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
September 28, 2020
ClickShare Vulnerabilities May Have Been Patched, But They Mask a Much Bigger Problem

Shifting security fixes back towards the development process isn't easy, but is necessary in today's world where even seemingly simple devices like presentation tools are both surprisingly complex, and also networked into everything else.

Blog
September 23, 2020
Coders Conquer Security OWASP Top 10 API Series - Excessive Data Exposure

The actual mechanics behind this vulnerability are similar to others, but excessive data exposure, in this case, is defined as involving legally protected or highly sensitive data.

Blog
September 16, 2020
Coders Conquer Security OWASP Top 10 API Series - Broken Authentication

Authentication often acts as a gateway to both an application and potentially to the rest of a network, so they are tempting targets for attackers. If an authentication process is broken or vulnerable, there is a good chance that attackers will discover that weakness and exploit it.

Blog
September 10, 2020
Expert Interview: Infrastructure as Code with Oscar Quintas

We'd like to shine the spotlight on one of our experts, Oscar Quintas. He's part of our Product Content team, working as a Senior Security Researcher. He's also our resident sorcerer on all things Infrastructure as Code (IaC).

Blog
September 9, 2020
Coders Conquer Security OWASP Top 10 API Series - Broken Object Level Authorization

In general, object level authorization checks should be included for every function that accesses a data source using an input from the user, and failure to do so comes at a great risk.

Blog
August 25, 2020
Death by Doki: A new Docker vulnerability with serious bite (and what you can do about it)

Cyberattacks are only getting more frequent, and threats affecting Linux-based infrastructure are becoming more common, with the end goal being an opportunity to crack open a loot chest of sensitive data stored in the cloud.

Blog
August 3, 2020
Is your organization really DevSec-ready? Put it to the test.

With your organization in mind, think about these questions in the context of your role. How would it fare when put to the DevSec test?

Blog
July 20, 2020
Strike first, strike hard: Why curated secure coding courses extend no mercy to cyber threats

A curated course containing the exact modules in which your developers would need to show proficiency will have a potent impact, and allow them to hit the ground running when it comes to security best practices in their day-to-day work.

Blog
July 15, 2020
Want developers to code with security awareness? Bring the training to them.

We already know there is too much going on in a workday, so what incentive do developers have to schlep off to a classroom, or context-switch to go through five steps to access static theory-based training?

Blog
July 8, 2020
COVID-19 contact tracing: What's the secure coding situation?

The idea behind contact tracing apps is sound. This technology, when functioning well, would ensure hotspots are quickly revealed and comprehensive testing can occur - both essential components of fighting the spread of a contagious virus.

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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