Database Security

What is Database Security?

Database security encompasses the measures used to protect database integrity, confidentiality, and availability. It involves a range of strategies including data access control, database activity monitoring, data masking, and auditing to prevent unauthorized access, data breaches, and other security threats. Effective database security is crucial for maintaining data privacy, regulatory compliance, and protecting organizational assets. It addresses both internal and external threats, ensuring data remains secure throughout its lifecycle.

Why is Database Security Unique?

Database security is different from securing other assets because it is often the most critical part of an organization’s infrastructure where the most sensitive data lives. Protecting the database involves applying well known security best practices of securing the network and the server that the database resides on, but also going several steps further and securing the data that lives inside it.

The table below contrasts database security to application security and infrastructure security

Aspect	Database Security	Application Security	Infrastructure Security
Focus	Protecting data stored in databases	Securing software applications	Ensuring the security of hardware and network systems
Primary Goals	Data integrity, confidentiality, and availability	Code integrity, user authentication, and authorization	Network integrity, system uptime, and physical security
Techniques Used	Access control, encryption, data masking, auditing	Input validation, secure coding practices, vulnerability scanning	Firewalls, intrusion detection systems, patch management, access management
Common Threats	Unauthorized access, data breaches	Cross-site scripting (XSS), SQL injection, code tampering	DDoS attacks, unauthorized physical access, network breaches
Tools and Solutions	Database Firewalls, Database Activity Monitoring( DAM), Data Masking, Data Security Posture Management (DSPM)	Web application firewalls (WAFs), static analysis tools	Network firewalls, intrusion detection/prevention systems, privileged access management (PAM)
Stakeholders	DBAs, infrastructure team, security analysts, compliance officers	Developers, QA testers, security analysts	Network administrators, IT support, security analysts

Data Loss Prevention vs Database Security 

Data Loss Prevention (DLP) and database security serve distinct but complementary roles in safeguarding organizational data. 

• DLP focuses on preventing data leakage across the entire organization by monitoring and controlling data transfer across endpoints, networks, and cloud services. It ensures sensitive data is not accessed or transmitted outside authorized channels. 
• In contrast, database security specifically protects data within databases. It involves measures such as data access control, database activity monitoring, data masking, and auditing to safeguard data integrity and confidentiality. 

While DLP addresses the broader scope of data transfer points, database security is confined to protecting data stored within databases. DLP uses monitoring and policy enforcement techniques, whereas database security employs encryption, masking, and access controls. Ultimately, DLP aims to prevent data leakage, while database security protects data from breaches and unauthorized access within database environments.

Common Database Security Challenges

Database security is essential for protecting sensitive information, but organizations face several challenges in this area. These challenges often arise from the unique requirements and complexities of database environments.

Access Control:

• Access management is critical for every security model, and organizations typically invest in centralized Identity and Access Management (IAM) and identity governance programs. However, when it comes to databases, access control is managed using database-specific roles, which cannot be inherited from central IAM entitlements. This disconnect creates a fragmented approach to access management, making it difficult to ensure consistent security policies across the organization. Without centralized control, managing access to sensitive data becomes more complex and error-prone. Read how IAM Can’t Solve Today’s Data Security Problems

Least Privilege:

• Implementing a least privilege model is a key security principle, but it is particularly challenging for databases. Managing the combination of roles and permissions using views, User-Defined Functions (UDFs), and stored procedures is often infeasible due to the required skills and time constraints. Additionally, database permissions are rarely granular enough and cannot be managed dynamically, making it difficult to enforce the principle of least privilege effectively. This complexity can lead to over-privileged accounts, increasing the risk of unauthorized access.

Logging:

• Effective logging is crucial for monitoring database activities, but it poses significant challenges. Logging database operations can impede performance, as it requires additional resources to capture and store logs. Moreover, logs can inadvertently contain sensitive data, leading to potential data leakage if not handled properly. Balancing the need for comprehensive monitoring with the performance and security implications of logging is a complex task that requires careful planning and management.

Auditing:

• While auditing is essential for ensuring compliance and detecting security incidents, it is often harder to implement than it appears. Security teams typically rely on data teams for auditing, which compromises the segregation of duties. Database administrators (DBAs) can easily turn off or bypass auditing controls, undermining the effectiveness of the audit process. Ensuring robust auditing without compromising the independence of the security function requires strong policies and oversight mechanisms.

Service Accounts:

• Databases often have numerous service accounts used by various applications, BI tools, ETL jobs, and other automated processes. These service accounts can be challenging to manage and secure. They typically require high levels of access to perform their functions, making them attractive targets for attackers. If compromised, service accounts can be abused to gain unauthorized access to sensitive data or execute malicious activities. Properly managing service accounts involves regularly reviewing and updating their permissions, rotating credentials, and monitoring their activities to detect any unusual behavior. This requires a coordinated effort between database administrators and security teams to ensure that service accounts do not become a weak link in the security chain.

By addressing these challenges, organizations can improve their database security posture and better protect sensitive information from unauthorized access and breaches.

