a training config:
@file:configs/training.yaml I want to reduce memory usage for fine-tuning
on a single 24GB GPU. Suggest changes to batch size, gradient accumulation,
and precision settings while maintaining training quality.

The model understands the relationships between hyperparameters and can suggest coherent changes rather than isolated tweaks. For hardware-constrained environments, this kind of guided configuration saves significant trial-and-error time.

Cursor vs VS Code + GitHub Copilot

This is the comparison most developers want. Both tools serve the same fundamental purpose: AI-assisted code editing. The differences are in depth of integration and approach.

Where Cursor wins: The depth of codebase context is Cursor’s primary advantage. The @codebase reference genuinely searches your project and provides relevant context, while Copilot’s workspace awareness, though improving, is more limited. Composer for multi-file edits is more mature than Copilot Edits. The .cursorrules system gives teams fine-grained control over AI behavior per project. Model flexibility (choosing between Claude and GPT models per feature) is a significant advantage for teams that have preferences based on task type.

Where Copilot wins: The extension ecosystem is fully supported since Copilot is an extension within the canonical VS Code, not a fork. Copilot is half the price. For organizations already invested in GitHub Enterprise, Copilot integrates with existing SSO, team management, and policy controls. The GitHub integration for pull request reviews, issue context, and repository knowledge is tighter.

Our recommendation: If AI-assisted coding is central to your workflow and you want the deepest possible integration, Cursor justifies the premium. If you need the stability of the official VS Code release cycle, tight GitHub Enterprise integration, or are price-sensitive, Copilot is the practical choice. Both are dramatically better than coding without AI assistance.

Cursor vs Windsurf, Zed, and Claude Code CLI

Cursor vs Windsurf

Windsurf (formerly Codeium) is another AI-native editor forked from VS Code. It takes a similar approach to Cursor with deep AI integration, its own model offerings, and multi-file editing capabilities. The key difference is in context handling: Cursor’s indexing and @reference system is more mature, while Windsurf’s “Cascade” flow aims to maintain context across longer chains of edits. Windsurf’s free tier is more generous than Cursor’s, making it a solid option for individual developers evaluating AI editors before committing to a subscription.

Cursor vs Zed

Zed is a native code editor written in Rust that emphasizes raw performance. It is not a VS Code fork; it is built from scratch with its own GPU-accelerated rendering engine. Zed includes AI features (completions, inline assist, chat), but the AI is one component of a performance-first editor rather than the central design principle. Choose Zed if editor speed and responsiveness matter more than AI depth. Choose Cursor if AI integration is the priority and you are comfortable with VS Code’s Electron-based performance profile.

Cursor vs Claude Code CLI

Claude Code is Anthropic’s official CLI tool for AI-assisted development. It runs in your terminal rather than in a graphical editor. Claude Code excels at autonomous multi-file operations, codebase exploration, and complex refactors that span many files. It reads your entire project, understands the architecture, and can make coordinated changes across dozens of files in a single operation.

Cursor and Claude Code serve different interaction patterns. Cursor is for interactive, visual editing where you see diffs, accept or reject suggestions, and maintain a traditional editor workflow. Claude Code is for larger, more autonomous operations where you describe a goal and let the agent figure out the implementation path. Many developers (including our team) use both: Cursor for day-to-day editing and Claude Code for major refactors, project scaffolding, and complex multi-file tasks.

Extensions That Work with Cursor

Because Cursor is a VS Code fork, most VS Code extensions work without modification. Here are the extensions our security and development team runs alongside Cursor’s built-in AI features:

Security and Compliance

• Semgrep: Static analysis with custom rules. Write rules for your organization’s security patterns and catch violations as you type.
• HashiCorp Terraform: Syntax highlighting, validation, and autocompletion for infrastructure-as-code. Pair with Cursor’s AI for security-aware Terraform suggestions.
• Docker: Dockerfile linting and container management. Cursor’s AI understands Dockerfile syntax and suggests hardened configurations.
• YAML: Schema validation for Kubernetes manifests, Ansible playbooks, and CI/CD pipelines.
• Remote SSH: Connect to GPU servers, cloud instances, and lab environments for remote development.

