What I learn from Google Cloud Study Jam: GenAI with Cloud Core

Google Cloud Solution Architect:

A professional responsible for designing and implementing solutions on the Google Cloud platform.
Works closely with customers to understand their business requirements and technical needs.
Designs architectures and recommends Google Cloud products and services to meet customer goals.
Develops proof of concepts, prototypes, and technical proposals.
Collaborates with cross-functional teams to ensure successful implementation and deployment of solutions.
Provides technical guidance, support, and troubleshooting throughout the project lifecycle.
Stays updated on Google Cloud technologies and industry trends to offer the best solutions to customers.

Computing and storing

What is the Google computer engine?

Google Compute Engine is an Infrastructure as a Service (IaaS) offering by Google Cloud.
It provides virtual machine instances in the cloud for running workloads.
Users can choose from a variety of machine types and customize the virtual machines based on their needs.
Compute Engine offers scalability, flexibility, and high-performance computing capabilities.
It integrates with other Google Cloud services, allowing seamless data and resource management.
Compute Engine supports both Linux and Windows operating systems.
Users have control over networking, storage, and security configurations.
Pricing is based on a pay-as-you-go model, with options for sustained use discounts and custom machine types.

What is Google Kubernetes engine?

Google Kubernetes Engine is a managed container orchestration platform provided by Google Cloud.
It enables the deployment, scaling, and management of containerized applications using Kubernetes.
GKE automates much of the infrastructure management, allowing developers to focus on application development.
It offers features like automatic scaling, load balancing, and self-healing capabilities for containers.
GKE integrates with other Google Cloud services, providing seamless access to storage, networking, and monitoring tools.
It supports both stateless and stateful applications, providing persistent storage options.
GKE provides a robust security framework, including identity and access management, encryption, and network policies.
Pricing is based on a pay-as-you-go model, with options for node pools and cluster autoscaling.

What is the Google app engine?

Google App Engine is a fully managed Platform as a Service (PaaS) offering by Google Cloud.
It allows developers to build and deploy web applications and APIs without managing the underlying infrastructure.
App Engine provides a NoSQL Datastore for storing and retrieving data.
It offers a Memcache service for caching frequently accessed data, improving application performance.
App Engine includes built-in load balancing to distribute traffic across multiple instances.
It supports health checks to monitor the status of instances and automatically replace unhealthy ones.
App Engine provides logging capabilities for collecting and analyzing application logs.
It offers a User Authentication API for managing user authentication and authorization.
App Engine supports preconfigured containers for running custom runtime environments.
It integrates with a Content Delivery Network (CDN) for efficient content delivery worldwide.
App Engine includes security scanning to detect and mitigate vulnerabilities in applications.
It supports versioning, allowing developers to manage different versions of their applications.

What is Google Cloud's function?

Google Cloud Functions is a serverless compute service provided by Google Cloud.
It allows developers to write and deploy small, event-driven functions in various languages.
Functions are triggered by events from various sources like Cloud Storage, Pub/Sub, HTTP requests, and more.
Cloud Functions automatically scales based on the incoming workload and charges only for the execution time.
It supports multiple programming languages, including JavaScript, Python, Go, and more.
Cloud Functions integrates seamlessly with other Google Cloud services and APIs.
It provides built-in logging and monitoring capabilities for observability.
Cloud Functions simplifies the deployment and management of individual functions, abstracting away infrastructure concerns.
It enables rapid development and deployment of microservices and event-driven applications.
Cloud Functions is suitable for use cases such as data processing, real-time analytics, and application logic execution.

What is Google Cloud run?

Google Cloud Run is a fully managed serverless container platform provided by Google Cloud.
It allows developers to run stateless HTTP containers in a serverless environment.
Cloud Run abstracts away infrastructure management, auto-scales based on incoming requests, and charges only for actual usage.
It supports containerized applications built with Docker and can be written in any programming language.
Cloud Run provides a seamless deployment experience with easy integration into existing CI/CD workflows.
It offers rapid scaling capabilities to handle high traffic and spikes in demand.
Cloud Run supports both HTTP/1.1 and HTTP/2 protocols for efficient communication.
It integrates with other Google Cloud services for seamless data storage, logging, and monitoring.
Cloud Run is suitable for microservices, APIs, web applications, and event-driven workloads.
It provides a balance between the scalability and flexibility of containers and the simplicity of serverless computing.

What is Google Cloud DataFlow?

