What does zero trust mean in this course?

In this course, zero trust means designing data security around the idea that breaches are possible and no access request is trusted by default. The focus is on continuous verification, least-privilege access, and monitoring across AI and ML systems instead of depending on a trusted internal perimeter.

When would you use a zero trust approach?

You would use it when sensitive data moves across multiple systems, services, or teams and internal access cannot be assumed safe. The course treats it as especially relevant for AI and machine learning environments, where pipelines and shared resources create many paths to valuable data.

How does zero trust fit into a broader security workflow?

It sits in the middle of a broader security process, turning what you learn from incidents into concrete access, monitoring, and segmentation decisions. In this course, it connects breach analysis with ongoing control review so security can be designed, checked, and improved as one repeatable practice.

How is zero trust different from perimeter-based security?

Perimeter-based security mainly tries to keep threats outside a trusted boundary, while zero trust requires every request to be verified no matter where it comes from. The course emphasizes removing implicit trust inside AI systems by tightening identity, access, monitoring, and network boundaries.

Do you need any prerequisites before learning zero trust architecture?

A background in enterprise security concepts, data governance principles, and a basic understanding of compliance frameworks is helpful. What matters most is being comfortable following access decisions, incident evidence, and control evaluations at an intermediate level.

What tools, platforms, or methods are used in this course?

The course mainly uses structured breach investigation and zero trust access-control design methods rather than centering on one software platform. You also use common security standards as reference points when reviewing whether controls are effective.

What specific tasks will you practice or complete in this course?

You practice analyzing incident reports, correlating evidence into attack timelines, designing least-privilege access and verification controls, and reviewing security gaps against common standards. These tasks help you move from reacting to a breach toward building and checking a repeatable zero trust security model.

Analyze, Create, and Secure Data with Zero Trust

Ce cours n'est pas disponible en Français (France)

Nous sommes actuellement en train de le traduire dans plus de langues.

Analyze, Create, and Secure Data with Zero Trust

Ce cours fait partie de Spécialisation "AI Systems Reliability & Security"

Instructeur : Hurix Digital

Inclus avec

3 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

2 heures à compléter

Planning flexible

Apprenez à votre propre rythme

3 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

2 heures à compléter

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

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.

Compétences que vous acquerrez

Catégorie : Failure Analysis
Catégorie : IT Security Architecture
Catégorie : Investigation
Catégorie : Cyber Security Assessment
Catégorie : Root Cause Analysis
Catégorie : Security Architecture Review

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janvier 2026

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Ce cours fait partie de la Spécialisation "AI Systems Reliability & Security"

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Il y a 3 modules dans ce cours

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.

Détails du module

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.

Inclus

3 vidéos1 lecture2 devoirs

3 vidéosTotal 16 minutes

When AI Systems Become Attack Vectors: The Hidden Cost of Poor Investigation4 minutes
The MITRE ATT&CK Framework for AI System Investigation7 minutes
Reconstructing Attack Timelines Using Log Analysis Tools5 minutes

1 lectureTotal 10 minutes

Timeline Reconstruction Methodology for Complex AI System Breaches10 minutes

2 devoirsTotal 21 minutes

Capital One Breach Timeline Analysis and Root Cause Investigation18 minutes
Root Cause Analysis Techniques Validation3 minutes

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.

Inclus

2 vidéos2 lectures1 devoir

2 vidéosTotal 13 minutes

Zero Trust Core Principles and Architecture Components7 minutes
Implementing Least Privilege IAM Policies for AI Workloads6 minutes

2 lecturesTotal 20 minutes

The Business Case for Zero Trust in AI Environments10 minutes
Identity and Access Management Architecture for AI Systems10 minutes

1 devoirTotal 8 minutes

Zero Trust Architecture Components Knowledge Check8 minutes

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.

Inclus

3 vidéos1 lecture3 devoirs

3 vidéosTotal 18 minutes

The Compliance Imperative: Why Security Audits Make or Break AI Companies5 minutes
Systematic Gap Analysis Methodology for Security Control Assessment7 minutes
Conducting Security Control Gap Analysis Using Assessment Frameworks6 minutes

1 lectureTotal 10 minutes

Framework Comparison: SOC 2, NIST, and CIS Security Standards for AI Systems10 minutes

3 devoirsTotal 34 minutes

Comprehensive Security Controls Audit for AI System Compliance16 minutes
Security Framework Assessment and Compliance Validation3 minutes
Comprehensive Security Architecture Evaluation and Compliance Strategy15 minutes

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