Development Productivity

• Ruff: Fast Python linting and formatting, written in Rust. Replaces flake8, isort, and black with a single tool.
• GitLens: Git blame, history, and visual diff. See who changed what and why, directly in the editor.
• Error Lens: Inline error and warning display. See diagnostics directly on the line that causes them.
• Thunder Client: API testing within the editor. Test endpoints without switching to Postman or curl.
• Jupyter: Notebook support with Cursor’s AI available in every cell.

Extensions to Avoid

• GitHub Copilot: Running Copilot alongside Cursor creates conflicting completions. Cursor’s built-in AI replaces Copilot entirely.
• Tabnine or Codeium extensions: Same conflict as Copilot. Multiple AI completion providers degrade the experience.
• Any extension that modifies the editor’s completion behavior: These may interfere with Cursor’s tab completion system.

Tips, Tricks, and Prompt Engineering

Essential Keyboard Shortcuts

Prompt Engineering for Better Suggestions

The quality of Cursor’s output depends directly on how you communicate with it. These patterns produce consistently better results:

Be specific about constraints. Instead of “write a login function,” say “write a login function that accepts email and password, validates both against Pydantic schemas, checks bcrypt-hashed passwords in PostgreSQL, returns a JWT with 15-minute expiry, and logs failed attempts with the source IP.”

Reference your codebase. “@file:src/models/user.py @file:src/config.py Write an endpoint that follows the same patterns as the existing user CRUD endpoints in @file:src/api/users.py.” Concrete references eliminate ambiguity.

State what you do not want. “Do not use global variables. Do not import from deprecated modules. Do not catch generic exceptions.” Negative constraints prevent common AI coding pitfalls.

Iterate, do not restart. If the first suggestion is 70% right, tell Cursor what to fix: “Good structure, but replace the requests library with httpx for async support, and add retry logic with exponential backoff.” Iterating on a partial solution is faster than regenerating from scratch.

Use the .cursorrules file for persistent instructions. If you find yourself repeating the same constraints in every prompt (“use type hints,” “prefer composition over inheritance,” “always validate input”), move those instructions into .cursorrules so they apply automatically.

Workflow Patterns

Explore, then edit. Start with chat (@codebase questions) to understand unfamiliar code, then switch to Cmd+K or Composer when you know what to change. This mirrors how experienced developers work: read first, then write.

Review every diff. Never accept AI-generated changes without reading the diff. Cursor shows changes clearly; use that visibility. AI-generated code is a first draft, not a final product.

Use Composer for scaffolding, Cmd+K for refinement. Composer excels at creating new files and broad refactors. Cmd+K excels at targeted edits within a single function or block. Use each tool where it is strongest.

Limitations and Considerations

Subscription Cost

Cursor Pro costs $20/month per seat. The free tier provides limited AI completions (approximately 2,000 per month) and limited chat messages, which is enough for evaluation but not for daily professional use. For teams, the Business tier at $40/month adds admin controls, centralized billing, and team-wide .cursorrules enforcement. This is more expensive than Copilot ($10/month individual, $19/month business), and the cost difference matters for larger teams.

Privacy Considerations

Even with Privacy Mode enabled, your code is transmitted to cloud-hosted models for processing during each request. For organizations with strict air-gap requirements or handling classified data, this may be a disqualifying factor. The custom API endpoint feature provides a partial solution by routing requests to self-hosted models, but it requires maintaining your own model infrastructure.

Review your organization’s data handling policies before adopting any cloud-based AI coding tool. For teams working under CMMC, HIPAA, or FedRAMP requirements, document the data flows and confirm compliance with your control framework.

Model Dependency

Cursor’s value is directly tied to the quality and availability of its underlying models. If Anthropic or OpenAI experience outages, Cursor’s AI features stop working. The editor itself remains functional as a VS Code fork, but the features you are paying for are unavailable. During peak usage periods, you may experience slower response times or rate limiting on the Pro tier.

