How can you create a secure database schema?

Every database object is in schema.When you assign a permission to a schema, it is applied to all objects within the schema. Note that this permission can be explicitly denied at object-level. In SQL database the user is defaulted to "dbo" schema, unless if you assign it to a different schema. So it is advised to group together in a schema all the objects that are used for a particular job role. A database role then can be made owner of the schema that maps to the job role and is assigned to that schema by default.

1. Use principle of least privilege 2. Use Views to further solidify #1 3. DML, DCL operations should follow a strict change management cycle 3. PII data should be salted/masked 4. Encryption at rest and transit should be enforced 5. Backups should be encrypted 6. Constraints can be utilized to prevent accidental batch updates on critical tables 7. All DDL, DML and DCL statements should have audit trail 8. A periodic clean up job should be in place depending on the firms data retention standards 9. Data access (Select) should be regularly monitored via ML for suspicious queries 10. Storage should be used smartly, to encrypt/decrypt data efficiently ex. Store numbers in INT vs Varchar etc.

Have a high level design document created and get it reviewed by all stakeholder and confirm whether it is inline with business.Once you define your design you can work on security aspect of it by working on defining encryption strategy and based on your business policies define implement those strategy either by encrypting data in rest or in transit.

Considerations to minimizes security risks and vulnerabilities will help us to create a secure database schema. There are many things that might go wrong, however, few things that comes to my mind - 1. Understand security requirements and work closely with the Network Security team and DBA 2. Use strong authentication and authorization while accessing the database by any means including API access and and ensuring the "Least Privilege Principle" for the db users 3. Data encryption to encrypt sensitive data, both in transit and at rest. 4. Keep the DBMS and related software up to date by applying regular updates and patches.

Creating a secure database schema involves defining data needs, selecting the right data model, and applying normalization techniques. Enhance data security using validation and encryption, and utilize consistent naming conventions. Proper documentation aids in understanding, while regular testing ensures functionality and security. Incorporate Role-Based Access Control, maintain encrypted backups, and implement regular monitoring. Keep the DBMS updated and consider multi-factor authentication for added security.

Don't be afraid to propose different data models for different types of information in the same project or feature. Depending on the shape or nature of the data it could be best represented as SQL, NoSQL, graph, etc. Even the same entity, could have some part of it stored as a row in a table, and another section as a JSON document.

I'll focus on a Secure Database Schema, nothing else. * Perform a diligent design of *roles*, which are an object type in practically all DBMS-s, and which can be granted to other roles, and to individual users. * If your database allows it, use data access policy. This automatically rewrites any queries to include additional filter conditions to mask away rows the current user is not intended to see, that applies functions to mask their content, even at different levels, depending on the privileges of the role/user. * If that does not exist in your DBMS, use one view per privilege level, and grant accordingly instead of the table.

In my experience, knowing your data consumers is very crucial to know the level of security needed for your data on top of some basic ground rules. It's good to get started with understanding and defining requirements, identifying the data model to suit your needs, implementing the best practices for Data Performance ,Security and Scalability.

Data Structure: Structured: Best for consistent tabular data; use RDBMS like MySQL or PostgreSQL. Semi-Structured: For data with some organization, like JSON or XML; consider NoSQL databases such as MongoDB. Unstructured: For data without a specific format, like text or images; use solutions like Elasticsearch or blob storage. Data Relationships: Relational: For data with tabular relationships, use RDBMS. Complex/Hierarchical: For intricate relationships, consider document databases like MongoDB or graph databases like Neo4j. Scalability: Vertical: Add resources to an existing server, typical for RDBMS.

When designing a database schema, start by understanding your application's data requirements and relationships. Choose a relational model for structured data with clear relationships, or a NoSQL model like document or key-value for more flexible or semi-structured data. Consider your system's scalability, performance needs, and whether ACID compliance is crucial. Assess the ease of use and your team's familiarity with the model. It's advisable to prototype and test different data models with real-world scenarios to ensure they meet your needs before finalizing your choice.

The idea behind normalized database is to ensure the atomic nature of information while maintaining the knowledge it possesses. Which is why overly normalized database can also lead to performance downfall. General rule of thumb when normalizing a database is to keep track points of both interaction and extraction for the dataset. For instance, in a relational database 3NF is more than enough to achieve a secure and performant database because at 3NF the tables can be clustered and maintained in different data-houses.

The normalization techniques are based on the relational model, that is in the SQL world, a good design has the flexibility to support present and future requirements. It is a key point when you develop Transactional systems because its aims are: performance and no redundancy. On the other hand, when you need to summarize data you should change perspective and design tables to support a multidimensional design, for example, that is time (day, week, month, year), categories (product, category, family), locations (shop, district, region, country, continent), etc. The normalization and denormalization techniques have their own guides and provide complementary applications to support business operations and make decisions.

If performance is the most important factor, then denormalization may be a good option. However, if data integrity is the most important factor, then normalization is a better choice. - Normalize your database. - Denormalize your database only when necessary and only when there is a clear performance benefit. - Monitor your database performance and make adjustments as needed.

Normalization organizes a database into tables to minimize redundancy and avoid data anomalies, applied through a series of normal forms. Denormalization introduces redundancy by combining tables or duplicating columns to improve performance and simplify data retrieval. Both techniques require understanding of the data and queries, with the goal to balance between data integrity, performance, and scalability according to your application's needs.

En este punto se debe establecer y simular en la práctica hasta que nivel la normalización no empieza a convertirse en un factor de inestabilidad en el performance de mi BD. Esto es una tarea difícil y complicada a la vez porque implica recrear datos de la vida real.

