Through lectures, demonstrations, and hands-on labs, participants explore and deploy the components of a secure GCP solution. Participants also learn mitigation techniques for attacks at many points in a GCP-based infrastructure, including Distributed Denial-of-Service attacks and threats involving content classification and use.
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Course Description
A Professional Cloud Security Engineer enables organizations to design and implement a secure infrastructure on Google Cloud Platform. Through an understanding of security best practices and industry security requirements, this individual designs, develops, and manages a secure infrastructure leveraging Google security technologies.
The Cloud Security Professional should be proficient in all aspects of Cloud Security including managing identity and access management, defining organizational structure and policies, using Google technologies to provide data protection, configuring network security defenses, collecting and analyzing Google Cloud Platform logs, managing incident responses, and an understanding of regulatory concerns.
The Cloud Security Professional should be proficient in all aspects of Cloud Security including managing identity and access management, defining organizational structure and policies, using Google technologies to provide data protection, configuring network security defenses, collecting and analyzing Google Cloud Platform logs, managing incident responses, and an understanding of regulatory concerns.
Learning Objectives
This course teaches participants the following skills:
- Understanding the Google approach to security Managing administrative identities using Cloud Identity.
- Implementing least privilege administrative access using Google Cloud Resource Manager, Cloud IAM.
- Implementing IP traffic controls using VPC firewalls and Cloud Armor Implementing Identity Aware Proxy Analyzing changes to the configuration or metadata of resources with GCP audit logs Scanning for and redact sensitive data with the Data Loss Prevention API Scanning a GCP deployment with Forseti Remediating important types of vulnerabilities, especially in public access to data and VMs.
Certification Curriculum
Module 1
Foundations of GCP Security
- Google Cloud’s approach to security
- The shared security responsibility model
- Threats mitigated by Google and by GCP
- Access Transparency
Module 2
Cloud Identity
- Cloud Identity
- Syncing with Microsoft Active Directory
- Choosing between Google authentication and SAML-based SSO
- GCP best practices
Module 3
Identity and Access Management
- GCP Resource Manager: projects, folders, and organizations
- GCP IAM roles, including custom roles
- GCP IAM policies, including organization policies
- GCP IAM best practices
Module 4
Configuring Google Virtual Private Cloud for Isolation and Security
- Configuring VPC firewalls (both ingress and egress rules)
- Load balancing and SSL policies
- Private Google API access
- SSL proxy use
- Best practices for structuring VPC networks
- Best security practices for VPNs
- Security considerations for interconnection and peering options
- Available security products from partners
Module 5
Monitoring, Logging, Auditing, and Scanning
- Stackdriver monitoring and logging
- VPC flow logs
- Cloud audit logging
- Deploying and Using Forseti
Module 6
Securing Compute Engine: techniques and best practices
- Compute Engine service accounts, default and customer-defined
- IAM roles for VMs
- API scopes for VMs
- Managing SSH keys for Linux VMs
- Managing RDP logins for Windows VMs
- Organization policy controls: trusted images, public IP address, disabling serial port
- Encrypting VM images with customer-managed encryption keys and with customer-supplied encryption keys
- Finding and remediating public access to VMs
- VM best practices
- Encrypting VM disks with customer-supplied encryption keys
Module 7
Securing cloud data: techniques and best practices
- Cloud Storage and IAM permissions
- Cloud Storage and ACLs
- Auditing cloud data, including finding and remediating publicly accessible data
- Signed Cloud Storage URLs
- Signed policy documents
- Encrypting Cloud Storage objects with customer-managed encryption keys and with customer-supplied encryption keys
- Best practices, including deleting archived versions of objects after key rotation
- BigQuery authorized views
- BigQuery IAM roles
- Best practices, including preferring IAM permissions over ACLs
Module 8
Protecting against Distributed Denial of Service Attacks: techniques and best practices
- How DDoS attacks work
- Mitigations: GCLB, Cloud CDN, autoscaling, VPC ingress and egress firewalls, Cloud Armor
- Types of complementary partner products
Module 9
Application Security: techniques and best practices
- Types of application security vulnerabilities
- DoS protections in App Engine and Cloud Functions
- Cloud Security Scanner
- Threat: Identity and Oauth phishing
- Identity Aware Proxy
Module 10
Content-related vulnerabilities: techniques and best practices
- Threat: Ransomware
- Mitigations: Backups, IAM, Data Loss Prevention API
- Threats: Data misuse, privacy violations, sensitive/restricted/unacceptable content
- Mitigations: Classifying content using Cloud ML APIs; scanning and redacting data using Data Loss Prevention API
Prerequisites
To get the most of out of this course, participants should have:
- Prior completion of Google Cloud Platform Fundamentals: Core Infrastructure or equivalent experience Prior completion of Networking in Google Cloud Platform or equivalent experience Knowledge of foundational concepts in information security.
- Fundamental concepts: vulnerability, threat, attack surface confidentiality, integrity, availability Common threat types and their mitigation strategies Public-key cryptography Public and private key pairs Certificates Cipher types Key width Certificate authorities.
- Transport Layer Security/Secure Sockets Layer encrypted communication Public key infrastructures Security policy Basic proficiency with command-line tools and Linux operating system environments Systems Operations experience, including deploying and managing applications, either on-premises or in a public cloud environment Reading comprehension of code in Python or JavaScript
Download Brochure
Join Google Cloud Certified Professional Cloud Security Engineer Training and gain the skills needed to enable organizations to design and implement a secure infrastructure on Google Cloud. Download the brochure and check the different focus areas that are covered within these three days of training.
Certification Assessment
The exam guide contains a complete list of topics that may be included on the exam. Review the exam guide to determine if your skills align with the topics on the exam.
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Testimonials
Our clients praise us for our great results, personable service, expert knowledge, and on-time delivery. Here are what just a few of them had to say:
Sarbojit Bose
The course is one of the two in the track of Agile Professional Coach. It is designed to provide both wide and deep knowledge to become a competent Coach with the additional skills of a Trainer and a Mentor. The two trainers, Preeth Panday and Naveen K Singh, are excellent Facilitators and Coaches with patience and promptness.
Tiong Yeow Tan
It was a good introductory course to agile coaching, providing a good overview and potential deep dive into other courses if one wishes to learn more about a particular topic. Trainer was able to address most of the questions from the participants and demonstrate coaching in his interaction with us.
Chun Ngee Boo Alson
The course is well structure. Timing is also right. The trainer Mr Raj is professional. And he asnwer all my question and doubts.
Puli
One of best I have encountered in my life. Freedom to interact and respond candidly and with courage for every question is not an easy task for Trainers which they did it exceptionally well.
Training FAQ's
What is Machine Learning Training all about?
In the Machine Learning Training, you will learn how to write distributed machine learning models that scale in Tensorflow, scale out the training of those models and offer high-performance predictions
What is the duration of Machine Learning Training?
The duration of Machine Learning Training is two-days.
Who can take up the Machine Learning Training?
Data Engineers and programmers interested in learning how to apply machine learning in practice or anyone interested in learning how to build and operationalize TensorFlow models can apply for Machine Learning Training.
What is the benefit of taking up the Machine LearningTraining?
With Machine Learning Training with TensorFlow Certification, you can strengthen your cloud knowledge, earn a digital certificate, and start preparing for an industry-recognized Google Cloud certification.
What are the prerequisites of Machine LearningTraining?
Participants should have experience coding in Python, knowledge of basic statistics, and knowledge of SQL and cloud computing.
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