AI may write code, but skill secures it.

Our enterprise secure coding platform builds the skills needed to secure both human and AI-generated code without slowing delivery.

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ナンバーワンのセキュアコーディング研修会社より
スキルギャップ

AI accelerates code. AI security skills must keep pace.

AI coding assistants can generate production-ready code in seconds. But speed does not equal security. AI security training helps developers identify vulnerabilities in AI-generated code, prevent prompt injection, and apply secure coding practices across modern AI workflows.

開発者には現在、以下のことが求められています：
Identify vulnerabilities in AI-generated code
LLMによって導入された不安定なパターンを認識する
言語を問わずセキュアコーディング基準を適用する
Prevent new risks like prompt injection

Nearly 45% of AI-generated code contains known security vulnerabilities. Securing AI-generated code starts with developer capability to identify and fix risks before code reaches production.

製品概要

Build developer capability for secure AI development

Secure Code Warrior Learning provides AI security training that builds the skills behind every commit. Developers learn to secure AI-generated code through hands-on practice across real-world AI workflows, reducing risk at the source.

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中核能力

Comprehensive AI security training for modern development

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AI security challenges for developers

AI security challenges for developers

Simulated AI-assisted development workflows

Developers learn to secure AI-generated code through interactive challenges that simulate real-world AI workflows. Learn to detect insecure patterns, validate outputs, and prevent vulnerabilities in a safe, controlled environment.

AI and LLM vulnerability training

AI and LLM vulnerability training

Learn to identify real AI risk patterns

Learning covers emerging AI vulnerabilities including prompt injection, excessive agency, system prompt leakage, sensitive data exposure, and vector and embedding weaknesses.

Modern AI frameworks and environments

Modern AI frameworks and environments

Secure real-world AI stacks

Developers train across production AI technologies including Python (LangChain, MCP), Terraform (AWS Bedrock), and modern backend frameworks powering AI applications.

LLM missions and coding labs

LLM missions and coding labs

Apply AI security skills in real scenarios

Developers build capability through immersive Missions and hands-on Coding Labs that simulate real-world AI security scenarios and vulnerability exploitation patterns.

AI security concepts and design patterns

AI security concepts and design patterns

Build foundational AI security knowledge

Developers learn how to securely use AI through topics like AI risk and security, threat modeling with AI, OWASP Top 10 for LLMs, and AI agent protocols (MCP, A2A, ACP).

AIソフトウェアガバナンス

AI駆動開発のための制御面

AI駆動の開発を可視化し、安全かつ強靭に——本番環境導入前の脆弱性を防止し、チームが自信を持って迅速に動けるようにする。

クエスト
Guided, role-based learning paths.
コーディングラボ
Remediation in live environments.
AIへの挑戦
Secure code in AI workflows.
Missions
Immersive AI security simulations.

クエスト

Discover Quests
Quests combine AI Challenges, labs, and missions into guided programs aligned to real-world AI risks and concepts
AI/LLM SECURITY
AI Agents and their Protocols (MCP, A2A and ACP)
Coding With AI
Introduction to AI Risk & Security
LLM Security Design Patterns
OWASP Top 10 for LLM Applications
AIを用いた脅威モデリング
Vibe Coding: Risk Management Framework
CYBERMON 2025 BEAT THE BOSS
Bypassaur: Direct Prompt Injection
Keykraken: Indirect Prompt Injection
Promptgeist: Vector and Embedding Weaknesses
Proxysurfa: Excessive Agency

コーディング・ラボ

Discover Coding Labs
Practice real-world AI and application security scenarios in live coding environments. Fix vulnerabilities as they would appear in actual development work — not just theory.
直接プロンプト注入
直接プロンプト注入
直接プロンプト注入

AIへの挑戦

Discover AI Challenges
Over 800 challenges that simulate real AI-assisted development workflows. Build the ability to detect insecure patterns, validate AI outputs, and prevent vulnerabilities before they reach production.
800+ AI security challenges

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Missions

Discover missions
Apply skills across complex, multi-step scenarios that simulate authentic AI risks. Missions build the muscle memory to recognise and respond to real threats in context.
AI/LLM SECURITY
直接プロンプト注入
過剰な代理性
不適切な出力処理
間接プロンプト注入
LLM Awareness
機密情報の開示
Vector & Embedding Weaknesses
成果と影響

Reduce AI-driven risk at the source of code creation through developer training

Secure Code Warrior delivers AI security training that builds developer capability to identify and prevent vulnerabilities in both human-written and AI-generated code. Through hands-on learning and real-world AI security scenarios, organizations reduce recurring vulnerabilities, strengthen secure coding behavior, and demonstrate measurable improvement across modern development workflows.

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*進行中
導入された脆弱性の削減
53%+
より速いとは、
の修復時間を意味する
3x+
AI/LLM learning
activities
1k+
Comprehensive secure coding languages covered
75+
その仕組み

What developers learn in AI security training

Coverage spans LLM vulnerabilities, agent protocols, infrastructure security, and foundational AI security design — mapped to real developer workflows.

