Analyze, Create, and Secure Data with Zero Trust

本课程是 AI Systems Reliability & Security 专项课程的一部分

位教师：Hurix Digital

访问权限由 New York State Department of Labor 提供

3个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 小时完成

灵活的计划

自行安排学习进度

3个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Effective incident response identifies root causes like policy gaps, configuration errors, and design flaws, not just symptoms.
Zero-trust architecture shifts security from perimeter-based models to continuous verification for every access request.
Security controls must be systematically evaluated against frameworks to spot gaps causing compliance and operational risks.
Sustainable data security integrates forensics, proactive architecture, and continuous monitoring into one operations framework.

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要了解的详细信息

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作业

6 任务¹

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授课语言：英语（English）

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本课程是 AI Systems Reliability & Security 专项课程专项课程的一部分

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该课程共有3个模块

Ever wondered why data breaches keep happening despite massive security investments? The answer lies in moving beyond perimeter defense to a comprehensive zero-trust approach that assumes breach and verifies everything.

This Short Course was created to help Machine Learning and AI professionals accomplish enterprise-grade data security that protects against both external threats and insider risks. By completing this course, you'll master the critical investigative skills to identify why breaches occur, architect security systems that never trust by default, and systematically evaluate your defenses against the gold standards that regulators and customers demand. By the end of this course, you will be able to: • Analyze incident reports to determine root causes of data breaches • Create a zero-trust data security architecture • Evaluate security controls and practices against industry standards and compliance requirements This course is unique because it combines post-incident forensics with proactive architecture design, ensuring you can both respond to security failures and prevent them from happening again. You'll work with real breach scenarios, design authentication frameworks that eliminate implicit trust, and audit systems against SOC 2, NIST, and CIS benchmarks. To be successful in this project, you should have a background in enterprise security concepts, data governance principles, and basic understanding of compliance frameworks.

Learners master investigative techniques using MITRE ATT&CK framework to reconstruct attack timelines, correlate evidence across multiple systems, and distinguish between immediate attack techniques and underlying architectural vulnerabilities requiring systemic remediation.

涵盖的内容

3个视频1篇阅读材料2个作业

3个视频总计16分钟

When AI Systems Become Attack Vectors: The Hidden Cost of Poor Investigation 4分钟
The MITRE ATT&CK Framework for AI System Investigation 7分钟
Reconstructing Attack Timelines Using Log Analysis Tools 5分钟

1篇阅读材料总计10分钟

Timeline Reconstruction Methodology for Complex AI System Breaches 10分钟

2个作业总计21分钟

Capital One Breach Timeline Analysis and Root Cause Investigation 18分钟
Root Cause Analysis Techniques Validation 3分钟

Learners develop practical zero trust frameworks by implementing identity and access management controls, establishing data loss prevention policies with real-time monitoring, and creating network segmentation strategies that eliminate implicit trust assumptions.

涵盖的内容

2个视频2篇阅读材料1个作业

2个视频总计13分钟

Zero Trust Core Principles and Architecture Components 7分钟
Implementing Least Privilege IAM Policies for AI Workloads 6分钟

2篇阅读材料总计20分钟

The Business Case for Zero Trust in AI Environments 10分钟
Identity and Access Management Architecture for AI Systems 10分钟

1个作业总计8分钟

Zero Trust Architecture Components Knowledge Check 8分钟

Learners conduct comprehensive gap analysis comparing current implementations against SOC 2, NIST, and CIS requirements, prioritize remediation activities based on risk impact and compliance criticality, and create executive-ready assessment reports.