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Cloud Bigtable is a fully managed NoSQL database service designed to handle large analytical and operational workloads. One of its key features is the ability to replicate data across multiple geographic locations automatically, ensuring high availability and resilience. Here’s a detailed explanation:

Replication: Cloud Bigtable supports multi-cluster routing and replication across different geographic regions. This means that data can be replicated across multiple zones within a region or even across regions, providing geo-redundancy.

Automatic Handling: Once configured, Bigtable automatically manages replication without requiring manual intervention. This is in line with the company's policy for long-term data storage that necessitates automatic replication over at least two geographic places.

Use Case Suitability: Bigtable is ideal for applications that require low-latency access to large amounts of data, which makes it suitable for various use cases including analytical applications, IoT, and financial data processing.

Configuration: Setting up replication involves creating instances in multiple zones and configuring them to replicate data. Google Cloud’s management interface and APIs make this straightforward to configure and monitor.

Google Cloud Bigtable Documentation

Google Cloud Storage Options

If the environment variable GOOGLE_APPLICATION_CREDENTIALS is set, ADC uses the service account key or configuration file that the variable points to. If the environment variable GOOGLE_APPLICATION_CREDENTIALS isn't set, ADC uses the service account that is attached to the resource that is running your code. https://cloud.google.com/docs/authentication/production#passing_the_path_to_the_service_account_key_in_code

"The service evaluates all changes and remote access attempts to detect runtime attacks in near-real time." : https://cloud.google.com/security-command-center/docs/concepts-container-threat-detection-overview This has nothing to do with KNOWN security Vulns in images

This option directly addresses the needs of the business user who must access curated reports. By creating curated tables in a separate dataset, you can control access to specific data. Assigning the roles/bigquery.dataViewer role allows the business user to view the data in BigQuery.

To organize GCP projects based on different business units and manage IAM permissions, you should create an organization node and assign folders for each business unit. This approach allows you to logically separate projects under folders and apply IAM policies at the folder level.

Step-by-Step:

Create Organization Node: Ensure that your GCP account is linked to an organization.

Create Folders for Business Units:

Navigate to the GCP Console > IAM & Admin > Resource Manager.

Create a folder for each business unit under the organization node.

Move Projects to Folders:

Move existing projects into the respective folders according to the business unit.

Set IAM Policies:

Assign IAM roles and permissions at the folder level to manage access for each business unit independently.

Monitor and Manage: Use Cloud Audit Logs and other GCP tools to monitor the activities and ensure compliance with the organization’s policies.

Creating and Managing Folders

Managing IAM Policies

To ensure that Vertex AI Workbench Instances (formerly AI Platform Notebooks) are automatically updated and that users cannot modify operating system settings, it's crucial to implement organizational policies that enforce these requirements.

disableRootAccess Organization Policy:This policy prevents users from obtaining root access on virtual machines. By enforcing this policy, you ensure that users cannot make unauthorized changes to the operating system settings, maintaining the integrity and security of the instances.

requireAutoUpgradeSchedule Organization Policy:This policy mandates that virtual machines have an auto-upgrade schedule for their operating systems. By enforcing this policy, you ensure that instances are automatically kept up-to-date with the latest security patches and updates, reducing the risk of vulnerabilities.

Given the options:

Option A: Enabling VM Manager helps in managing updates and configurations but does not inherently prevent users from altering OS settings.

Option B: Enforcing the disableRootAccess and requireAutoUpgradeSchedule organization policies directly addresses both requirements: preventing unauthorized OS modifications and ensuring automatic updates.

Option C: Assigning specific roles controls user permissions but does not enforce OS-level restrictions or automatic updates.

Option D: Implementing firewall rules to prevent SSH access adds a layer of security but does not ensure automatic updates or prevent OS modifications through other means.

Therefore, Option B is the most effective approach, as it directly enforces the necessary policies to meet both requirements.

To ensure end-user access is only allowed if the traffic originates from a specific known good CIDR and to utilize GCP's native SYN flood protection, you can use the following product:

VPC Firewall Rules: By configuring VPC firewall rules, you can control traffic to and from your instances based on IP address, protocol, and port. You can set rules to only allow traffic from a specific CIDR block, ensuring that only authorized traffic can reach your application.

Additionally, Google Cloud Platform provides built-in protections against SYN flood attacks, which are a type of DDoS attack. These protections are part of the underlying infrastructure and do not require additional configuration.

Using VPC firewall rules will help you comply with the internal requirement of allowing access only from a specific CIDR and provide the necessary SYN flood DDoS protection.

References

Google Cloud VPC Firewall Rules

Google Cloud DDoS Protection

Comprehensive and Detailed Explanation From Exact Extract:

The problem is the detection of secrets (sensitive data patterns) within the environment variables of deployed resources (Cloud Functions) in a timely, automated manner.

Sensitive Data Protection (SDP), formerly Cloud DLP, is the purpose-built Google Cloud service for scanning and classifying sensitive data patterns. It can be configured to scan code, configuration, or environment variables and integrate its findings directly with Security Command Center (SCC).

Extracts:

"Sensitive Data Protection provides highly configurable, automated detection of sensitive data, including API keys, passwords, and other credentials, using both pre-built and custom infoTypes." (Source 8.1)

"SDP can be integrated with Cloud Functions and other resource configurations to scan environment variables or configuration files for secrets. Violations can be automatically routed to Security Command Center as findings." (Source 8.2)

Option D (DAST) scans the application code or running application logic, but the requirement specifies the secrets are in the environment variables, which are part of the configuration/deployment metadata, making SDP the correct detection tool.

To enhance the security of your microservices architecture on Google Kubernetes Engine (GKE) and ensure that only approved container images are deployed, implementing Binary Authorization is a robust solution.​

Option A: Enforcing Binary Authorization in your GKE clusters ensures that only container images that meet your organization's security policies are deployed. By integrating container image vulnerability scanning into your Continuous Integration/Continuous Deployment (CI/CD) pipeline, you can assess images for known vulnerabilities before they are deployed. Binary Authorization can be configured to use these vulnerability scan results to make policy decisions, effectively preventing the deployment of insecure images. This approach leverages managed services provided by Google Cloud, ensuring scalability and compliance with security standards.​

Option B: Developing custom organization policies to restrict deployments to images within a specific Artifact Registry project helps in controlling the source of images but does not inherently assess the security posture of those images. Without integrated vulnerability scanning and enforcement mechanisms, this approach may not fully mitigate the risk of deploying vulnerable images.​

Option C: Building a system using third-party vulnerability databases and custom scripts requires significant maintenance and may not integrate seamlessly with GKE. This approach can be error-prone and lacks the efficiency of managed services designed for this purpose.​

Option D: Automatically deploying new images upon successful CI/CD builds ensures rapid deployment but does not address the need for security assessments of the images. While setting up firewall rules is good practice, it does not prevent the deployment of potentially vulnerable images.​

Therefore, Option A is the most effective approach, as it utilizes Google Cloud's managed services to enforce security policies and integrate vulnerability assessments directly into the deployment process, ensuring that only approved and secure container images are deployed to your GKE clusters.​