Google Cloud Dataflow is a fully managed service for processing and analyzing large-scale data sets.
It is based on Apache Beam, an open-source unified programming model for batch and streaming data processing.
Dataflow allows you to build data pipelines for ETL (Extract, Transform, Load), batch processing, and stream processing.
It offers automatic scaling, handling the complexity of resource management and optimization.
Dataflow supports both batch and streaming data processing, offering near-real-time data analysis.
It integrates with other Google Cloud services, such as BigQuery, Pub/Sub, and Cloud Storage.
Dataflow provides a visual monitoring interface and detailed logs for pipeline monitoring and troubleshooting.
It offers flexibility in programming languages, supporting Java, Python, and other Beam SDK-supported languages.
Dataflow enables data parallelism, distributing processing across multiple workers for faster execution.
It provides a unified model for both batch and streaming processing, simplifying development and maintenance of data pipelines.

What is Google Dataproc?

Google Dataproc is a fully managed cloud service for running Apache Hadoop, Spark, and other big data frameworks.
It provides a scalable and cost-effective solution for processing and analyzing large datasets.
Dataproc automates the provisioning, management, and scaling of clusters, allowing users to focus on data analysis.
It integrates with other Google Cloud services, such as BigQuery, Cloud Storage, and Pub/Sub.
Dataproc clusters can be easily customized with specific software versions, libraries, and configurations.
It supports both batch and streaming data processing, enabling real-time data analysis.
Dataproc provides flexibility in choosing the cluster size to meet performance and cost requirements.
It offers monitoring, logging, and debugging capabilities for cluster management and troubleshooting.
Dataproc supports popular big data tools and frameworks, including Hadoop, Spark, Hive, and Pig.
It allows easy migration of on-premises big data workloads to the cloud, reducing operational overhead.

What is Google Firestore?

Google Firestore is a fully managed NoSQL document database provided by Google Cloud.
It offers flexible and scalable data storage for web, mobile, and server applications.
Firestore organizes data into collections and documents, allowing hierarchical data modeling.
It provides real-time data synchronization and automatic scaling to handle high traffic and concurrent access.
Firestore offers strong consistency, ensuring data integrity across distributed environments.
It supports querying and indexing for efficient data retrieval and searching.
Firestore integrates with other Google Cloud services, such as Cloud Functions and Cloud Storage.
It provides client libraries for various programming languages and mobile platforms.
Firestore offers granular access control and security features to protect data.
It allows seamless integration with Firebase, enabling powerful backend services for mobile and web applications.

What is Google Looker?

Google Looker is a business intelligence and data analytics platform.
It enables organizations to explore, analyze, and visualize data from various sources.
Looker provides a unified view of data through a web-based interface.
It offers a wide range of data modeling and querying capabilities.
Looker supports collaborative data exploration and sharing of insights.
It integrates with popular databases, data warehouses, and cloud platforms.
Looker provides advanced analytics features, including predictive modeling and machine learning.
It offers customizable dashboards and reports for data visualization.
Looker allows users to create and share data-driven reports and presentations.
It empowers organizations with data-driven decision-making and insights.

What are Automatic scaling and upgrades?

Automatic scaling refers to the ability of a system to dynamically adjust its resources based on demand.
It allows the system to automatically allocate or deallocate resources to meet changing workload requirements.
Automatic scaling helps optimize resource utilization, ensuring efficient and cost-effective operations.
Scaling can be based on various metrics such as CPU usage, memory utilization, or incoming request rate.
Automatic upgrades refer to the process of automatically applying software updates or patches to a system.
It eliminates the need for manual intervention and ensures that the system remains up-to-date with the latest features and security fixes.
Automatic upgrades can be scheduled during maintenance windows or performed seamlessly with zero downtime.
They help improve system reliability, performance, and security by keeping the software stack current.
Automatic scaling and upgrades are common features in cloud computing platforms and services to provide scalability, flexibility, and optimal performance.

Data

What is the difference between a stream and batch process?

Stream Processing:

Stream processing deals with continuous and real-time data.
Data is processed as it arrives, typically in small, incremental units.
It enables immediate response and analysis of data as it flows.
Stream processing is suitable for scenarios that require low latency and real-time insights.
Examples include real-time analytics, fraud detection, and IoT data processing.

Batch Processing:

Batch processing deals with large volumes of data collected over a period of time.
Data is processed in batches or chunks, typically in scheduled intervals.
It involves processing a significant amount of data in a single operation.
Batch processing is suitable for scenarios where results can be obtained retrospectively.
Examples include data warehousing, generating reports, and data analysis.

