By Anastasios Arampatzis

Working together today often means working globally in the cloud, with flexible work arrangements that include hybrid and remote work. No longer restricted to the four walls of an organization, employees now have the flexibility to collaborate and uphold productivity across the digital landscape. However, with this increased adaptability comes an even greater need for robust data security and the proper data loss prevention (DLP) solution.

The evolving nature of DLP adoption

Data protection is crucial for any organization, and a reliable DLP (Data Loss Prevention) solution plays a significant role in safeguarding the company's data assets. It not only helps in complying with regulations but also prevents sensitive data from leaking out. To implement a successful DLP strategy, it is necessary to take a comprehensive approach that considers people, processes, and technology. With the increasing complexity of modern data environments, where data is often stored in the cloud, DLP has become a critical component of any data protection strategy.

In response to the changing data environment, businesses are moving from on-premises DLP solutions toward the cloud. At the same time, these businesses face adoption barriers like uncertainty, cost, perceived complexity, impact on productivity, and employee education. The same survey found that companies who are more proactive in their approach to DLP are better prepared to mitigate the effects on productivity. Companies with a more evolved DLP adoption status strongly focus on employee education and processes that better align with a holistic approach to DLP and data security.

The DLP value to business success can be understood from the following abstract by Forrester: "Today, data isn't only IP and regulated data such as personal data, cardholder data, and healthcare information, but also operational data, and data about your data, business processes, and more. The value derived from this data is what differentiates your firm's products and services, and it requires protection from both internal and external threats."

DLP future adoption trends

As businesses, data, and threats evolve, the technology behind data loss prevention moves forward to mitigate further the risk of managing confidential data. According to McKinsey research, the following are the three trends shaping the DLP future.

Behavior analysis and contextual analytics

Many organizations with access to large data sets and solid machine-learning capabilities have already started to use contextual heuristics to identify, flag, and characterize potentially suspicious activity. These heuristics include log-in time, user behavior, and mouse movements. Organizations must have enough telemetry to collect data across the tech stack and advanced behavioral analytics tools to identify abnormal behaviors and infer contextual information such as intent, secondary actors, and root causes to implement this capability.

The contextual information obtained can be integrated into DLP solutions and policy decision points in identity and access management (IAM) tools to determine user access to data. It can also help in incident forensics and targeted enforcement of access controls. For instance, if an incident is detected, an organization's DLP system can automatically revoke access for the suspected actor and any inferred secondary actors.

Privacy compliance integration

Combining typical data management capabilities, such as classification and rule-based access enforcement, with solutions that proactively prevent compliance violations has become increasingly popular. This integration is beneficial in cases where data transfers contain personally identifiable information (PII) of EU citizens. Businesses can use automated and manual data tagging to comply with relevant regulations in a specific region. DLP solutions can automate enforcement and notification based on data tags to control data transfers.

In addition, DLP solutions can be integrated with technology that automatically generates dashboards and reports for audits to increase transparency and reduce the compliance burden. These reports can show where relevant PII is located, the compliance enforcement mechanisms, and any violations. To implement this capability, organizations must have automated compliance solutions that can be integrated with their data governance and classification tools. However, this requirement is particularly challenging for global organizations, especially in regions where regulations continue to evolve.

Protection of audio data

The combination of advances in natural language processing, voice recognition, and AI-based text-to-speech technology has made it possible for organizations to implement DLP to protect audio data. In addition to using OCR and expression matching to detect keywords and patterns in text documents, organizations can broaden the scope of their data protection measures by analyzing audio and video files. In some industries and scenarios involving highly sensitive data, these controls can be extended to identify leakage of restricted data during live conversations, such as those in boardrooms or over selected phone lines.

Despite the benefits of implementing audio inspection, many organizations are hesitant because employees and clients may perceive it as intrusive. Therefore, companies should carefully consider the potential impact on their culture before deciding whether to implement these capabilities. If the risk is deemed high enough, audio inspection should be integrated, but with clear communication to employees on why it is essential and how it works. Implementation should also be targeted and risk-based to avoid compromising employee trust.

Conclusion

Tightening regulations, an ever-evolving cyber threat landscape, and increasingly complex data management have made it necessary for organizations to have comprehensive transparency into the location and flow of important data. This is essential to protect themselves against breaches and regulatory fines. To achieve this, organizations are enhancing their DLP capabilities.

The future of data protection relies on integrating advanced analytics, machine learning, and contextual heuristics with privacy and reporting solutions. For the most high-risk areas, advanced applications, such as audio-data exfiltration tools, can be added for additional protection.

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ABOUT THE AUTHOR

Anastasios Arampatzis is a retired Hellenic Air Force officer with over 20 years' worth of experience in managing IT projects and evaluating cybersecurity. During his service in the Armed Forces, he was assigned to various key positions in national, NATO and EU headquarters and has been honoured by numerous high-ranking officers for his expertise and professionalism. He was nominated as a certified NATO evaluator for information security. 
 
Anastasios' interests include among others cybersecurity policy and governance, ICS and IoT security, encryption, and certificates management. He is also exploring the human side of cybersecurity - the psychology of security, public education, organizational training programs, and the effect of biases (cultural, heuristic and cognitive) in applying cybersecurity policies and integrating technology into learning. He is intrigued by new challenges, open-minded and flexible. 
 
Currently, he works as a cybersecurity content writer for Bora Design. Tassos is a member of the non-profit organization Homo Digitalis.