Successful application deployments, analytics procedures, and machine learning advancements rely on data. As a result, data protection is one of the most prominent concerns among customer executives to consider a shift and create an AWS Account.
Data protection entails using tools and technology to improve an organization’s understanding of where personal data and other sensitive information are stored and how they get used.
This understanding is important for many web services, but it is especially important for using an AWS account.
When correctly implemented, robust data security control and access control policies will safeguard an organization’s information assets from cybercriminals’ insider threats and human errors, which are still among the primary causes of today’s global infrastructure breaches.
This article will discuss the following:
- What Is Data Security?
- Types of Data in AWS
- Data Migration to AWS
- Database Security in AWS Best Practices
This is a part of our extensive data security guide.
What Is Data Security?
Data security is the discipline of safeguarding digital data throughout its lifecycle from unwanted access, corruption, or theft. It is a framework that incorporates all aspects of information security, from hardware and storage device physical security to administrative and access controls and the logical security of software applications. It also comprises policies and procedures for the organization.
Types of Data in AWS
Amazon Web Services offer a wide variety of services and partner tools to help you migrate your data sets, whether they are files, databases, machine images, block volumes, or even tape backups.
Some of the unique types of data that AWS supports include the following:
A Relational Database Management System (RDBMS) is a set of applications and features that allow IT professionals and others to develop, update, administer, and interact with relational databases.
A data lake is a centralized repository that can hold both structured and unstructured data at any scale. You can use dashboards and visualizations to make better decisions. You can run several sorts of analytics — from big data processing, real-time analytics, and machine learning— without needing to pre-process the data.
Data Warehouses and Analytics
A data warehouse is a centralized collection of data that you can examine to help people make better decisions. Data regularly flows into a data warehouse from transactional systems, relational databases, and other sources. Business analysts, data engineers, data scientists, and decision-makers use BI tools, SQL clients, and other analytics software to access the data.
For organizations to remain competitive, data and analytics have become essential. Reports, dashboards, and analytics tools get used by business users to gain insights from their data, monitor corporate performance, and help decision-making. These reports, dashboards, and analytics tools are powered by data warehouses, which store data effectively to reduce data input and output (I/O) and deliver query results swiftly to hundreds or thousands of users simultaneously.
Data Migration to AWS
Moving to the cloud is a major decision that requires significant organizational transformation and investment. No organization should take cloud migration lightly, and this is only right.
Getting the executive team on board and aligned with the migration strategy and process will be a significant factor for most firms in AWS cloud migration. That requires developing a credible business case for AWS cloud migration that will gain executive support in the future.
Ultimately, moving data to AWS has the following advantages:
- Agility and Staff Productivity
- Data Center Consolidation
- Security and Resilience
Database Security in AWS Best Practices
Companies must be flexible and ready to succeed in today’s professional landscape. They must be adaptable in the face of rapidly changing technology and customer preferences. As a result, many businesses use Amazon Web Services to accomplish this (AWS).
Following that, below are a few AWS Best Practices:
Know Where Your Sensitive Data is and Focus on it
As you store more and more data in the cloud, you will need security automation to keep it safe at scale. To address this, AWS released Amazon Macie. This fully managed service employs machine learning and pattern matching to help you detect, classify, and better safeguard sensitive data stored in the AWS Cloud.
Many data breaches get caused by mistakes committed by authorized users rather than malicious activities by unauthorized users. Thus, you must first identify sensitive data to monitor and manage its security.
Audit Your Data Access
AWS Audit Manager simplifies how you analyze risk and compliance with regulations and industry standards by allowing you to audit your AWS consumption regularly. Audit Manager automates evidence collecting, reducing the manual labor that frequently occurs during audits and enabling you to scale your audit capacity in the cloud as your company grows.
Have Clear Data Access Policies
Remember that you, not AWS, are responsible for the security of your cloud workloads. It is, therefore, your responsibility to ensure that consumer and company data get safeguarded from unauthorized assaults.
Some cloud security policies and processes are listed below to help you reduce the danger of a data breach:
- Define User Roles Clearly
- Conduct audits of privileges
- Create a Password Policy that is both Strong and Easy to Remember
- Implement Multi-factor Authentication (MFA) and Permission Time-outs
- Make Data Encryption a Priority
These cloud security controls will assist in mitigating some of the risks associated with poor security hygiene, making it more difficult for unauthorized parties to access your data. However, these measures will only be effective if you are consistent in your enforcement and ensure that these controls get implemented throughout your firm.
Data has never had a higher business value than it does now.
Every facet of how today’s organizations operate and compete is getting altered by digital transformation. Consequently, the amount of data that businesses generate, handle, and store increases, necessitating the amount of data that requires more data governance.
Satori, The DataSecOps platform, provides a security layer for data access on your AWS data environment, as well as outside of it. This is done in a non-intrusive way, whether it’s databases, data warehouses, or data lakes.