Key Differences:

Stream processing is real-time and continuous, while batch processing is retrospective and periodic.
Stream processing deals with data as it arrives, while batch processing operates on collected data.
Stream processing enables immediate insights, while batch processing provides insights after the data is processed.
Stream processing is suitable for low-latency, real-time applications, while batch processing is suitable for large-scale data analysis and reporting.

What is Google Cloud pub/sub?

Google Cloud Pub/Sub is a fully managed messaging service provided by Google Cloud.
It enables asynchronous and reliable communication between independent applications.
Pub/Sub follows the publish-subscribe model, where publishers send messages to topics, and subscribers receive messages from subscriptions.
It provides scalable and durable message storage, ensuring message delivery even during system failures.
Pub/Sub supports at-least-once delivery semantics, ensuring message reliability.
It integrates with other Google Cloud services, enabling seamless data flow and event-driven architectures.
Pub/Sub allows for the decoupling of sender and receiver systems, improving scalability and flexibility.
It supports push and pull message delivery mechanisms to accommodate different application requirements.
Pub/Sub can handle high throughput and high-volume message processing.
It provides access controls and authentication mechanisms to secure message communication.

What is a Google BigQuery? (Analysis workload, SQL)

Google BigQuery is a fully managed, serverless data warehouse and analytics platform provided by Google Cloud.
It is designed for processing and analyzing large-scale datasets in a fast and scalable manner.
BigQuery supports analysis workloads, allowing users to run complex queries on large datasets.
It offers a familiar SQL interface for querying and manipulating data.
BigQuery provides high-performance query execution, leveraging distributed computing power.
It automatically scales resources based on query demands, ensuring fast query response times.
BigQuery integrates with other Google Cloud services and third-party tools for data ingestion and export.
It supports data encryption at rest and in transit, ensuring data security.
BigQuery allows for real-time analysis using streaming data ingestion.
It provides features for data exploration, data visualization, and machine learning integration.

What is Google's cloud bigTable? (Analysis workload, NoSQL)

Google Cloud Bigtable is a highly scalable, NoSQL database service provided by Google Cloud.
It is designed to handle large-scale, high-throughput workloads with low latency.
Bigtable is a distributed, columnar database that can store and retrieve vast amounts of structured data.
It is suitable for applications requiring real-time analytics, time-series data, and high-volume data processing.
Bigtable provides linear scalability, allowing users to add or remove nodes to meet changing workload demands.
It offers automatic sharding and load balancing, distributing data and queries across multiple nodes.
Bigtable supports high-speed read and write operations, making it suitable for applications with low-latency requirements.
It integrates with other Google Cloud services, such as BigQuery and Dataflow, for data processing and analysis.
Bigtable provides data durability and fault-tolerance through replication and data backup mechanisms.
It is used by various industries for use cases like IoT data storage, time-series data analysis, and ad serving platforms.

What is Google's cloud spanner? (SQL, global scalability)

Google Cloud Spanner is a globally distributed, horizontally scalable, and strongly consistent relational database service provided by Google Cloud.
It combines the benefits of relational databases with the scalability and global reach of a NoSQL database.
Spanner supports SQL queries and provides ACID transactions across globally distributed data centers.
It offers automatic scaling and rebalancing of resources to handle changing workloads and data volumes.
Spanner provides high availability and fault tolerance, with built-in replication and data redundancy.
It offers strong consistency guarantees, ensuring that all replicas of the data are consistent at all times.
Spanner is suitable for applications requiring global scalability, high throughput, and low-latency data access.
It integrates with other Google Cloud services and supports seamless data replication and synchronization.
Spanner provides schema changes without downtime, enabling schema evolution in a live system.
It is used for various use cases, including multi-region data replication, financial applications, and global inventory management.

What is Google's Firebase? (noSQL)

Google Firebase is a comprehensive development platform provided by Google for building web and mobile applications.
It offers a range of services and tools to support app development, backend infrastructure, and user engagement.
Firebase provides a NoSQL database called Cloud Firestore for storing and syncing data in real-time.
Firestore is designed for scalability, offline data access, and real-time synchronization across devices.
Firebase offers authentication services, allowing developers to easily implement user registration, login, and access control.
It provides cloud functions for serverless computing, enabling developers to run custom code in response to events.
Firebase includes hosting services for deploying and serving web apps with HTTPS support.
It offers a variety of SDKs and libraries for different platforms, making it easy to integrate Firebase services into applications.
Firebase provides analytics, crash reporting, and performance monitoring tools to gain insights into app usage and performance.
It supports push notifications, in-app messaging, and remote configuration to enhance user engagement and app personalization.

