Author : Amjad AbdulRahman, Commercial and Technical Director, SCOPE Middle East
For more than two decades, we have worked with organizations across the region, supporting the implementation and operation of information security solutions. Yet many companies still ask us how to use Data Loss Prevention (DLP) systems in the most effective way.
In this article, we explain what a “basic capabilities” list looks like for a traditional DLP solution and what you should expect from a “premium set of capabilities” in a next-generation DLP platform.
The Evolution of DLP
Sometimes organizations don’t have the necessary expertise to pick a modern DLP and choose so-called “legacy” solutions for deployment. These traditional systems mainly focus on blocking specific data transfer channels, such as email or USB drives. However, this approach is no longer sufficient.
The rapid development of information technologies and the proliferation of threats have fundamentally changed requirements for a DLP solution. The discussion has moved from pure technical controls to complex business risk mitigation. Today, security teams are asking less frequently for “DLP to block leaks” and more often for “solutions to manage internal risks.” Leading analysts such as Gartner now categorize DLP within broader Insider Risk Management platforms.
DLP is more than just blocking
In practice, security teams expect a modern DLP system to do far more than block a few communication channels. It must control business collaboration tools, cloud services for storage and content creation, AI services, and many other business applications used daily across organizations.
Next-generation solutions identify vulnerabilities within business processes, such as fraud risks, policy violations, or operational mismanagement. A clear example of this approach is provided by the SearchInform team, which combines traditional data protection with tools designed to detect internal risks within a unified platform.
Data Discovery and Classification
One of the major challenges for security teams is that the information is no longer stored in a single centralized repository. Instead, it is distributed across on-premise systems, cloud storage, SaaS platforms, and corporate workstations. Enterprises store 62% of their data in the cloud, according to Forrester survey.
Next-Gen DLP platforms include built-in capabilities to:
- Automatically discover sensitive data
- Analyze file content and classify a document
- Identify “shadow data” that is stored outside of security controls
- Enforce protection policies regardless of storage location
Effective protection today depends not only on monitoring how data moves, but on understanding the content of that data and how sensitive it is.
AI Usage Control
Artificial intelligence has rapidly become part of everyday work. 75% of Middle East employees have used AI in their jobs in 2025, according to PwC survey. Employees use AI tools to draft emails, summarize documents, write code, and analyze data. AI significantly increases productivity, but it also introduces new risks.
For example:
- Confidential data may be uploaded into public AI services. Over time, sensitive information from these interactions could be used as training data and may subsequently appear in responses provided to other users of the service.
- Sensitive text can be paraphrased to bypass keyword-based security policies, effectively allowing users to evade traditional word-based controls.
- AI agents can become new channels for data exfiltration. Autonomous AI agents, which became popular recently, can collect data and upload it to storage accessible to external adversaries.
Legacy DLP solutions, built primarily around keyword detection, struggle to address these challenges.
In 2023, Samsung temporarily restricted the use of generative AI tools after confidential source code was exposed through employee interactions with an AI service. The incident highlighted how easily confidential information can leave the organization through seemingly productive workflows.
Modern DLP platforms, however:
- Monitor interaction with AI services
- Analyze context rather than only keywords
- Detect attempts to bypass security controls
Control over AI usage is quickly becoming a mandatory element of enterprise security strategy.
Watermarking
Beyond AI governance, next-generation platforms also introduce proactive deterrence mechanisms. In mature organizations, watermarking serves as both a traceability mechanism and a behavioral deterrent.
People are far less likely to attempt data theft if they know that files are protected with unique identifiers and that the security team can quickly determine the source of an incident. Imagine an employee takes a photo of a sensitive file displayed on their screen and leaks it, exposing confidential data. Later, a security specialist can analyze the image in a DLP system. A hidden on-screen watermark can then reveal key details about the workstation and help identify the user who was working with the file.
As a result, watermarking not only enables companies to trace violators but also serves as a powerful tool for proactive data protection.
Additional Value of DLP
Legacy DLP solutions generate security alerts. Next-Gen DLP platforms produce structured intelligence across the organization. Many departments can benefit from these insights, helping to improve the efficiency of business processes.
- IT teams can use application usage reports to address shadow IT and optimize spending on licenses and SaaS subscriptions.
- HR departments can detect excessive use of social media or YouTube during working hours and monitor potential internal conflicts.
- Department managers gain access to objective data that supports informed employee development and promotion decisions while helping ensure a balanced workload across the team. Moreover, a DLP solution can assist in monitoring communication quality and investigating client complaints.
- Compliance teams can ensure compliance with regulatory requirements and provide reports to relevant authorities. For instance, in March 2025, the Saudi Central Bank banned the use of WhatsApp for customer service for Saudi banks. With a DLP system in place, the compliance team can monitor WhatsApp usage across the company, prohibit its use entirely, or restrict the ability to attach files containing personal data or share confidential records in chats.
As a result, a DLP system enables better decision-making across the enterprise and becomes part of the organization’s governance framework.
DLP: Practical Takeaway
Businesses no longer want five disconnected systems — one for data leak prevention, one for activity monitoring, one for data classification, one for AI governance, and another for analytics. Unified platforms simplify both deployment and day-to-day operations, allowing security teams to manage protection from a single console instead of switching between multiple solutions.
Today, businesses need more from DLP than blocking data transfers. They need to know where sensitive data is stored, who works with it, how employees use AI, and where real risks appear. This is the level of functionality companies should expect from a modern solution.
A next-generation DLP system brings these capabilities together in a single platform and provides security teams with practical tools to protect confidential data.