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
March 30, 2021
What part does the human element play in the future of secure coding?

As the number of cyber-threats continues to grow, organizations are making daily trade-offs between security, practicality, and speed – exposing themselves to risks in the process.

Blog
March 25, 2021
We need heroes to secure our code. Have developers got what it takes?

In a world where cyber threats continue to multiply, are your coders stepping up? Is the human element of secure coding – the all-important developer – ready to play their part in securing our connected world? To answer this question, let’s look at some insights from a recent study on developers attitudes towards secure coding, secure code practices, and security operations, conducted by Secure Code Warrior with Evans Data Corp.

Blog
March 23, 2021
Shifting the focus from reactive to proactive, with human-led secure coding

The same 10 software vulnerabilities have caused more security breaches in the last 20+ years than any others. And yet, many businesses still opt for post-breach, post-event remediation; muddling through the human and business ramifications of it all. But now a new research study points to a new, human-led direction.

Blog
March 17, 2021
Happy birthday SQL injection, the bug that can’t be squashed

It's SQL injection’s 22nd birthday, and despite this vulnerability being old enough to drink, we’re letting it get the better of us instead of squashing it for good.

Blog
March 10, 2021
Building trust: The path to true security synergy between AppSec and developers

A relationship that is built on the shaky foundations of mistrust is, well, best approached with low expectations. Sadly, this can be the state of the working relationship between developers and the AppSec team within an organization.

Blog
February 21, 2021
Try This Online Java Gotchas Quiz

A fun little Java Gotchas quiz and supporting Github repo showing some gotchas and how to fix them

Blog
February 15, 2021
Running IntelliJ Inspections From Continuous Integration

Learn how to run Sensei and IntelliJ Intention Actions in batch mode as Inspections within the IDE, from the Command-Line, and in Continuous Integration.

Blog
February 10, 2021
Starting "left of left": Is secure code always quality code?

Code of a certain level of quality is by its definition also secure, but all secure code is not necessarily good quality. Is starting “left of left” the formula to ensure pure secure coding standards?

Blog
February 7, 2021
Java Gotchas - Bitwise vs Boolean Operators

In this blog post we take a look at a common Java coding mistake (using a bitwise operator instead of a conditional operator), the error it makes our code vulnerable to, and how we can use Sensei to fix and detect the issue.

Blog
February 4, 2021
For developers to help slay the cybercrime beast, training is a quest in two parts

The playing field between the heroes and villains in cybersecurity is notoriously unfair. Sensitive data is the new gold, and attackers adapt quickly to circumvent defenses, exploiting security bugs large and small for potential paydirt.

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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Read Case study

"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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Read Case study

“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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Read Case study

“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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