App Engine

NoSQL database:
Cloud Datastore, Cloud Firestore, Cloud BigTable

Relational database:
Cloud SQL or Cloud AlloyDB, Cloud Spanner

File/object storage:
Cloud Storage, Cloud Filestore, Google Drive

Caching:
Cloud Memorystore (Redis or Memcached)

Task execution:
Cloud Tasks, Cloud Pub/Sub, Cloud Scheduler, Cloud Workflows

User authentication:
Cloud Identity Platform, Firebase Auth, Google Identity Services

Endpoint

What is a cloud endpoint?

Cloud Endpoint is a service provided by Google Cloud for building, deploying, and managing APIs.
It allows developers to create, secure, and monitor APIs for their applications.
Cloud Endpoint provides features like request validation, authentication, and authorization.
It supports various API protocols, including REST and gRPC.
Cloud Endpoint integrates with other Google Cloud services, such as Cloud Run and Cloud Functions.
It offers features like API versioning, traffic splitting, and analytics for API management.
Cloud Endpoint provides client libraries and tools for generating API documentation and SDKs.
It offers scalability and reliability, handling API traffic across multiple regions.
Cloud Endpoint helps developers focus on building APIs by handling infrastructure and security concerns.
It supports deployment on Google Cloud or on-premises using Google Kubernetes Engine (GKE).

What is Apigee API management?

Apigee is an API management platform provided by Google Cloud.
It enables organizations to design, deploy, and manage APIs at scale.
Apigee offers features for API lifecycle management, including design, development, testing, and deployment.
It provides tools for API security, authentication, and access control.
Apigee allows organizations to create developer portals for API documentation, onboarding, and support.
It offers analytics and monitoring capabilities to track API usage, performance, and trends.
Apigee supports traffic management and rate limiting to ensure API availability and performance.
It integrates with other Google Cloud services and third-party tools for seamless API integration.
Apigee provides tools for API versioning, caching, and transformation.
It helps organizations with API governance, compliance, and policy enforcement.

Logging

What is Infrastructure as Code (IaC)?

Infrastructure as Code refers to the practice of managing and provisioning infrastructure resources using machine-readable configuration files or scripts.
It enables the automation and reproducibility of infrastructure deployment and management.
IaC treats infrastructure as software, allowing it to be version-controlled, tested, and managed using familiar software development practices.
It reduces manual configuration and human error by codifying infrastructure provisioning and configuration steps.
IaC tools, such as Terraform and AWS CloudFormation, allow infrastructure to be defined declaratively, specifying the desired state of resources.
Infrastructure changes can be tracked, reviewed, and audited through version control systems.
IaC promotes consistency and scalability by providing a repeatable and scalable approach to infrastructure deployment and management.
It supports cloud platforms, virtualization technologies, and configuration management tools.
IaC enables the creation and management of infrastructure resources across different environments, such as development, testing, and production.
It facilitates collaboration among teams by providing a shared and collaborative infrastructure definition.

Why need Logging and Monitoring with Cloud Monitoring?

Logging and monitoring are essential for understanding the health, performance, and behavior of applications and infrastructure.
Cloud Monitoring provides real-time insights into the performance and availability of cloud resources.
Logging allows capturing and storing log data from various sources, such as applications, virtual machines, and containers.
Monitoring enables proactive detection of issues, performance bottlenecks, and anomalies in real-time.
Logging helps in troubleshooting, debugging, and auditing by providing a record of events and actions.
Monitoring provides alerts and notifications to notify about critical events or deviations from predefined thresholds.
It helps in capacity planning and resource optimization by analyzing usage patterns and trends.
Logging and monitoring enable compliance with regulatory requirements and security auditing.
Cloud Monitoring integrates with other Google Cloud services and third-party tools for comprehensive visibility.
It supports customizable dashboards, visualizations, and reporting for data analysis and decision-making.

What is Google Cloud logging?

