AI가 코드를 작성할 수는 있지만, 기술이 그것을 보장한다.

당사의 기업용 보안 코딩 플랫폼은 개발 속도를 저하시키지 않으면서 인간과 AI가 생성한 코드 모두를 보호하는 데 필요한 역량을 구축합니다.

데모 예약하기
최고의 보안 코딩 교육 기업에서
기술 격차

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.

개발자들은 이제 다음과 같은 사항을 준수해야 합니다:
Identify vulnerabilities in AI-generated code
LLM이 도입한 불안정한 패턴을 인식하라
모든 언어에 걸쳐 안전한 코딩 표준을 적용하십시오
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.

제품 개요

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.

데모 예약
핵심 역량

Comprehensive AI security training for modern development

데모 예약
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).

인공지능 소프트웨어 거버넌스

AI 기반 개발을 위한 제어 평면

AI 기반 개발을 가시화하고 안전하며 탄력적으로 만들어 생산 환경 이전에 취약점을 방지함으로써 팀이 자신감을 가지고 신속하게 진행할 수 있도록 지원합니다.

퀘스트
Guided, role-based learning paths.
코딩 랩
Remediation in live environments.
AI Challenges
Secure code in AI workflows.
Missions
Immersive AI security simulations.

퀘스트

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
인공지능을 활용한 위협 모델링
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

코딩 실습

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.
직접 프롬프트 주입
직접 프롬프트 주입
직접 프롬프트 주입

AI Challenges

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
직접 프롬프트 주입
과도한 대리성
부적절한 출력 처리
간접 프롬프트 주입
LLM Awareness
민감 정보 공개
Vector & Embedding Weaknesses
성과 및 영향

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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*진행 중
도입된 취약점 감소
53%+
더 빠른 평균 복구 시간
3x+
AI/LLM learning
activities
1k+
Comprehensive secure coding languages covered
75+
작동 방식

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.

데모 예약
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:

직접 프롬프트 주입
과도한 대리성
부적절한 출력 처리
간접 프롬프트 주입
민감 정보 공개
Supply ChainMCP, Agents, and AI Infrastructure Security
시스템 프롬프트 유출
벡터와 임베딩의 취약점
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
인공지능을 활용한 위협 모델링
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

직접 프롬프트 주입
과도한 대리성
불충분한 로깅 및 모니터링
민감 정보 공개
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
직접 프롬프트 주입
간접 프롬프트 주입
Information Exposure: Sensitive Data Exposure
불충분한 로깅 및 모니터링
Insufficient Transport Layer Protection: Unprotected Transport of Sensitive Information
Server-Side Request Forgery: Server-Side Request Forgery
Vulnerable Components: Using Known Vulnerable Components
누구를 위한 것인가

Security, engineering, and learning leaders responsible for secure development

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

보안 및 AI 거버넌스 리더를 위한

인간 및 AI 지원 개발 전반에 걸쳐 측정 가능한 개발자 역량을 입증하고 소프트웨어 위험을 줄입니다.

학습 및 개발 리더를 위한

구조화되고 측정 가능한 보안 코딩 프로그램을 제공하여 도입을 촉진하고, 효과를 입증하며, 기업 규정 준수 요구사항에 부합하도록 합니다.

엔지니어링 리더들을 위해

개발자가 속도를 유지하고 재작업을 줄이면서 탄력적이고 안전한 코드를 작성할 수 있도록 지원합니다.

앱보안 리더들을 위한

개발자 주도형 보안을 확장하고 검토 인력을 늘리지 않으면서 도입된 취약점을 줄입니다.

Secure AI-generated code starts with trained developers

안전한 코딩 기술을 강화하고, 도입된 취약점을 줄이며, 조직 전반에 걸쳐 측정 가능한 개발자 신뢰를 구축하십시오.

데모 예약
신뢰 점수
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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