VS Code Sync Lag

Because Cursor is a fork rather than a downstream consumer of VS Code, there is a delay between new VS Code releases and when those changes appear in Cursor. Security patches, new language features, and API changes in VS Code take time to merge into Cursor’s fork. This delay is typically days to weeks, rarely months, but it means Cursor is not always on the latest VS Code version.

Extension Compatibility

While most VS Code extensions work in Cursor, some do not. Extensions that hook deeply into VS Code’s completion system, language server protocol, or editor rendering may conflict with Cursor’s modifications. Test critical extensions before fully migrating. Microsoft-published extensions that require a VS Code telemetry handshake may not function in the fork.

Frequently Asked Questions

Is Cursor safe for commercial and enterprise code?

With Privacy Mode enabled, Cursor does not retain your code after processing requests and does not use it for model training. The Business tier adds SOC 2 compliance documentation and admin controls. For enterprise use, evaluate Cursor’s data processing agreement against your compliance requirements. Many enterprises use Cursor in production, but each organization’s risk tolerance and regulatory obligations differ.

Can I use Cursor offline?

The editor functions offline as a VS Code fork: file editing, terminal, extensions, and debugging all work without an internet connection. The AI features (completions, chat, composer) require internet access to reach the model endpoints. There is no fully offline AI mode in the current version, though the custom API endpoint feature could theoretically point to a locally hosted model on the same machine.

Does Cursor replace VS Code or run alongside it?

Cursor installs as a separate application. You can run it alongside VS Code without conflicts. They share no configuration by default, though Cursor can import your VS Code settings during initial setup. Many developers keep both installed: VS Code for projects where they prefer Copilot or do not need AI features, and Cursor for AI-intensive work.

How does Cursor handle large codebases?

Cursor indexes your codebase locally using embeddings. For large projects (100K+ files), the initial indexing takes several minutes but runs in the background. Once indexed, @codebase queries are fast. You can exclude directories (node_modules, build artifacts, virtual environments) from indexing via settings to reduce noise and improve relevance. For monorepos, use @folder to scope queries to the relevant subdirectory.

What programming languages does Cursor support?

Cursor supports every language that VS Code supports, which is effectively every language with a VS Code extension. The AI features work with all languages, though the quality of suggestions varies by language. Python, TypeScript, JavaScript, Go, Rust, Java, and C++ get the best suggestions because the underlying models have the most training data for these languages. Less common languages (Haskell, Elixir, Zig) work but with less precise suggestions.

Can I use my own API keys instead of a Cursor subscription?

Yes. Cursor allows you to bring your own API keys for OpenAI, Anthropic, and other supported providers. This bypasses the Cursor subscription model for AI requests, though you pay the provider directly based on token usage. This option is useful for teams that already have enterprise API agreements with model providers or want more control over model selection and spending.

Is Cursor worth it if I already use GitHub Copilot?

If your current workflow with Copilot meets your needs, switching has a real cost in terms of learning new patterns and potential extension compatibility issues. Cursor is worth evaluating if you frequently work on complex multi-file refactors (where Composer excels), if you need deeper codebase context in AI queries, or if you want to use Claude models as your primary AI assistant. The free tier lets you evaluate before committing.

Getting Started

Install Cursor, import your VS Code settings, create a .cursorrules file for your current project, and start with the chat panel (@codebase queries) to explore how Cursor understands your code. Spend a week using Tab completions and Cmd+K inline edits before trying Composer for multi-file changes. The learning curve is gentle if you already use VS Code, and the productivity gains compound as the AI learns your project’s patterns.

If your organization is evaluating AI-assisted development tools for security-sensitive work, or if you need help configuring AI development environments, secure coding workflows, or GPU infrastructure for ML workloads, contact our team at Petronella for a consultation.

About the Author: Craig Petronella is the CEO of Petronella Technology Group, a cybersecurity and IT infrastructure firm in Raleigh, NC. With CMMC-RP, CCNA, CWNE, and DFE certifications and over 30 years in IT, Craig’s team uses Cursor daily for security engineering, AI development, and infrastructure automation.