Google Cloud Logging is a service provided by Google Cloud for collecting, storing, and analyzing log data from various sources.
It supports different types of logs, including Cloud Audit logs, agent logs, network logs, and service logs.
Cloud Audit logs capture API calls and administrative actions for auditing and compliance purposes.
Agent logs are generated by monitoring agents installed on virtual machines or containers, providing insights into system-level activities.
Network logs capture network traffic information for monitoring and troubleshooting network-related issues.
Service logs are specific to Google Cloud services and provide detailed information about service activities and events.
Cloud Logging allows for centralized log management and analysis across multiple projects and resources.
It provides filtering, searching, and correlation capabilities to extract meaningful insights from log data.
Cloud Logging integrates with other Google Cloud services, such as Cloud Monitoring and BigQuery, for advanced analysis and visualization.
It supports log export to external systems and tools for further processing and archiving.
Cloud Logging helps in troubleshooting, performance monitoring, security analysis, and compliance auditing.

What is Google error reporting?

Google Error Reporting is a service provided by Google Cloud for collecting, analyzing, and managing application error data.
It automatically captures and aggregates error information from applications running on Google Cloud.
Error Reporting supports multiple programming languages and frameworks, including Java, Python, Node.js, and more.
It provides real-time notification and alerting for critical errors and exceptions.
Error data includes stack traces, error messages, affected user information, and other relevant metadata.
It integrates with other Google Cloud services, such as Cloud Monitoring and Cloud Logging, for comprehensive error analysis.
Error Reporting allows for grouping and deduplication of similar errors to focus on root causes and prioritize fixes.
It offers customizable dashboards and reports to visualize error trends, patterns, and impact.
Error Reporting helps in identifying and resolving application issues, improving application quality and user experience.
It supports error data export to external systems and tools for further analysis and integration.

What is Google Cloud Trace?

Google Cloud Trace is a service provided by Google Cloud for capturing, analyzing, and visualizing application latency data.
It helps in understanding and optimizing the performance of applications running on Google Cloud.
Cloud Trace captures timing data for requests and traces the flow of requests across different services and components.
It provides insights into latency bottlenecks, performance issues, and dependencies between different parts of an application.
Cloud Trace integrates with popular programming languages and frameworks, allowing developers to instrument their code for tracing.
It offers detailed information about request processing time, network latency, and time spent in different parts of the application.
Cloud Trace provides visualizations, including flame graphs and waterfall diagrams, to visualize the latency distribution and identify bottlenecks.
It integrates with other Google Cloud services, such as Cloud Monitoring and Cloud Logging, for comprehensive performance analysis.
Cloud Trace helps in troubleshooting and optimizing application performance, leading to improved user experience and efficiency.
It supports exporting trace data to external systems and tools for further analysis and integration.

What is a Google Cloud profile?

Google Cloud Profiler is a service provided by Google Cloud for profiling and analyzing application performance.
It helps developers identify performance bottlenecks, hotspots, and inefficiencies in their applications.
Cloud Profiler collects and analyzes CPU usage, memory allocations, and function call traces.
It provides detailed performance data at the function level, allowing developers to pinpoint performance issues.
Cloud Profiler integrates with popular programming languages and frameworks, including Java, Go, Python, and Node.js.
It requires minimal code instrumentation to capture performance data.
Cloud Profiler offers visualizations and reports to understand application behavior and performance patterns.
It integrates with other Google Cloud services, such as Cloud Monitoring and Cloud Logging, for comprehensive performance analysis.
Cloud Profiler helps in optimizing application performance, reducing resource usage, and improving scalability.
It supports exporting profiling data to external systems and tools for further analysis and integration.

Security

What are rate quotes and allocation quotes?

Rate Quote is an estimate provided by Google Cloud for the cost of using specific Google Cloud services and resources.
It helps customers understand the anticipated charges based on the usage and pricing details of the selected services.
Rate Quotes provide transparency and assist in budgeting and cost planning for using Google Cloud services.
Allocation Quote is an allocation of resources provided by Google Cloud to customers for specific services or projects.
It defines the limits and capacity allocated to a customer, such as compute resources, storage, or API quotas.
Allocation Quotes ensure that customers have sufficient resources available to meet their needs without exceeding the allocated capacity.
Both Rate Quotes and Allocation Quotes are essential for customers to understand the cost implications and resource availability associated with using Google Cloud services.

What is the editor and owner role of Google Cloud?

Editor Role:

The Editor role is a predefined Google Cloud IAM (Identity and Access Management) role.
Users assigned the Editor role have broad access to manage resources within a project.
Editors can create, modify, and delete resources, including instances, networks, and storage buckets.
They can also grant access to other users and assign roles to control resource permissions.

