Setting the Standard: SCW Releases Free AI Coding Security Rules on GitHub
AI-assisted development is no longer on the horizon — it’s here, and it’s rapidly reshaping how software is written. Tools like GitHub Copilot, Cline, Roo, Cursor, Aider, and Windsurf are transforming developers into co-pilots of their own, enabling faster iteration and accelerating everything from prototyping to major refactoring projects.
But with this shift comes a familiar tension: speed vs. security.
At Secure Code Warrior, we’ve been thinking a lot about how to help developers stay secure while working with AI coding tools. That’s why we’re excited to launch something simple, powerful, and immediately useful: our AI Security Rules — a public, free-to-use resource available to everyone on GitHub. You don’t need to be a Secure Code Warrior customer to use them; we’re providing these rules as a free, community-driven foundation that anyone can adopt and extend into their own projects.
These rules are designed to act as guardrails, nudging AI tools toward safer coding practices, even when developers are moving at breakneck speed.
Summary for those in a hurry:
As AI coding tools like Copilot and Cursor become essential to modern development, security can't be an afterthought. That’s why we’ve built a set of lightweight, security-first rulesets designed to guide AI code generation toward safer defaults.
- Covers web frontend, backend, and mobile
- Easy to drop into AI tools
- Public, free-to-use, and ready to adopt into your own projects
Explore the rules → https://github.com/SecureCodeWarrior/ai-security-rules
Let’s make secure coding the default—even with AI at the keyboard.
1. Why rules matter in the age of AI-assisted coding
AI coding tools are incredibly helpful, but not infallible. While they can generate working code quickly, they often lack the nuance to understand the specific standards, conventions, and security policies of a given team or project.
This is where project-level rule files come into play.
Modern AI tools like Cursor and Copilot support configuration files that influence how code is generated. These rule files act like a whisper in the AI’s ear, telling it:
“In this project, we never concatenate SQL strings.”
“Prefer fetch with safe headers over insecure defaults.”
“Avoid using eval() unless you want a security audit.”
These rules aren’t a silver bullet, or a substitute for strong code review practices and security tooling, however they can help align AI-generated code with the practices that teams already follow or should follow for secure development.
2. What we built (and what we didn’t)
Our Starter Rules are available now in a public GitHub repository. They’re:
- Organized by domain – including web frontend, backend, and mobile
- Security-focused – covering recurring issues like injection flaws, unsafe handling, CSRF protection, weak auth flows, and more
- Lightweight by design – they’re meant to be a practical starting point, not an exhaustive rulebook
We know how valuable your AI context windows are and how quickly code consumes those tokens, so we’ve kept our rules clear, concise, and strictly focused on security. We made a deliberate decision to avoid language- or framework-specific guidance, opting instead for broadly applicable, high-impact security practices that work across a wide range of environments without becoming opinionated on architecture or design.
These rules are written to be easily dropped into the supported config formats of AI tools, with little to no refactoring. Think of them as a starting set of policies that nudge the AI toward secure defaults.
3. A new layer of defense
Here’s what this looks like in practice:
- When the AI suggests code that handles user input, it leans toward validation and encoding, not bare processing.
- When building database queries, it’s more likely to recommend parameterization, not string concatenation.
- When generating frontend auth flows, the AI will be more likely to promote token handling best practices, not insecure local storage hacks.
None of this replaces strategic developer risk management within a security program, including continuous security upskilling. It also doesn’t eliminate the need for security-proficient developers, especially as they increasingly prompt LLMs and review AI-generated code. These guardrails add a meaningful layer of defense—especially when developers are moving fast, multitasking, or just trusting the tool a little too much.
What’s next?
This isn’t a finished product—it’s a starting point.
As AI coding tools evolve, so must our approach to secure development. Our AI Security Rules are free to use, adaptable, and extendable to your projects. We’re committed to continuously evolving these rulesets, and we’d love your input — so try them out and let us know what you think.
Explore the rules on GitHub
Read the Using Rules guideline in SCW Explore
AI-assisted coding is already reshaping how we build software. Let’s make sure it’s secure from the start.
AI-assisted development is no longer on the horizon — it’s here, and it’s rapidly reshaping how software is written. Tools like GitHub Copilot, Cline, Roo, Cursor, Aider, and Windsurf are transforming developers into co-pilots of their own, enabling faster iteration and accelerating everything from prototyping to major refactoring projects.
