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Il y a 13 modules dans ce cours
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.
You will analyze breach scenarios, design role-based access controls, create infrastructure-as-code policies, and establish secure coding guidelines that prevent vulnerabilities at scale. Build practical skills in incident response, automated policy enforcement, threat modeling, and compliance evaluation.
By the end of this course, you will be able to secure AI systems and data confidently, enforce enterprise-grade policies, anticipate and mitigate threats, and demonstrate readiness for senior security roles in AI-driven organizations.
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.
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.
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.
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.
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.
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.
You will apply systematic security assessment by analyzing threat modeling outputs and penetration testing findings to make informed security decisions for AI systems.
You will develop comprehensive secure coding frameworks that bridge security requirements with developer workflow realities, providing actionable guidance that scales across development teams.
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.
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.
You will develop the critical skill of embedding security requirements directly into infrastructure deployment processes, ensuring consistent policy enforcement at scale.
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.
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.
Inclus
5 lectures1 devoir
Afficher les informations sur le contenu du module
5 lectures•Total 160 minutes
Module Overview•10 minutes
Professional Context•10 minutes
Practical Applications: AI Security and Governance•10 minutes
Assignment: AI Security Governance Integration•120 minutes
Solution Key•10 minutes
1 devoir•Total 30 minutes
Graded Quiz: Securing AI Data and Applications•30 minutes
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Is Securing AI Data and Applications good for beginners with no security background?
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.
What tools will I be able to use after completing Securing AI Data and Applications?
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.
When will I have access to the lectures and assignments?
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What will I get if I subscribe to this Certificate?
When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.