Owner Role:

The Owner role is a predefined Google Cloud IAM role with the highest level of access.
Owners have full control and authority over a project and all its resources.
They can perform all actions available to Editors, as well as manage billing and project settings.
Owners can add or remove other project members, assign roles, and manage IAM policies.

Note: It is important to follow the principle of least privilege and assign roles based on the specific needs and responsibilities of individuals or teams.

What is encryption at rest?

Encryption at rest refers to the practice of encrypting data when it is stored or "at rest" in storage systems or databases.
It ensures that data remains protected even if the storage media or devices are compromised.
Encryption at rest uses cryptographic algorithms to convert plaintext data into ciphertext, rendering it unreadable without the encryption key.
It adds an extra layer of security to sensitive data, preventing unauthorized access or data breaches.
Encrypted data remains encrypted until it is accessed or retrieved by authorized users or applications.
Encryption at rest is commonly used in cloud storage services, databases, and file systems.
It helps organizations meet security and compliance requirements for data protection and privacy.
Encryption keys used for encryption at rest should be securely managed and protected to maintain the confidentiality and integrity of the data.

What is Google's front end?

Google Front End (GFE) is a global infrastructure component of Google Cloud that handles incoming network traffic.
GFE acts as a load balancer and SSL/TLS termination point for secure connections.
It receives client requests and distributes them to backend services for processing.
GFE supports Transport Layer Security (TLS) encryption for secure communication between clients and services.
It performs SSL/TLS termination by decrypting incoming encrypted traffic and forwarding it to backend services as unencrypted traffic.
GFE handles the complexities of SSL/TLS negotiation and certificate management.
It offloads the computational overhead of SSL/TLS encryption from backend services, improving their performance.
GFE supports advanced features like HTTP/2 and QUIC protocols for efficient and faster communication.
Google Front End plays a crucial role in providing secure and scalable network access to Google Cloud services.

What is intrusion detection?

Intrusion detection is a security mechanism used to identify and respond to unauthorized activities or malicious behavior within a computer system or network.
It involves monitoring and analyzing network traffic, system logs, and other data sources to detect signs of intrusion or suspicious activity.
Intrusion detection systems (IDS) can be host-based or network-based, depending on the scope of monitoring.
Host-based IDS monitor activities on individual systems, while network-based IDS monitor network traffic for anomalies.
Intrusion detection systems use various techniques such as signature-based detection, anomaly detection, and behavior-based detection.
Signature-based detection involves comparing observed patterns with known attack signatures or patterns.
Anomaly detection identifies deviations from normal behavior based on predefined thresholds or statistical models.
Behavior-based detection analyzes user and system behavior to identify abnormal or malicious activities.
Intrusion detection systems generate alerts or notifications when suspicious activities are detected, allowing for timely response and mitigation.
Intrusion detection is an important component of a comprehensive security strategy and helps protect systems and networks from unauthorized access and potential threats.

What is insider risk?

Insider risk refers to the potential threat posed by individuals within an organization who have authorized access to sensitive data, systems, or resources.
It involves the risk of intentional or unintentional misuse, theft, or compromise of confidential information by employees, contractors, or trusted insiders.
Insider risks can arise from malicious actions, such as data theft, sabotage, or unauthorized access, as well as unintentional errors or negligence.
Insider risks can result in data breaches, financial loss, reputational damage, and regulatory non-compliance.
Common insider risk scenarios include unauthorized data access, unauthorized sharing of sensitive information, insider trading, and intellectual property theft.
Insider risk mitigation strategies include implementing access controls and least privilege principles, monitoring and auditing user activities, and conducting employee awareness and training programs.
Insider risk management involves a combination of technical controls, policies, procedures, and employee education to detect, prevent, and respond to insider threats.
Organizations may deploy insider threat detection solutions, data loss prevention (DLP) tools, and behavior analytics to identify suspicious activities and patterns.
Collaboration between HR, IT, and security teams is crucial in addressing insider risk through effective policies, incident response plans, and employee monitoring practices.
Proactive identification and mitigation of insider risks are essential to protect sensitive data, maintain trust, and safeguard organizational assets.

What is the security responsibility shared between customers and Google Cloud?

What is a different type of encryption key?

What is Google KMS?

What is cloud identity?

What are predefined roles and custom roles?

What is a service account?

What is a virtual private cloud?

What is a public IP address and a private IP address?

Networking

What is Load balancing?

What is IPsec VPC protocol?

What is direct peering?