Shannon Holt is a cybersecurity product marketer with a background in application security, cloud security services, and compliance standards like PCI-DSS and HITRUST.
Secure Code Warrior is here for your organization to help you secure code across the entire software development lifecycle and create a culture in which cybersecurity is top of mind. Whether you’re an AppSec Manager, Developer, CISO, or anyone involved in security, we can help your organization reduce risks associated with insecure code.
Book a demo
Shannon Holt is a cybersecurity product marketer with a background in application security, cloud security services, and compliance standards like PCI-DSS and HITRUST.
Shannon Holt is a cybersecurity product marketer with a background in application security, cloud security services, and compliance standards like PCI-DSS and HITRUST. She’s passionate about making secure development and compliance more practical and approachable for technical teams, bridging the gap between security expectations and the realities of modern software development.
AI-assisted development is no longer on the horizon — it’s here, and it’s rapidly reshaping how software is written. Tools like GitHub Copilot, Cline, Roo, Cursor, Aider, and Windsurf are transforming developers into co-pilots of their own, enabling faster iteration and accelerating everything from prototyping to major refactoring projects.
But with this shift comes a familiar tension: speed vs. security.
At Secure Code Warrior, we’ve been thinking a lot about how to help developers stay secure while working with AI coding tools. That’s why we’re excited to launch something simple, powerful, and immediately useful: our AI Security Rules — a public, free-to-use resource available to everyone on GitHub. You don’t need to be a Secure Code Warrior customer to use them; we’re providing these rules as a free, community-driven foundation that anyone can adopt and extend into their own projects.
These rules are designed to act as guardrails, nudging AI tools toward safer coding practices, even when developers are moving at breakneck speed.
Summary for those in a hurry:
As AI coding tools like Copilot and Cursor become essential to modern development, security can't be an afterthought. That’s why we’ve built a set of lightweight, security-first rulesets designed to guide AI code generation toward safer defaults.
- Covers web frontend, backend, and mobile
- Easy to drop into AI tools
- Public, free-to-use, and ready to adopt into your own projects
Explore the rules → https://github.com/SecureCodeWarrior/ai-security-rules
Let’s make secure coding the default—even with AI at the keyboard.
1. Why rules matter in the age of AI-assisted coding
AI coding tools are incredibly helpful, but not infallible. While they can generate working code quickly, they often lack the nuance to understand the specific standards, conventions, and security policies of a given team or project.
This is where project-level rule files come into play.
Modern AI tools like Cursor and Copilot support configuration files that influence how code is generated. These rule files act like a whisper in the AI’s ear, telling it:
“In this project, we never concatenate SQL strings.”
“Prefer fetch with safe headers over insecure defaults.”
“Avoid using eval() unless you want a security audit.”
These rules aren’t a silver bullet, or a substitute for strong code review practices and security tooling, however they can help align AI-generated code with the practices that teams already follow or should follow for secure development.
2. What we built (and what we didn’t)
Our Starter Rules are available now in a public GitHub repository. They’re:
- Organized by domain – including web frontend, backend, and mobile
- Security-focused – covering recurring issues like injection flaws, unsafe handling, CSRF protection, weak auth flows, and more
- Lightweight by design – they’re meant to be a practical starting point, not an exhaustive rulebook
We know how valuable your AI context windows are and how quickly code consumes those tokens, so we’ve kept our rules clear, concise, and strictly focused on security. We made a deliberate decision to avoid language- or framework-specific guidance, opting instead for broadly applicable, high-impact security practices that work across a wide range of environments without becoming opinionated on architecture or design.
These rules are written to be easily dropped into the supported config formats of AI tools, with little to no refactoring. Think of them as a starting set of policies that nudge the AI toward secure defaults.
3. A new layer of defense
Here’s what this looks like in practice:
- When the AI suggests code that handles user input, it leans toward validation and encoding, not bare processing.
- When building database queries, it’s more likely to recommend parameterization, not string concatenation.
- When generating frontend auth flows, the AI will be more likely to promote token handling best practices, not insecure local storage hacks.
