Stop AI software risk before it starts

Ship secure, high-quality code at every commit – no matter who (or what) wrote it.

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AI Software Governance

Adopt AI-driven development with confidence

Visibility into shadow AI. Audit-ready traceability for every commit — human or agent. Adaptive learning that turns every finding into stronger secure-coding capability. SCW is the AI Software Governance platform built for the agentic era.

Operationalize AI governance across software development.

Enable AI-assisted development while maintaining security oversight. Gain visibility into AI usage, apply governance workflows at commit, and align development practices with enterprise risk thresholds.

Securely scale AI software development

  • Gain enterprise-wide visibility into AI-assisted development
  • Strengthen secure coding capability across engineering teams
  • Train developers to safely review AI-generated code
AI Governance
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Prevent AI-introduced vulnerabilities at commit.

Make AI usage visible, apply secure coding guardrails at commit, and align AI-assisted development with security standards to prevent vulnerabilities across human and AI-generated code.

Reduce introduced vulnerabilities by 53%+

  • Build secure coding capability across development teams
  • Deliver policy-aligned guidance directly in developer tools
  • See how AI-generated code impacts software risk
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Scale AI development without slowing delivery.

Make AI-assisted development secure and measurable — reducing rework, avoiding security review bottlenecks, and enabling teams to ship faster with confidence.

Reduce MTTR by up to 82%

  • Improve developer security skills with adaptive learning
  • Deliver real-time guidance inside developer tools
  • Fix vulnerabilities earlier to reduce cost of rework
Engineering
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Why we’re awesome

Secure and built for the tools you already use

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*In progress
Total interactive learning activities
11k+
Vulnerability topics & security concepts
650+
AI / LLM focused learning activities
800+
Coding languages and frameworks
75+

Our latest content

Blog
February 1, 2021
What is static analysis?

Learn about Static Analysis and how can it help you write better code with examples of 5 IDE based approaches and plugins.

Blog
January 27, 2021
Six Years of Secure Code Warrior: Are we grown up yet?

It’s that special time of the year (for us, anyway) where I reflect on our most recent lap around the sun, and what has been done in the previous 365 days to position us for a new year of growth, lessons, and inevitable unpredictability.

Blog
January 5, 2021
2021 cybersecurity predictions: The intergalactic battle begins

We’re predicting that 2021 is the year we take a new kind of space race into the mainstream: keeping our galaxy safe from cyber threats.

Blog
December 22, 2020
Coders Conquer Security OWASP Top 10 API Series - Improper Assets Management

This vulnerability is more of a human or management problem that allows older APIs to remain in place long after they should have been replaced by newer, more secure versions.

Blog
December 21, 2020
Amending Method and Class Visibility for JUnit 5

Learn how Sensei can help migration by identifying deprecated patterns and prompting you with the fix to use going forward.

Blog
December 15, 2020
My pentester, my enemy? Developers reveal what they really think about pentesting and static analysis results

Penetration testing and static analysis scanning tools (better known as SAST) are just part of the overall process to mitigate security risks, operating rather independently from what we do - until the code bounces back to us for hotfixes, of course!

Blog
December 9, 2020
The future of work is flexible, and it's great for cybersecurity

Whether discomfort comes from the unknowns of a new way of working, a little mistrust, or perhaps not believing remote work, I find that companies who are resistant to it tend to fall behind in terms of attracting top talent, maintaining global reach and frankly, moving with the times.

Blog
November 25, 2020
Coders Conquer Security OWASP Top 10 API Series - Insufficient Logging and Monitoring

The insufficient logging and monitoring flaw mostly happens as a result of a failed cybersecurity plan in regards to logging all failed authentication attempts, denied access, and input validation errors.

Blog
November 23, 2020
Sharing Cookbooks within a Team

Learn how to share Sensei cookbooks and help everyone in your team improve their code quality and productivity.

Blog
November 11, 2020
Introducing Missions: The next phase of developer-centric security training

We're thrilled to announce a brand new feature release on the Secure Code Warrior platform: Missions. This all-new challenge category is the next phase in developer-ified security training, moving users from the recall of security knowledge, to applying it in a real-world simulation environment.

Observability

Make AI-driven development risk visible

See how AI coding is used, the risk it creates, and the behavior behind it—so you can stop vulnerabilities before they ship.

