本课程是一门聚焦于 AI 智能体（AI Agents）纵深防御的实战加固课程，专为 AI 工程师和安全专家设计，旨在解决开源框架 OpenClaw 在开发与生产环境中的核心安全痛点。课程围绕 OpenClaw 的威胁模型展开，深入传授如何通过最小权限原则、强身份验证和严格的密钥管理来构建安全基线。学员将系统性掌握防御提示词注入（Prompt Injection）和工具滥用的核心技术，并学会利用沙箱隔离技术（Sandboxing）安全运行智能体生成代码，同时配合实时监控、可观测性审计日志与应急响应机制，全面降低自主性 AI 带来的系统越权和数据泄漏风险。
Published 6/2026
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 6h 27m | Size: 2.88 GB
Secure OpenClaw AI Agents with Authentication, Secret Management, Prompt Injection Defense, Sandboxing & Monitoring
What you’ll learn
Understand OpenClaw’s security architecture, threat model, and common attack vectors.
Configure OpenClaw securely using least-privilege access, authentication, and secret management.
Prevent prompt injection, tool abuse, and unauthorized actions through secure agent design.
Implement monitoring, logging, sandboxing, and incident response to protect OpenClaw deployments.
Requirements
Basic knowledge of Linux, networking, and command-line operations is helpful but not required.
Description Disclaimer : This course contains the use of artificial intelligence AI agents are becoming increasingly powerful, but with greater autonomy comes greater security responsibility. If you plan to deploy OpenClaw in development, testing, or production environments, understanding how to secure it is essential.
This course is designed to teach you the security best practices required to protect OpenClaw from common threats while building reliable and secure AI agent workflows. Whether you are a software developer, DevOps engineer, security professional, or AI enthusiast, you will gain practical knowledge that you can immediately apply to your own deployments.
Throughout the course, you’ll learn how to configure OpenClaw securely, implement the principle of least privilege, protect API keys and secrets, defend against prompt injection attacks, secure connected tools, and reduce the risks associated with autonomous AI agents.
The course also covers sandboxing techniques, authentication and authorization, secure environment configuration, logging, monitoring, auditing, vulnerability management, and incident response. Every topic is explained with practical examples and real-world recommendations that follow modern cybersecurity principles.
By the end of this course, you will understand how to identify security risks before they become problems and implement layered defenses to protect your OpenClaw environment.
If you want to deploy OpenClaw with confidence and follow proven security practices, this course will provide the practical knowledge and techniques you need to build secure, resilient AI agent systems.
Who this course is for
AI engineers, DevOps professionals, cybersecurity practitioners, software developers, and anyone deploying or managing OpenClaw who wants to implement security best practices and reduce operational risk.
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