It is more important than ever for organizations in all industries to prioritize data security and build up a defense against data breaches and leaks. Many workers in recent years have switched to remote or hybrid working environments, sometimes using their own devices, and companies have also increased online operations on the customer-facing side. This means that sensitive data is more spread out and less easily defended. 

There is a wide variety in the types of security solutions that are designed to protect data in some form or another. Data detection and response (DDR) is an advanced solution for data protection that addresses the development of the digital landscape and accounts for new and sophisticated threats.

Challenges of Traditional Data Security 

In the past, organizations could get along with what is known as the “castle and moat” model of cybersecurity. This means that the enterprise network is defined as the castle, and the security measures in place are the moat, keeping outsiders from infiltrating the organization. The company’s digital assets—like sensitive data, devices, and software—are all mostly assumed to be safe within the castle walls, so long as the defenses are good at keeping out attackers. This was never an extremely effective tactic for preventing attacks, but recent developments in the digital landscape have made the castle and moat model all but obsolete.

With the growth of remote and hybrid working, it no longer makes any sense to attempt to define the perimeter of the organization’s network, much less to defend it. The attack surface is more spread out rather than centralized in one physical location, employees often use their own devices to manage sensitive data, and there is no way to enforce cybersecurity policies for all remote workers. Protecting data and other digital assets requires more than just keeping outsiders out: modern solutions must account for modern threats. Data is at danger from internal actors as well as external ones, intentionally as well as unintentionally, and access to sensitive areas of the network should not be automatically granted to all insiders.

Defining Data Detection and Response (DDR)

Data detection and response (DDR) is “a new generation of data security technology that’s designed to address the long-standing challenges with protecting data.” It is built around a fundamentally different approach to data protection, accounting for crucial risks and advances that traditional security measures could not have considered. Cloud storage and computing, remote and hybrid workers, and artificial intelligence and machine learning (AI/ML) are all making it more and more difficult to defend data against all kinds of threats.

The foundation of DDR is based in a few principles:

• Classifying data based on its lineage.
• Focusing on data in motion.
• Following data everywhere.
• Acting in real time to prevent data loss.

This distinguishes DDR from other security solutions, which focus on data at rest, where it is stored at endpoints, and classifying it based solely on content. These traditional methods—such as endpoint detection and response (EDR), cloud security posture management (CSPM), and data security posture management (DSPM)—are not as effective as DDR at protecting data and preventing exfiltration.

Benefits of DDR

There are a number of reasons that DDR can be a boon for organizations looking to protect their data, especially when used in combination with complementary solutions like data loss prevention (DLP). With a DLP solution in place to proactively prevent data loss with security policies and controls, helping organizations reach compliance requirements for data protection, the addition of a DDR solution can cover angles that are left vulnerable otherwise. The DDR reacts to security incidents in real time or after, focusing on minimizing and mitigating damage, and contributes to maintaining compliance for breach notification.

Some of the other key benefits of implementing DDR include:

• Protecting supply chains against cyberthreats.
• Consolidating features of many other solutions, such as insider risk management (IRM) and DLP.
• Achieving visibility into the full lineage of data and its flow through all assets.
• Mitigating actual data exfiltration events or other cybersecurity incidents in progress.
• Using the content and the context of data to determine its risk profile.
• Forensic investigation and analysis of security incidents.
• Maintaining data integrity throughout its lifecycle.
• Mitigating major data security challenges.

Conclusion

With other tools in place to reduce the vulnerabilities and opportunities for attacks to occur, DDR adds another crucial layer of security by reducing harm in the event of a security incident. It also often has the capability to analyze these incidents, providing organizations with helpful data regarding the risk to their sensitive assets, hopefully to go toward preventing future attacks. Protecting data with DDR is dynamic, adaptive, and accounts for modern and advanced threats that might be designed to circumvent more traditional security measures. Using DDR, organizations can alleviate much of the strain of protecting sensitive data against unauthorized exfiltration.

PJ Bradley is a writer on a wide variety of topics, passionate about learning and helping people above all else. Holding a bachelor’s degree from Oakland University, PJ enjoys using a lifelong desire to understand how things work to write about subjects that inspire interest. Most of PJ’s free time is spent reading and writing. PJ is also a regular writer at Bora.