Data security posture management (DSPM) is an approach designed for discovering, assessing, and securing sensitive data assets across an organization. As data sprawl continues across all industries, it has become increasingly difficult to meet enterprise data security needs by just leveraging legacy, system-centric tools that focus purely on system boundaries and offer minimal data visibility. Therefore, adopting a modern, DSPM-centric solution is highly recommended as it provides granular visibility into individual data points and prioritizes securing critical data throughout its life cycle against potential security threats. Take a look at the DSPM best practices below to learn the key strategies that will safeguard your company's vital data against security attacks.
Data loss prevention (DLP) is the process of identifying sensitive data; monitoring its flow across the organization; and preventing its theft, loss, or misuse through unintended or unauthorized actions. The sudden rise of information security threats coupled with stringent regulatory mandates has highlighted the importance of adopting DLP practices and tools. A world-class DLP solution can protect sensitive data no matter where it's stored, how it's used, or how it's transmitted.
Scan files for sensitive information, such as credit card details, SSNs, and tax IDs, using predefined rules. Regular data risk assessments help you identify risky sensitive data entering your file systems. Classify files based on the sensitivity of their content using tags such as public, internal, confidential, or restricted—determined by matching policies and the assigned risk scores to enable content-aware protection.
Perform file analysis to determine the ownership of sensitive files and revoke permissions that are unnecessarily delegated, when required. Apply the principle of least privilege to reduce the risk of data breaches. Additionally, identify and remove duplicate, outdated, and unused files containing sensitive data to streamline your storage infrastructure.
Strengthen your data security efforts with a defense-in-depth approach by automating the detection of and responses to potential threats within your organization. Automated remediation measures, such as isolating compromised machines from the network, shutting them down, or logging out of compromised user sessions, can effectively contain and neutralize threats in real time.
Use a robust compliance auditing tool to maintain governance over sensitive data in accordance with regulations like the GDPR, the PCI DSS, and SOX. Use third-party tools that offer detailed auditing and reporting capabilities, allowing you to review sensitive files and gain in-depth insights through comprehensive reports.
Gain full visibility into how data traverses across endpoints such as desktops, laptops, and mobile devices. Track file sharing, USB activity, and local file modifications to detect suspicious behavior. Implement policies to block unauthorized actions, generate real-time alerts, and maintain detailed audit trails.
Protect against unauthorized transfers of sensitive data by enforcing strict security policies. Monitor browser activity and inspect content before it's uploaded to cloud storage, personal drives, and other third-party platforms. Leverage data loss prevention tools to block high-risk uploads, USB transfers, emails, and more to ensure compliance with data protection standards.
Regularly simulate ransomware attacks to test your defenses and identify potential gaps before real attackers exploit them. Conduct tabletop exercises and red team drills to assess response strategies using realistic scenarios. Leverage data maps to trace the impact and recovery timelines and measure how quickly you can detect affected data, revoke access, and restore data from backups.
Protect sensitive data by ensuring it's encrypted both at rest and in transit. Encryption at rest safeguards stored data from unauthorized access, while encryption in transit secures data as it moves across networks.
Promote a culture of security awareness by training teams on best practices for handling sensitive data. Use real-world examples like overly permissive access or exposed public links to highlight risks. Tailor training based on roles to make it relevant and actionable. Empower data owners and developers to take responsibility for securing the data they manage.
Build and continuously update a centralized inventory of all sensitive data across cloud, on-premises, and SaaS environments. Ensure visibility down to granular levels, such as file, field, user access, and data lineage levels, so you can trace exactly where sensitive data lives, who accesses it, and how it's used or shared.