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"The security champion network has been seen as a key control of that program. For one team the impact felt was enormous - with an 82% reduction in mean time to fix a vulnerability."

Mads Howard
People-Centered Security Lead at Sage

Discover shadow AI

See which AI tools, LLMs and MCPs are being used across your teams.

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Correlate true risk

Connect AI-assisted code with developer skill and introduced vulnerabilities at commit.

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Trace AI tool usage

Understand where AI-assisted development occurs—by repository, project, and contributor.

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distribution chart

Prioritize risk signals

Highlight the most urgent commit-level risk hotspots across teams and repositories.

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Learning

Reduce vulnerabilities at the source

Hands-on secure coding and AI security learning delivered in real-world developer workflows — helping organizations reduce vulnerabilities by 53%+.

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“Our partnership with Secure Code Warrior has been smooth and productive. They helped us implement and improve our training program, resulting in measurable risk reduction and a stronger culture of secure development.”

Sebastiaan Rijnbout
Product Owner of Development Services 
at Kamer van Koophandel

Gamified hands-on learning

Interactive play modes – including Labs, Quests, Missions, and Tournaments – build secure coding habits.

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Secure AI code development

Over 800 AI, LLM, and MCP activities teach developers to validate AI-generated code safely and efficiently.

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Empower teams to optimize

Embed a security mindset into your development process with learning beyond developer training.

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Benchmark your security program

Understand how your program compares to peers and define standards aligned to your risk strategy.

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Governance

Scale AI-driven development with confidence

Gain visibility into how AI contributes to your code, connect activity to real risk, and align development to enterprise standards — so you can reduce risk and prove trust before code reaches production.

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“Secure Code Warrior has helped us increase developer productivity, accelerate our ability to bring products and improvements to market, and significantly reduce costs and risk over time.”

Alan Osborne

Chief Information Security Officer at Paysafe

Govern AI coding policies

Bring governance visibility to AI-assisted development and help teams consistently meet secure coding standards.

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Set AI usage policies

Restrict usage to authorized AI tools, LLMs, and coding agents at the point of commit.

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Flag risk signals

Highlight AI usage and policy misalignment to support secure development decisions.

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Commit policy

Trigger policy remediation

Assign targeted adaptive learning when risky behavior or unauthorized AI use is detected.

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Govern AI-driven development before it ships

See developer risk, enforce policy, and prevent vulnerabilities across your software development lifecycle.

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AI software governance FAQs

Understand AI software governance and how to reduce AI-driven software risk

Learn what AI software governance is, why it matters, and how Secure Code Warrior helps organizations safely adopt AI-assisted development.

What is AI software governance?

AI software governance is the ability to see, measure, control, and enforce how artificial intelligence is used in software development. It includes visibility into AI coding assistants and LLMs, commit-level risk analysis, policy enforcement, and preventing risky AI-generated code from reaching production.

Why is AI software governance important?

As organizations move from developers casually using AI chatbots to AI agents autonomously generating and modifying code, the risk surface expands dramatically. These tools can introduce vulnerabilities, insecure patterns, and compliance exposure at machine speed.

AI software governance enables organizations to adopt AI safely by making AI usage visible, enforcing policy controls, and preventing AI-introduced risk before code reaches production.

How is AI development governance different from DevSecOps?

DevSecOps integrates security testing into CI/CD pipelines to detect vulnerabilities. AI development governance goes further by making AI usage visible, correlating AI-assisted commits with developer skill, enforcing AI model policies at commit, and improving secure coding behavior. DevSecOps detects risk; AI governance prevents it.

How does Secure Code Warrior reduce AI software risk?

Securing AI-generated code requires visibility into AI tool usage, commit-level risk analysis, and governance oversight across development workflows. Secure Code Warrior provides AI observability, vulnerability correlation, and developer capability insights within a unified AI software governance platform.

How do you prove AI risk reduction to leadership or auditors?

Secure Code Warrior provides enterprise dashboards, AI model traceability, and governance reporting that demonstrate measurable reductions in introduced vulnerabilities, improved developer Trust Score®™ metrics, and policy compliance across teams.

The platform also maintains audit-ready traceability of who — or what — generated specific code, including developers, AI coding assistants, LLMs, and autonomous agents. This creates verifiable AI software supply chain accountability for leadership, regulators, and auditors.

Still have questions?

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