However, encryption at column levels often create additional overheads and full table scans causing performance to suffer. Also, backup and recovery becomes more difficult specially when integrating with third party vendors. If not done correctly, in terms of configuration and key access, recovering from a disaster type of event might not be possible. When designing pipelines with encrypted data, proper attention has to be placed in access to the keys as the raw encrypted data might be moved into a system where more people would need access to. Other techniques can be used to minimize some of these situations. They include but are not limited to, removing sensitive data out of your production database into a more secured data system

it worth to note that data encryption generally is done using symmetric encryption, that is using a single key to encrypt/decrypt, that key should be cryptographically strong enough to be really hard to guess using brute force. Data validation could be done at row level calculating a cryptographic hash of the sensitive fields, and storing it at the same row on another column, later you could recalculate the hash and check against the storage hash, if they changed , data have changed and you could considerer it a security breach.

Data encryption or data anonymization is important as a last defensive line in case of perimeter defense breach. Anonymizing personally-identifiable (PII) data in a database protects not only from external threats, but also (perhaps more importantly) from internal privileged users.

Synthetic data can be used to create a secure database schema without revealing sensitive information. This approach helps to protect the privacy of individuals while still allowing for accurate testing and analysis. Additionally, synthetic data can be easily modified to simulate different scenarios, making it a versatile tool for database management.

Ensure the input validation before storing data into the database Encrypting the data at rest with encryption key stored in KMS like system and can be rotated at designated interval.

In my experience, proper and Consistent Naming conventions( should be defined in your project's Coding Standards), is one of the most important items to have to increased ease of collaboration, have an efficient development process and smoother maintenance cycle. Also, make your code easily readable so minimum comments are required to explain it. This will also help to maintain a clear and concise documentation.

Have proper naming standard followed in all object naming be it tables,procedures or views or indexes.Having proper naming standard help in knowledge transition and also is very helpful during migration strategy.Any change request to database should follow proper flow which should already defined in your organisation.

In the two projects I worked on, I had to deal with a pre-existing database design. While the database itself was functional, the naming conventions for columns and table names were not helpful in understanding which table served what purpose. To make sense of it, I often had to inspect the values and review the code.

As I strategize on creating a secure database schema, my approach includes classifying sensitive data and employing robust encryption. I'll implement the least privilege principle for access controls, normalize data to maintain integrity, and rigorously validate inputs to thwart SQL injections. Regular security assessments, timely DBMS patching, and secure, tested backups are on my agenda. I plan to use network safeguards and prepare incident response protocols, understanding that database security is an ongoing task demanding vigilance and proactive measures.

Consistency is a must ingredient of a meaningful database. Follow always the rule "No job is finished till the paperwork is done", for fully document a database.

Testing and reviewing a database schema is a crucial step in the database development process to ensure its accuracy, efficiency, and security. Here's a systematic approach to test and review a database schema: 1. Review the Requirements: Understand the Purpose: Clearly understand the purpose of the database and its intended use cases. 2. Data Integrity and Constraints: 3. Normalization: 4. Performance: 5. Security: 6. Data Consistency: 7. Error Handling: 8. Backup and Recovery: 9. Scalability 10. Documentation 11. User Acceptance Testing (UAT): 12. Review Tools: 13. Iterative Process:

From my own experience, testing the database schema is vital to ensure data integrity, performance optimization and enhance security. It can facilitate the data integration, reduce redundancy and keep an easier maintenance. Involving the test to review the database schema ensures data consistency and data accuracy throughout the system and allow easy usage. This review is related directly with the Data Quality standards, which are specific criteria and guidelines for ensuring the accuracy, completeness, consistency, and reliability. It's only a matter of deciding which tools and process are going to be used to ensure that test.

I have been designing db's, protecting data, normalization, testing, documenting, auditing, relational db's, etc for 40 years... I love my job, inventing in everything I do... --> I have lectured to get the message across too... I use the laughing-learning (my) model of education... I have scores with my students of about 100%...

I think that the title should be change from secure to effective relational data model. Nowadays there are many other ways to store data in a database (eg look at big data, structured, vector etc). This article is focused solely on structured relational databases. Also the word secure is a bit confusing to me - because a schema cannot be secure or not. It is the database engine that secures it since the validations, encryption and even in most cases nowadays the row access is controlled at that level

Creating a secure database schema involves a multi-faceted approach. Start by following the principle of least privilege, ensuring that database users and applications have the minimum permissions necessary to perform their tasks. Employ strong, unique, and encrypted authentication mechanisms to restrict unauthorized access. Design the schema with data sensitivity in mind, separating and encrypting sensitive data, and employing proper access controls and auditing mechanisms to monitor access and changes to the data. Utilize secure communication protocols to protect data in transit, and regularly update and patch the database management system to protect against known vulnerabilities.

Creating a secure database is crucial in today's digital era. Start by defining data requirements, then select the right data model, balancing between normalization and denormalization. Ensure data validation and encryption are in place. Adopt consistent naming conventions, maintain clear documentation, and implement role-based access control. Regular backups, consistent monitoring, and timely updates are key. Always prioritize security; it's not just about storing data but protecting it. Stay updated with emerging threats and solutions

building a schema is like building a vault to safeguard the gold and money in this case it is the information. Just like constructing a vault, it requires meticulous planning, solid implementation, and rigorous testing to make sure that the data remains secure leaks and bad incidents will cost a lot! so making sure that the schema is secure is all about protecting it from unauthorized and public access, modifications, or deletions, while also ensuring that the data remains accurate and consistent. It definitely involves careful planning, accurate implementation, and thorough testing

Ensuring that the database transactions are logged and secured with appropriate encryption protocols (preferably different security keys) adds security from attackers stealing logs and recreating a clone. It is a good practice to keep application level locks on database in shared environments so same user can never access more than one database in full capacity.