None of this replaces strategic developer risk management within a security program, including continuous security upskilling. It also doesn’t eliminate the need for security-proficient developers, especially as they increasingly prompt LLMs and review AI-generated code. These guardrails add a meaningful layer of defense—especially when developers are moving fast, multitasking, or just trusting the tool a little too much.
What’s next?
This isn’t a finished product—it’s a starting point.
As AI coding tools evolve, so must our approach to secure development. Our AI Security Rules are free to use, adaptable, and extendable to your projects. We’re committed to continuously evolving these rulesets, and we’d love your input — so try them out and let us know what you think.
Explore the rules on GitHub
Read the Using Rules guideline in SCW Explore
AI-assisted coding is already reshaping how we build software. Let’s make sure it’s secure from the start.
AI-assisted development is no longer on the horizon — it’s here, and it’s rapidly reshaping how software is written. Tools like GitHub Copilot, Cline, Roo, Cursor, Aider, and Windsurf are transforming developers into co-pilots of their own, enabling faster iteration and accelerating everything from prototyping to major refactoring projects.
But with this shift comes a familiar tension: speed vs. security.
At Secure Code Warrior, we’ve been thinking a lot about how to help developers stay secure while working with AI coding tools. That’s why we’re excited to launch something simple, powerful, and immediately useful: our AI Security Rules — a public, free-to-use resource available to everyone on GitHub. You don’t need to be a Secure Code Warrior customer to use them; we’re providing these rules as a free, community-driven foundation that anyone can adopt and extend into their own projects.
These rules are designed to act as guardrails, nudging AI tools toward safer coding practices, even when developers are moving at breakneck speed.
Summary for those in a hurry:
As AI coding tools like Copilot and Cursor become essential to modern development, security can't be an afterthought. That’s why we’ve built a set of lightweight, security-first rulesets designed to guide AI code generation toward safer defaults.
- Covers web frontend, backend, and mobile
- Easy to drop into AI tools
- Public, free-to-use, and ready to adopt into your own projects
Explore the rules → https://github.com/SecureCodeWarrior/ai-security-rules
Let’s make secure coding the default—even with AI at the keyboard.
1. Why rules matter in the age of AI-assisted coding
AI coding tools are incredibly helpful, but not infallible. While they can generate working code quickly, they often lack the nuance to understand the specific standards, conventions, and security policies of a given team or project.
This is where project-level rule files come into play.
Modern AI tools like Cursor and Copilot support configuration files that influence how code is generated. These rule files act like a whisper in the AI’s ear, telling it:
“In this project, we never concatenate SQL strings.”
“Prefer fetch with safe headers over insecure defaults.”
“Avoid using eval() unless you want a security audit.”
These rules aren’t a silver bullet, or a substitute for strong code review practices and security tooling, however they can help align AI-generated code with the practices that teams already follow or should follow for secure development.
2. What we built (and what we didn’t)
Our Starter Rules are available now in a public GitHub repository. They’re:
- Organized by domain – including web frontend, backend, and mobile
- Security-focused – covering recurring issues like injection flaws, unsafe handling, CSRF protection, weak auth flows, and more
- Lightweight by design – they’re meant to be a practical starting point, not an exhaustive rulebook
We know how valuable your AI context windows are and how quickly code consumes those tokens, so we’ve kept our rules clear, concise, and strictly focused on security. We made a deliberate decision to avoid language- or framework-specific guidance, opting instead for broadly applicable, high-impact security practices that work across a wide range of environments without becoming opinionated on architecture or design.
These rules are written to be easily dropped into the supported config formats of AI tools, with little to no refactoring. Think of them as a starting set of policies that nudge the AI toward secure defaults.
3. A new layer of defense
Here’s what this looks like in practice:
- When the AI suggests code that handles user input, it leans toward validation and encoding, not bare processing.
- When building database queries, it’s more likely to recommend parameterization, not string concatenation.
- When generating frontend auth flows, the AI will be more likely to promote token handling best practices, not insecure local storage hacks.
None of this replaces strategic developer risk management within a security program, including continuous security upskilling. It also doesn’t eliminate the need for security-proficient developers, especially as they increasingly prompt LLMs and review AI-generated code. These guardrails add a meaningful layer of defense—especially when developers are moving fast, multitasking, or just trusting the tool a little too much.