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LLM Vulnerability Coverage

Practice real-world AI and LLM security risks.

AI security training teaches developers how to identify, prevent, and remediate vulnerabilities in AI-generated code and modern AI systems, including:

直接プロンプト注入
過剰な代理性
不適切な出力処理
間接プロンプト注入
機密情報の開示
Supply ChainMCP, Agents, and AI Infrastructure Security
システムプロンプト漏洩
ベクトルと埋め込みの弱点
AI Security Concepts and Design

Build foundational AI security knowledge

Developers learn how to securely design and review AI systems through:

AI Agents and their Protocols (MCP, A2A and ACP)
Coding With AI
Introduction to AI Risk & Security
LLM Security Design Patterns
OWASP Top 10 for LLM Applications
AIを用いた脅威モデリング
Vibe Coding: Risk Management Framework
MCP, Agents & AI Infrastructure

Secure AI agents, protocols, and cloud AI environments

Understand and mitigate risks across agent-based systems and AI infrastructure, including MCP and cloud AI services:

Bedrock (Cloud AI Infrastructure)

Secure AI services and model integrations

直接プロンプト注入
過剰な代理性
不十分なロギングとモニタリング
機密情報の開示
MCP (Model Context Protocol)

Model Context Protocol — Secure AI agents and protocol interactions

Access Control: Missing Function Level Access Control
Authentication: Improper Authentication
Authentication: Insufficiently Protected Credentials
直接プロンプト注入
間接プロンプト注入
Information Exposure: Sensitive Data Exposure
不十分なロギングとモニタリング
Insufficient Transport Layer Protection: Unprotected Transport of Sensitive Information
Server-Side Request Forgery: Server-Side Request Forgery
Vulnerable Components: Using Known Vulnerable Components
対象者

Security, engineering, and learning leaders responsible for secure development

Support secure AI development with role-specific capabilities tailored to your organization’s needs.

セキュリティおよびAIガバナンスのリーダーの皆様へ

測定可能な開発者能力を実証し、人的開発とAI支援開発の両方におけるソフトウェアリスクを低減する。

For learning & development leaders

構造化され、測定可能なセキュアコーディングプログラムを提供し、採用を促進し、効果を実証し、企業のコンプライアンス要件に適合させる。

エンジニアリングリーダー向け

開発者が、速度を維持し手戻りを減らしながら、回復力のある安全なコードを記述できるようにする。

アプリケーションセキュリティのリーダー向け

開発者主導のセキュリティを拡張し、レビュー要員を増やすことなく導入される脆弱性を削減する。

Secure AI-generated code starts with trained developers

セキュアコーディングスキルを強化し、導入された脆弱性を削減し、組織全体で測定可能な開発者信頼を構築します。

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信頼スコア
AI security training for developers FAQs

Secure AI-assisted development starts with developer capability

Learn how Secure Code Warrior helps teams adopt AI safely, reduce risk, and build measurable developer capability.

How do developers learn to secure AI-generated code?

Developers learn to secure AI-generated code through hands-on AI security training in simulated AI workflows.

Secure Code Warrior provides Quests, AI Challenges, Coding Labs, and Missions that teach developers how to identify insecure patterns, validate outputs, and prevent vulnerabilities before code reaches production.

What security risks does AI-generated code introduce?

AI-generated code can introduce vulnerabilities such as prompt injection, excessive agency, sensitive data exposure, and insecure output handling.

These risks often appear in otherwise functional code, making them difficult to detect without developer awareness and training.

How is AI security training different from traditional secure coding training?

Secure Code Warrior delivers interactive, AI security training that focuses on how developers interact with AI systems, not just how they write code.

It teaches developers how to validate AI outputs, recognize insecure patterns introduced by LLMs, and apply secure coding practices across AI-assisted workflows.

Traditional training focuses on known vulnerabilities, while AI security training prepares developers for emerging, dynamic risks.

How does Secure Code Warrior support AI security training?

Secure Code Warrior builds developer capability through hands-on learning across AI Challenges, Missions, Coding Labs, and Quests.

Developers practice securing AI-generated code in real-world scenarios, helping reduce vulnerabilities at the source and support AI Software Governance.

What AI technologies and frameworks are covered?

Secure Code Warrior provides learning across modern AI technologies and frameworks, including:

  • AI agents and protocols (MCP, A2A, ACP)
  • Python LangChain 
  • Python MCP
  • Terraform AWS (Bedrock)
  • Typescript LangChain
  • LLM security concepts and design patterns

This ensures developers are prepared to secure real-world AI systems and workflows.

How can organizations govern AI-assisted development and reduce risk?

Organizations govern AI-assisted development by gaining visibility into how AI is used, applying governance policies within development workflows, and strengthening developer capability.

Secure Code Warrior supports this through Trust Agent AI, which provides visibility into AI usage across development workflows, correlates risk at the commit level, and enforces security policies. Combined with hands-on learning, this helps organizations reduce risk before vulnerabilities reach production.

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