Secure AI systems and data using enterprise-grade governance, zero-trust architecture, and compliance frameworks. This course teaches you to govern GenAI data safely, implement zero-trust security models, secure applications against evolving threats, and evaluate cloud systems against standards like NIST and SOC 2.

Securing AI Data and Applications
包含在 中
您将学到什么
Analyze data access patterns and security incidents to design role-based controls that balance AI innovation with governance requirements
Create zero-trust architectures and infrastructure-as-code policies that prevent breaches through continuous verification and automated enforcement
Evaluate application security postures using threat modeling, penetration testing, and dependency analysis to prioritize remediation efforts
Assess cloud security controls against industry frameworks like NIST, SOC 2, and compliance requirements for regulatory readiness
要了解的详细信息
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- 通过 Coursera 获得可共享的职业证书

该课程共有13个模块
You will establish critical skills for securing GenAI data through precise access controls. Learners explore why traditional permission models fail in AI environments, develop expertise in access pattern analysis and function-based RBAC design, and gain hands-on experience using SQL techniques to analyze real access logs.
涵盖的内容
3个视频1篇阅读材料2个作业
You will transform from framework consumers to assessment practitioners who can lead organizational governance improvement initiatives. Learners gain expertise in DAMA-DMBOK components and advanced assessment techniques, then practice facilitating maturity workshops through screencast demonstrations.
涵盖的内容
2个视频2篇阅读材料1个作业
You will integrate course concepts into practical stewardship program design capabilities. Learners learn the five essential components of effective programs—ownership assignment, quality frameworks, and governance procedures—then develop complete documentation and design skills to transform organizational data governance from ad-hoc practices into systematic capabilities that enable secure, compliant GenAI operations.
涵盖的内容
2个视频1篇阅读材料3个作业
You will apply 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
You will 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
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.
涵盖的内容
3个视频1篇阅读材料3个作业
You will apply systematic security assessment by analyzing threat modeling outputs and penetration testing findings to make informed security decisions for AI systems.
涵盖的内容
3个视频1篇阅读材料2个作业
You will develop comprehensive secure coding frameworks that bridge security requirements with developer workflow realities, providing actionable guidance that scales across development teams.
涵盖的内容
4个视频1篇阅读材料2个作业
You will build proficiency in contextual risk analysis of dependency vulnerabilities, transforming overwhelming vulnerability scan data into actionable remediation plans that protect the organization's most critical assets.
涵盖的内容
3个视频1篇阅读材料3个作业
You will gain the critical skill of detecting security threats through systematic IAM audit log analysis, enabling them to protect cloud infrastructure from privilege escalation attacks.
涵盖的内容
3个视频1篇阅读材料1个作业
You will develop the critical skill of embedding security requirements directly into infrastructure deployment processes, ensuring consistent policy enforcement at scale.
涵盖的内容
3个视频2篇阅读材料2个作业
You will develop comprehensive skills in security controls evaluation by systematically assessing organizational security practices against industry standards like SOC 2 and NIST, identifying compliance gaps, and ensuring regulatory adherence for AI/ML environments.
涵盖的内容
2个视频1篇阅读材料3个作业
You will build a comprehensive security governance framework for AI systems by integrating data protection, access control, and compliance evaluation practices. You'll learn how fundamental security components work together to create robust defense systems for AI operations, including how data governance affects access control decisions, how security assessments inform compliance strategies, and how application security prevents system vulnerabilities in real organizational environments.
涵盖的内容
5篇阅读材料1个作业
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常见问题
This course requires intermediate-level experience with enterprise security concepts, data governance, and cloud infrastructure. While comprehensive, it's designed for ML/AI professionals who already have foundational security knowledge and want to specialize in AI-specific security challenges.
You'll gain hands-on experience with Infrastructure-as-Code tools, IAM systems like AWS IAM, security frameworks including NIST 800-53 and SOC 2, and governance tools for implementing DAMA-DMBOK standards. You'll also work with threat modeling tools, penetration testing analysis, dependency scanners, and vulnerability management systems.
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
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¹ 本课程的部分作业采用 AI 评分。对于这些作业,将根据 Coursera 隐私声明使用您的数据。