What’s next?
This isn’t a finished product—it’s a starting point.
As AI coding tools evolve, so must our approach to secure development. Our AI Security Rules are free to use, adaptable, and extendable to your projects. We’re committed to continuously evolving these rulesets, and we’d love your input — so try them out and let us know what you think.
Explore the rules on GitHub
Read the Using Rules guideline in SCW Explore
AI-assisted coding is already reshaping how we build software. Let’s make sure it’s secure from the start.
Click on the link below and download the PDF of this resource.
Secure Code Warrior is here for your organization to help you secure code across the entire software development lifecycle and create a culture in which cybersecurity is top of mind. Whether you’re an AppSec Manager, Developer, CISO, or anyone involved in security, we can help your organization reduce risks associated with insecure code.
View reportBook a demo
Shannon Holt is a cybersecurity product marketer with a background in application security, cloud security services, and compliance standards like PCI-DSS and HITRUST.
Shannon Holt is a cybersecurity product marketer with a background in application security, cloud security services, and compliance standards like PCI-DSS and HITRUST. She’s passionate about making secure development and compliance more practical and approachable for technical teams, bridging the gap between security expectations and the realities of modern software development.
AI-assisted development is no longer on the horizon — it’s here, and it’s rapidly reshaping how software is written. Tools like GitHub Copilot, Cline, Roo, Cursor, Aider, and Windsurf are transforming developers into co-pilots of their own, enabling faster iteration and accelerating everything from prototyping to major refactoring projects.
But with this shift comes a familiar tension: speed vs. security.
At Secure Code Warrior, we’ve been thinking a lot about how to help developers stay secure while working with AI coding tools. That’s why we’re excited to launch something simple, powerful, and immediately useful: our AI Security Rules — a public, free-to-use resource available to everyone on GitHub. You don’t need to be a Secure Code Warrior customer to use them; we’re providing these rules as a free, community-driven foundation that anyone can adopt and extend into their own projects.
These rules are designed to act as guardrails, nudging AI tools toward safer coding practices, even when developers are moving at breakneck speed.
Summary for those in a hurry:
As AI coding tools like Copilot and Cursor become essential to modern development, security can't be an afterthought. That’s why we’ve built a set of lightweight, security-first rulesets designed to guide AI code generation toward safer defaults.
- Covers web frontend, backend, and mobile
- Easy to drop into AI tools
- Public, free-to-use, and ready to adopt into your own projects
Explore the rules → https://github.com/SecureCodeWarrior/ai-security-rules
Let’s make secure coding the default—even with AI at the keyboard.
1. Why rules matter in the age of AI-assisted coding
AI coding tools are incredibly helpful, but not infallible. While they can generate working code quickly, they often lack the nuance to understand the specific standards, conventions, and security policies of a given team or project.
This is where project-level rule files come into play.
Modern AI tools like Cursor and Copilot support configuration files that influence how code is generated. These rule files act like a whisper in the AI’s ear, telling it:
“In this project, we never concatenate SQL strings.”
“Prefer fetch with safe headers over insecure defaults.”
“Avoid using eval() unless you want a security audit.”
These rules aren’t a silver bullet, or a substitute for strong code review practices and security tooling, however they can help align AI-generated code with the practices that teams already follow or should follow for secure development.
2. What we built (and what we didn’t)
Our Starter Rules are available now in a public GitHub repository. They’re:
- Organized by domain – including web frontend, backend, and mobile
- Security-focused – covering recurring issues like injection flaws, unsafe handling, CSRF protection, weak auth flows, and more
- Lightweight by design – they’re meant to be a practical starting point, not an exhaustive rulebook
We know how valuable your AI context windows are and how quickly code consumes those tokens, so we’ve kept our rules clear, concise, and strictly focused on security. We made a deliberate decision to avoid language- or framework-specific guidance, opting instead for broadly applicable, high-impact security practices that work across a wide range of environments without becoming opinionated on architecture or design.
These rules are written to be easily dropped into the supported config formats of AI tools, with little to no refactoring. Think of them as a starting set of policies that nudge the AI toward secure defaults.
3. A new layer of defense
Here’s what this looks like in practice:
- When the AI suggests code that handles user input, it leans toward validation and encoding, not bare processing.
