book

Machine Learning Security with Azure

Name: Machine Learning Security with Azure
Author: Georgia Kalyva
ISBN: 9781805120483

by Georgia Kalyva

December 2023

Intermediate to advanced

310 pages

7h 34m

English

Packt Publishing

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Machine Learning Security with Azure
ForewordContributorsAbout the authorsAbout the reviewer
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesConventions usedGet in touchShare Your ThoughtsDownload a free PDF copy of this book
Part 1: Planning for Azure Machine Learning Security
Chapter 1: Assessing the Vulnerability of Your Algorithms, Models, and AI Environments
Technical requirementsAzure subscription and resourcesReviewing the Azure Machine Learning life cycleML life cycleAzure Machine LearningIntroducing an ML projectDatasetTraining the modelDeploying the modelMaking predictions using the deployed modelExploring the Zero Trust modelIntroducing the Zero Trust principlesExplaining Zero Trust defense areasAssessing the vulnerability of ML assets and appsIdentity managementData and data sourcesInfrastructureNetwork and endpointsMonitoring and maintenanceAI/ML applicationsSummaryFurther reading
Chapter 2: Understanding the Most Common Machine Learning Attacks
Introducing the MITRE ATLAS MatrixReconnaissanceResource developmentInitial accessML model accessExecutionPersistenceDefense evasionDiscoveryCollectionML attack stagingExfiltrationImpactUnderstanding ML and AI attacksReconnaissance techniquesResource development techniquesInitial access techniquesML model access techniquesExecution techniquesPersistence techniquesDefense evasion techniquesDiscovery techniquesCollection techniquesML attack staging techniquesExfiltration techniquesImpact techniquesExploring Azure services involved in ML attacksAccessDataNetworkApplicationsComputeAzure Machine LearningSummaryFurther reading
Chapter 3: Planning for Regulatory Compliance
Exploring Responsible AI developmentResponsible AI principlesGetting started with Responsible AI in your organizationRegulatory compliance in Azure Policy for Azure Machine LearningAzure Security BenchmarkFederal Risk and Authorization Management ProgramNew Zealand Information Security Manual (restricted)NIST SP 800-53 Rev. 5Reserve Bank of India IT Framework for Banks v2016Compliance auditing and reportingAzure portalAzure Resource Graph ExplorerCompliance automation in AzureAzure BlueprintsIaCSummary
Part 2: Securing Your Data
Chapter 4: Data Protection and Governance
Working with data governance in AzureIdentifying challengesExploring benefitsGetting started using cloud data best practicesExploring Azure tools and resourcesStoring and retrieving data in Azure Machine LearningConnecting datastoresAdding data assetsEncrypting and securing dataEncryption at restEncryption in transitExploring backup and recoveryReviewing backup options for your datastoresRecovering your workspaceSummary
Chapter 5: Data Privacy and Responsible AI Best Practices
Technical requirementsWorking with PythonRunning a notebook in Azure Machine LearningInstalling the SmartNoise SDKInstalling FairlearnDiscovering and protecting sensitive dataIdentifying sensitive dataExploring data anonymizationIntroducing differential privacyMitigating fairnessFairlearnWorking with model interpretabilityExploring the Responsible AI dashboardExploring FL and secure multi-party computationFL with Azure Machine LearningSummaryFurther reading
Part 3: Securing and Monitoring Your AI Environment

Chapter 6: Managing and Securing Access
Working with the PoLPAuthenticating with Microsoft Entra IDImplementing RBACWorking with built-in rolesCreating a custom role for Azure Machine LearningAuthenticating with application identitiesCreating a service principalWorking with managed identitiesEnhancing access securityConditional AccessPIMAzure Key VaultSummary
Chapter 7: Managing and Securing Your Azure Machine Learning Workspace
Technical requirementsExploring network securityCreating a VNetSecuring the workspaceSecuring associated resourcesValidating connectivityWorking with Azure Machine Learning computeSecuring compute instancesSecuring compute clustersManaging container registries and containersSecuring images with Azure Container RegistryWorking with ML endpointsSummary
Chapter 8: Managing and Securing the MLOps Life Cycle
Technical requirementsWorking with MLOps in Azure Machine LearningLeveraging IaCCombining IaC with Azure Machine LearningImplementing CI/CDWorking with Azure DevOpsExploring event-driven workflows in AzureExploring Event GridWorking with events in Azure Machine LearningDiscovering event handlers in AzureSummary
Chapter 9: Logging, Monitoring, and Threat Detection
Technical requirementsEnabling logging and configuring data retention for Azure servicesWorking with Azure MonitorEnabling diagnostic settingsWorking with alertsWorking with Application InsightsVisualizing the dataSecuring resources with Microsoft DefenderImproving our security postureExploring threat management with SentinelSummary
Part 4: Best Practices for Enterprise Security in Azure Machine Learning
Chapter 10: Setting a Security Baseline for Your Azure Machine Learning Workloads
Setting a baseline for Azure Machine LearningDiscovering services for added securityExploring an example solution architectureThreat modeling for Azure Machine LearningExploring the STRIDE methodologyGetting started with the Microsoft Threat Modeling ToolReviewing the shared responsibility model for cloud securityExploring the cloud provider responsibilitiesReviewing customers’ responsibilitiesSummary
Index
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Content preview from Machine Learning Security with Azure

Preface

Machine learning (ML) and artificial intelligence (AI) have continued to evolve rapidly in the past two years, with significant advancements and applications in various fields. AI and ML systems often process vast amounts of sensitive data, including personal information. Ensuring the security of this data is crucial to protect against breaches that could lead to identity theft, financial fraud, and other privacy violations. On top of this, governments and regulatory bodies are implementing stricter data protection and privacy laws. Compliance with these regulations is essential for legal and ethical operations. This is why securing those systems has become more vital than ever. As cyber threats evolve, AI and ML systems must be designed ...

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Publisher Resources

ISBN: 9781805120483

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

Security

Design

Business

Soft Skills

Machine Learning Security with Azure

by Georgia Kalyva

Preface

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.