KI能编写代码，但技巧才能确保其安全。

我们为企业打造的安全编码平台，赋予您保障人类与人工智能生成代码安全的能力，同时不会拖慢部署速度。

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来自#1安全编码培训公司
资格缺口

KI加速了代码运行。安全编程能力必须与之同步发展。

KI编码助手能在几秒钟内生成可投入生产的代码。然而，速度并不等同于安全性。

开发人员现在被期望：
检查AI生成的代码是否存在隐藏的安全漏洞
识别由大型语言模型引入的不安全模式
请采用所有语言的安全编码标准
应对新的风险，例如快速注射

Traditionelle Sicherheitsschulungen konzentrieren sich auf — keine Fähigkeit. Statisches Scannen erkennt Probleme, nachdem sie aufgetreten sind. Um das Softwarerisiko zu reduzieren, muss das sichere Codierungsverhalten verbessert werden Sichere Codierungsfunktionen sind die Grundlage für eine effektive KI-Software-Governance.

产品概述

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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核心能力

大规模构建安全的编码能力

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安全编程实践实验室

安全编程实践实验室

实践而非被动内容

开发者通过互动练习修复真实安全漏洞，支持75种以上语言和框架。

KI专用安全模块

KI专用安全模块

安全的人工智能辅助开发

验证并保护人工智能生成的代码，识别不安全的模式，并在人工智能支持的工作流中应用安全标准。

自适应学习路径

自适应学习路径

基于风险的能力发展

根据开发人员的操作行为、风险信号或基准差距，自动分配针对性培训课程。

衡量进步

衡量进步

建立基准并查看改进情况

通过SCW Trust Score®评估开发者的能力，将其与同行进行比较，并追踪安全编码方面的可量化进展。

实现合规

实现合规

证明安全改进

根据OWASP十大安全风险、NIST、PCI DSS、CRA和NIS2标准定制培训课程，并提供可审计报告。

KI软件生成

人工智能辅助开发的控制层

让人工智能驱动的开发过程变得可视化、安全且具有韧性，在生产前就消除安全漏洞，使团队能够快速且充满信心地采取行动。

任务
Guided, role-based learning paths.
编码实验室
Remediation in live environments.
人工智能挑战
Secure code in AI workflows.
Missions
Immersive AI security simulations.

任务

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
基于人工智能的威胁建模
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

编码实验室

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.
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人工智能挑战

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

毋庸置疑，这是个很好的机会。悬浮在空中的各种元素的三层结构。他说："我的意思是说，我可以在这里工作，但我不能在这里工作，因为我不能在这里工作，因为我不能在这里工作。在这里，我想说的是，我们要做的是，在我们的生活中，我们要做的是，在我们的生活中，我们要做的是，在我们的生活中，我们要做的是。在这里，我想说的是，我们的生命力是有限的。

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
直接提示注入
过度代理
不正确的输出处理
间接提示注入
LLM Awareness
敏感信息披露
Vector & Embedding Weaknesses
结果与影响

从源头减少安全漏洞

Secure Code Warrior 减少重复性安全漏洞，强化安全编程习惯，并为开发人员带来可量化的改进。这些成果证明了在现代开发环境中，大规模企业安全编程培训具有可衡量的实际效果。

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*即将到来
减少引入的安全漏洞
53% +
Schneller heißt
Zeit für die Sanierung
3x+
Praktisch Lernaktivitäten
1k+
编程语言与框架
75+
它是如何工作的

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:

直接提示注入
过度代理
不正确的输出处理
间接提示注入
敏感信息披露
Supply ChainMCP, Agents, and AI Infrastructure Security
系统提示泄露
向量与嵌入的弱点
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
基于人工智能的威胁建模
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

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过度代理
日志记录和监控不足
敏感信息披露
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
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Information Exposure: Sensitive Data Exposure
日志记录和监控不足
Insufficient Transport Layer Protection: Unprotected Transport of Sensitive Information
Server-Side Request Forgery: Server-Side Request Forgery
Vulnerable Components: Using Known Vulnerable Components
适合谁

专为人工智能治理团队设计

展示可衡量的开发者能力，降低人工与人工智能驱动的开发过程中的软件风险。

面向安全与人工智能治理领域的领导者

展示可衡量的开发者能力，降低人工与人工智能驱动的开发过程中的软件风险。

面向学习与发展领域的管理人员

提供结构化、可衡量且安全的编码计划，以促进接受度、证明其效果并满足企业的合规要求。

面向工程领域的管理人员

让开发人员能够编写可靠、安全的代码，同时保持速度并减少返工。

面向应用安全高管

扩展开发人员驱动的安全性，减少引入的安全漏洞，同时无需增加审核人员数量。

安全的代码始于安全的开发者

提升您在安全编程领域的技能，减少引入的安全漏洞，并在整个企业范围内建立可衡量的开发人员信任度。

预约演示
信任评分
关于安全编码与开发人员培训的常见问题

通过实践学习安全编程来减少安全漏洞

了解Secure Code Warrior 提升Secure Code Warrior 、减少安全漏洞并提供可衡量的风险降低效果。

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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