Database Security meets Cloud

The adoption of cloud computing brings additional complexities to database security. The cloud environment introduces new variables and challenges that organizations must navigate to protect their data effectively.

Lack of Control Over Underlying Infrastructure:

• In cloud environments, especially with Platform-as-a-Service (PaaS) offerings like AWS RDS, Snowflake, GCP BigQuery, and Azure Managed SQL, organizations do not have control over the underlying server and network infrastructure. This lack of control limits the ability to implement traditional security measures such as server hardening and network segmentation. Organizations must rely on the cloud provider’s security measures and ensure that their own security practices align with the provider’s controls. This dependency requires thorough understanding and continuous monitoring of the shared responsibility model between the cloud provider and the customer.

Ease of Database Provisioning and Replication:

• The cloud makes it extremely easy for users to spin up new databases and replicate existing ones. This convenience, while beneficial for agility and scalability, introduces significant security risks. Uncontrolled database provisioning can lead to a proliferation of databases, each potentially containing sensitive information. Without stringent governance, it becomes challenging to enforce consistent security policies across all databases. Additionally, replicated databases may not have the same security configurations as the original, increasing the risk of data breaches.

Data Democratization and Increased Access Demands:

• Cloud adoption often goes hand-in-hand with the pursuit of data democratization, which aims to make data more accessible to a broader range of users within the organization. While this approach can drive innovation and decision-making, it also increases the risk of unauthorized access. More people wanting access to data means a higher likelihood of improper data handling and potential security lapses. Ensuring that only authorized users have access to sensitive data requires robust access controls and continuous monitoring, which can be challenging to maintain in a rapidly changing cloud environment. Read about the Top 5 data security risks for organizations

Data Residency and Compliance:

• Cloud environments often span multiple geographic regions, which can complicate data residency and compliance requirements. Different jurisdictions have varying regulations regarding data storage and transfer, making it crucial to understand where data resides and ensure compliance with applicable laws. Managing these requirements adds an extra layer of complexity to database security, requiring continuous oversight and coordination with legal and compliance teams.

By addressing these cloud-specific challenges, organizations can better protect their data and maintain robust database security even as they leverage the benefits of cloud computing.

Database Security Best Practices

Below is a summary of key practices to secure your databases effectively.

• Federated Access Control: Implementing strong access control mechanisms is fundamental. Use centralized Identity and Access Management (IAM) systems to manage user identities and permissions, even if databases require specific roles. Regularly review and update access controls to ensure that only authorized users have access to sensitive data. Employ multi-factor authentication (MFA) to add an extra layer of security.
• Policy-based Data Access Control: Use policies to apply the least privilege principle to limit users’ access to only the data they need. Although complex for databases, strive to manage permissions using declarative policies that are specified centrally and are then enforced through an external authorization mechanism or translated into database-specific policies. Continuously monitor and adjust permissions to avoid over-privileged accounts and reduce the risk of unauthorized access.
• Dynamic Data Masking: Use dynamic data masking for production environments and on-the-fly masking for ETL processes. Try to implement centralized masking policies managed by an external authorization service to ensure consistency and simplify administration. Employ federated identities to enforce masking policies dynamically based on user entitlements, and ensure service account queries are annotated with user identities.
• Externalized Monitoring and Auditing: Implement an external tool-based comprehensive monitoring and auditing to track database activities. Use advanced tools to manage logging efficiently, avoid performance penalties and ensure sensitive data is not exposed in logs. Regularly review logs and audit trails to maintain oversight.
• Service Account Resolution: For logging and policy enforcement, make sure the controls can see through service accounts used by applications, BI tools, and ETL jobs. Regularly review and update their permissions, rotate credentials, and monitor their activities to detect any unusual behavior. Implement policies to ensure service accounts are used securely and do not undermine other security measures.
• Data Discovery and Classification: Implement automated discovery and classification tools to keep data labeling updated. Define masking and access control policies based on data types (e.g., PII) rather than specific fields to ensure comprehensive protection. Regularly scan databases to identify and mask sensitive data, reducing the risk of exposure.

By integrating these best practices, organizations can build a robust database security framework that protects sensitive information, ensures regulatory compliance, and supports secure data management across various environments. This comprehensive approach addresses the complexities and evolving nature of database security, providing a solid foundation for safeguarding data assets.

Cloud Database Security and Cyral

Cyral’s cloud database security solution enables organizations to seamlessly integrate identity management and data observability, facilitating observation of any data access attempt on any repository, with the full user context, at any given time. Cyral’s cloud data access governance ensures that any access attempt triggers an instant matching of the user with their IAM groups, a reference to a single source of policy rules, and the delivery of a password through a password storage solution. All data sources can be monitored in real time without a negative impact on performance, and a single, rich source of logs is available for audits, compliance requirements, and forensics. This information can be used by security and DevOps teams for troubleshooting, forensics, and incident response. Robust data activity monitoring, policy-based cloud access control, least privilege, and identity federation capabilities provide a powerful solution that helps companies establish a secure cloud database while also improving visibility.

Learn more about Cyral’s Cloud Database Security solution here.