Data Security Posture Management: Best Practices for Cloud Data Protection.
Data security is a crucial aspect of any business that relies on cloud services to store, process, and access its data. However, cloud data also poses unique challenges and risks that traditional security measures may not be able to address effectively. That’s why data security posture management (DSPM) is becoming a must-have solution for cloud data protection.
What is DSPM
DSPM is a set of practices and technologies used to assess, monitor, and reduce the risk related to data residing in cloud data stores – with a focus on multi-cloud environments. DSPM is data-centric, in that it looks at the context and content of the data being protected, placing the focus on sensitive records such as PII or medical records.
DSPM works by providing automatic visibility, risk assessment, and access analysis for cloud data. It helps organizations identify where sensitive data is stored in cloud environments, how it is classified and protected, and what are the potential threats and vulnerabilities that could compromise it. It also helps organizations implement appropriate security controls and policies to ensure that sensitive data remains secure throughout its lifecycle.
Why is DSPM important?
DSPM is important because it helps organizations achieve a higher level of data security than what perimeter-focused methods can offer. Perimeter-focused methods rely on static defenses such as firewalls, antivirus software, or encryption to protect the perimeter of the network from external attacks. However, these methods are not enough to protect the data that is constantly moving and adapting within the network.
DSPM offers a dynamic approach that ensures that sensitive data retains its security posture even when duplicated or moved across different cloud environments. It also offers a continuous and adaptive process that evolves with technological advancements and escalating cyber threats.
How does DSPM differ from traditional security measure?
Traditional security measures are mainly focused on securing the infrastructure or network layer of the cloud environment. They use tools such as vulnerability scanners or penetration testers to detect any gaps or issues in security controls. They also use tools such as firewalls or antivirus software to block or detect any unauthorized access or misuse of the network resources.
However, traditional security measures are not enough to address the challenges posed by cloud data. They do not provide visibility into where sensitive data is stored in different cloud environments, how it is being used or accessed by different users or applications, or what are the risks involved in each scenario. They also do not provide consistent rules and standards for accessing or modifying sensitive data across different cloud environments.
DSPM differs from traditional security measures by providing a comprehensive view of sensitive data location, access, and security posture. DSPM excels in data cataloging, discovery, classification, lineage, governance, and compliance. DSPM connects the dots between data and the infrastructure security, allowing security teams to understand what sensitive data is at risk instead of showing them a list of vulnerabilities to remediate. Essentially DSPM is adding a layer of data security and data context over the infrastructure security.
Issues that need to be addressed?
Data discovery and classification: DSPM tools need to be able to locate and categorize data across different cloud environments, services, and applications. This requires accurate and up-to-date metadata and data tagging frameworks that can capture the structure, quality, and sensitivity of the data.
Vulnerability and risk assessment: DSPM tools need to be able to identify and assess potential security risks and weak points in the cloud environment. This requires using tools such as vulnerability scanners or penetration testers that can detect any gaps or issues in security controls.
Policy enforcement: DSPM tools need to be able to apply consistent rules and standards for accessing or modifying sensitive data across different cloud environments. This requires using tools such as access control lists (ACLs) or encryption keys that can restrict or protect access to sensitive data.
Alerting and reporting: DSPM tools need to be able to monitor the status of sensitive data across different cloud environments. This requires using tools such as dashboards or reports that can track any changes or incidents related to sensitive data.
Data governance: DSPM tools need to be able to provide a clear strategy for managing the cloud resources in alignment with the business goals, regulatory requirements, and risk appetite. This requires defining roles and responsibilities, establishing policies and procedures, and measuring performance and compliance.
Automation: DSPM tools need to be able to automate some aspects of the data security posture management process, such as scanning, testing, or reporting. This can help reduce human errors, save time and resources, and improve efficiency and accuracy.
Secure coding standards: DSPM tools need to ensure that the developers and engineers who create applications or services that interact with the cloud resources follow secure coding standards when developing them. This can help prevent common vulnerabilities such as injection attacks, cross-site scripting (XSS), or broken authentication.