人工智能可以编写代码，但技能可以保护代码。

我们的企业安全编码平台培养了在不减缓交付速度的情况下保护人工和人工智能生成的代码所需的技能。

预约演示
来自 #1 安全编程培训公司
技能差距

人工智能可以加速代码开发。安全编码技能必须与时俱进。

AI编程助手能在几秒钟内生成生产就绪代码。但速度并不等于安全。

现在预计开发人员将：
验证 AI 生成的代码是否存在隐藏漏洞
识别大型语言模型引入的不安全模式
跨语言应用安全编码标准
应对即时注射等新风险

传统的安全培训侧重于 — 不是能力。静态扫描会在问题出现后检测到问题。 降低软件风险需要改善安全编码行为 安全编码能力是有效的人工智能软件治理的基础。

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

大规模建立安全编码能力

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动手安全编码实验室

动手安全编码实验室

练习，而不是被动内容

开发人员通过涵盖75多种语言和框架的交互式练习来解决实际漏洞。

特定于 AI 的安全模块

特定于 AI 的安全模块

安全的人工智能辅助开发

验证和保护人工智能生成的代码，检测不安全模式，并在人工智能辅助工作流程中应用安全标准。

自适应学习路径

自适应学习路径

基于风险的技能发展

根据开发者行为、提交的风险信号或基准测试差距，自动分配有针对性的培训。

衡量进度

衡量进度

创建基准并查看改进

使用 SCW Trust Score® 评估开发人员熟练程度，与同行进行基准测试，并跟踪可衡量的安全编码进度。

实现合规性

实现合规性

证明安全性得到改善

将培训与OWASP十大漏洞、NIST、PCI DSS、CRA和NIS2相结合，并提供可审计的报告。

人工智能软件治理

人工智能驱动开发的控制平面

让人工智能驱动的开发变得可见、安全且具有弹性，在投入生产前消除漏洞，使团队能够充满信心地快速行动。

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

从源头上减少漏洞

Secure Code Warrior 减少了反复出现的漏洞，增强了安全编码习惯，并展示了可衡量的开发成果。这些结果表明，企业安全编码培训在现代开发环境中大规模地产生了可衡量的影响。

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即将推出
减少引入的漏洞
53%+
更快的平均值
是时候修复了
3x+
动手操作 学习活动
1k+
编码语言和框架
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:

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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
这是给谁的

专为 AI 治理团队打造

展示可衡量的开发人员能力，降低人工和人工智能辅助开发中的软件风险。

适用于安全和人工智能治理领导者

展示可衡量的开发人员能力，降低人工和人工智能辅助开发中的软件风险。

专为学习与发展领导者而设计

提供结构化、可衡量的安全编码计划，以推动采用率、证明影响力并符合企业合规性要求。

专为工程领导者而设

使开发人员能够编写具有弹性的安全代码，同时保持速度并减少返工。

适用于应用安全领导者

在不增加审查人员的情况下，扩展开发人员驱动的安全性并减少引入的漏洞。

安全代码始于安全的开发人员

提升安全编码能力，减少引入的安全漏洞，并在整个组织中建立可衡量的开发人员信任。

预约演示
信任评分
安全编码与开发人员培训常见问题解答

通过实践安全编码学习减少漏洞

了解Secure Code Warrior 、减少漏洞并实现可衡量的风险降低。

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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