- When building database queries, it’s more likely to recommend parameterization, not string concatenation.
- When generating frontend auth flows, the AI will be more likely to promote token handling best practices, not insecure local storage hacks.
None of this replaces strategic developer risk management within a security program, including continuous security upskilling. It also doesn’t eliminate the need for security-proficient developers, especially as they increasingly prompt LLMs and review AI-generated code. These guardrails add a meaningful layer of defense—especially when developers are moving fast, multitasking, or just trusting the tool a little too much.
What’s next?
This isn’t a finished product—it’s a starting point.
As AI coding tools evolve, so must our approach to secure development. Our AI Security Rules are free to use, adaptable, and extendable to your projects. We’re committed to continuously evolving these rulesets, and we’d love your input — so try them out and let us know what you think.
Explore the rules on GitHub
Read the Using Rules guideline in SCW Explore
AI-assisted coding is already reshaping how we build software. Let’s make sure it’s secure from the start.
Table of contents
Shannon Holt is a cybersecurity product marketer with a background in application security, cloud security services, and compliance standards like PCI-DSS and HITRUST.
Secure Code Warrior is here for your organization to help you secure code across the entire software development lifecycle and create a culture in which cybersecurity is top of mind. Whether you’re an AppSec Manager, Developer, CISO, or anyone involved in security, we can help your organization reduce risks associated with insecure code.
Book a demoDownloadResources to get you started
Secure by Design: Defining Best Practices, Enabling Developers and Benchmarking Preventative Security Outcomes
In this research paper, Secure Code Warrior co-founders, Pieter Danhieux and Dr. Matias Madou, Ph.D., along with expert contributors, Chris Inglis, Former US National Cyber Director (now Strategic Advisor to Paladin Capital Group), and Devin Lynch, Senior Director, Paladin Global Institute, will reveal key findings from over twenty in-depth interviews with enterprise security leaders including CISOs, a VP of Application Security, and software security professionals.
Benchmarking Security Skills: Streamlining Secure-by-Design in the Enterprise
Finding meaningful data on the success of Secure-by-Design initiatives is notoriously difficult. CISOs are often challenged when attempting to prove the return on investment (ROI) and business value of security program activities at both the people and company levels. Not to mention, it’s particularly difficult for enterprises to gain insights into how their organizations are benchmarked against current industry standards. The President’s National Cybersecurity Strategy challenged stakeholders to “embrace security and resilience by design.” The key to making Secure-by-Design initiatives work is not only giving developers the skills to ensure secure code, but also assuring the regulators that those skills are in place. In this presentation, we share a myriad of qualitative and quantitative data, derived from multiple primary sources, including internal data points collected from over 250,000 developers, data-driven customer insights, and public studies. Leveraging this aggregation of data points, we aim to communicate a vision of the current state of Secure-by-Design initiatives across multiple verticals. The report details why this space is currently underutilized, the significant impact a successful upskilling program can have on cybersecurity risk mitigation, and the potential to eliminate categories of vulnerabilities from a codebase.
Resources to get you started
Close the Loop on Vulnerabilities with Secure Code Warrior + HackerOne
Secure Code Warrior is excited to announce our new integration with HackerOne, a leader in offensive security solutions. Together, we're building a powerful, integrated ecosystem. HackerOne pinpoints where vulnerabilities are actually happening in real-world environments, exposing the "what" and "where" of security issues.
Revealed: How the Cyber Industry Defines Secure by Design
In our latest white paper, our Co-Founders, Pieter Danhieux and Dr. Matias Madou, Ph.D., sat down with over twenty enterprise security leaders, including CISOs, AppSec leaders and security professionals, to figure out the key pieces of this puzzle and uncover the reality behind the Secure by Design movement. It’s a shared ambition across the security teams, but no shared playbook.
Is Vibe Coding Going to Turn Your Codebase Into a Frat Party?
Vibe coding is like a college frat party, and AI is the centerpiece of all the festivities, the keg. It’s a lot of fun to let loose, get creative, and see where your imagination can take you, but after a few keg stands, drinking (or, using AI) in moderation is undoubtedly the safer long-term solution.
Developer-driven coding.
Securely.
Contact us today and make software security an intrinsic part of your development process.
