With the migration of vast data to cloud platforms, the overall security landscape of the network has changed. The traditional concept of creating a fortress around on-premise servers is no longer viable, considering the free movement of data between endpoints, cloud applications, and other cloud-based services. This has given Cloud-Based DLP its place of importance, moving from being just another useful tool to becoming an essential component of the overall security infrastructure.
In the case of companies handling sensitive data, such as those in the financial sector, the consequences of failure are catastrophic, with companies being subject to massive financial penalties for even the slightest slip in data security. Cloud-Based DLP presents companies with a solution that can grow with the threat landscape, providing the necessary tools to traverse the complex digital world.
Understanding the Regulatory Pressure Cooker
Regulatory authorities worldwide have tightened their grip on data security and privacy standards. Companies are caught in the middle of a regulatory minefield, with data security and personal/financial data protection being of paramount importance. The introduction of the General Data Protection Regulation (GDPR) has set the new standard for data security, requiring companies to ensure the protection of personal data of EU citizens, irrespective of the location of data processing.
The Payment Card Industry Data Security Standard (PCI DSS) requires companies to maintain a secure environment if they process, store, or transmit credit card data. In the banking sector, Basel III has introduced stringent risk management practices, which indirectly require companies to maintain robust data governance practices to ensure correct risk reporting.
Mitigating Data Leaks and Unauthorized Access
Cloud-Based DLP helps to mitigate risks by offering detailed visibility into the location and use of sensitive data. Unlike traditional systems that may not effectively monitor cloud traffic, Cloud-Based DLP solutions are native to the cloud and integrate directly with cloud applications using APIs. They are then able to scan data in motion and at rest and detect sensitive data such as credit card numbers, social security numbers, and intellectual property.
After sensitive data has been identified, security policies may then be implemented. For example, if an employee wants to download a file containing customer financial data onto an unmanaged personal device, Cloud-Based DLP may prevent this from happening. It may also encrypt files before leaving the network, or even remove specific pieces of sensitive information from a document. Such measures prevent data leaks from happening as a result of human error, as well as from malicious intent by cybercriminals.
Best Practices for Institutional Scale
To maintain constant compliance with regulations and standards, organizations should implement strategies that go beyond initial deployment. They should see compliance as an ongoing process, as opposed to a one-time activity. The first step in this direction should be data classification. As stated earlier, you cannot protect what you do not know exists. Therefore, automated tools should always be used to classify sensitive data in the cloud. Solutions like a data loss prevention SaaS are also incredibly valuable.
Another best practice is the implementation of unified policy management. As the organization grows, it becomes impossible to manage different policies for different apps and platforms. A centralized cloud-based DLP solution provides security teams with the ability to create a policy once, such as “block all sharing of credit card info externally,” and then apply it across email, cloud storage, and collaboration tools at the same time.
The Future of Automated Risk Management
The direction that regulatory technology is heading is toward more automation. This is because, with more data being created every day, compliance with more intricate regulations will be difficult to achieve manually. The way forward is therefore the use of automated risk management, where Cloud-Based DLP uses machine learning to predict security breaches before they even happen. This means that organizations that embrace this technology will be securing their future, not only their present, against the changing landscape of compliance.