While AI is transforming the landscape of cyberattacks and defenses, the primary focus remains on data security. In a world where information is shared across various SaaS platforms, personal devices, and remote endpoints, stopping data leakage is essential for maintaining company performance.
Data Loss Prevention (DLP) has become vital for building digital trust as businesses automate more tasks and employees rely on AI in their daily activities. New DLP solutions must offer enhanced features and additional context to integrate effectively with wider access and identity policies, aiming to protect and secure business assets.
Vice President of Cybersecurity Sales and Service for Hughes Network Systems.
The Need for DLP is Evolving in Your Business
Employees sometimes engage in actions they shouldn’t, and this persists as a challenge for DLP initiatives. The growing use of content-driven tools like ChatGPT and greater dependence on cloud storage—such as Google Drive and Dropbox—afford more chances for staff to mishandle sensitive customer data.
In addition to company-sanctioned tools, the 2025 Netskope Cloud and Threat Report noted that 88% of employees are utilizing personal cloud applications at work. The existence of Shadow IT, where staff use SaaS applications without IT’s awareness, can expose your organization to a variety of risks from undisclosed apps.
Even approved use of large language models (LLMs) is often hindered by inadequate governance means employees might unintentionally cross data boundaries. Businesses face difficulties in detecting and curbing data leakage due to limited visibility into what LLMs are accessing, what data is being stored, where the outputs are sent, and who is utilizing them. Regrettably, traditional DLP solutions frequently lack the capability to monitor and regulate AI-based SaaS applications, leaving potentially sensitive data defenseless.
External Threats Heighten the Importance of DLP
When cybercriminals breach a network, their main objectives are usually to steal sensitive information or encrypt files for ransom. These risks keep businesses on high alert. While efficient ransomware prevention tools can mitigate the latter concern, effective DLP is crucial for defending against the former.
The appropriate DLP platform can stop sensitive information from exiting your network, regardless of how the intruder gained access or where they seek valuable content. Whenever someone attempts to access, modify, or transfer data designated as sensitive, you will be informed about their actions.
Combining DLP with a robust ransomware protection system can protect your sensitive assets from being accessed or moved by external attackers.
Integrations Address DLP Challenges in Current Systems
Certain essential features significantly enhance the effectiveness and usability of DLP solutions. Historically, a lack of integration and poor capabilities to prevent employees from circumventing established security measures have been common complaints.
Modern DLP systems can connect with thousands of web applications commonly used by employees, including email, Slack, Salesforce, Teams, and more. A contemporary DLP solution can also monitor inputs sent to LLMs.
Customizable rules allow you to scan LLM inputs for specific data types, such as credit card information, client records, critical source code, or confidential keywords related to sensitive projects or impending business opportunities. If you can identify it, an integrated DLP can monitor for it and prevent unauthorized actions.
Context and Customization Are Essential for Fast Response and Efficient Alert Management
Not every threat holds the same weight. Leading DLP tools allow flexibility in defining rules and responses tailored to different situations. To optimize results, your security team must first understand and categorize your sensitive data, which can vary from one organization to another. Whether it’s CRM lists, source code, customer information, the choice of what to monitor lies with you.
An advanced DLP can utilize your specific content definitions to guide personalized response actions. For example, if a user attempts to exfiltrate sensitive customer information, your DLP could automatically block that action and notify your security team. Alternatively, a less severe behavior might trigger a prompt offering guidance and reminders to adhere to policies, while an additional authentication step could confirm the action, or sensitive data could be automatically redacted.
Customizing your DLP strategy by selecting automated responses based on threat severity enables quicker and more efficient handling of each incident. By adding relevant context to highlight significant risks, you also lessen the number of non-urgent alerts sent to your security and IT teams, allowing them to focus on more critical issues.
Data loss prevention is essential for companies navigating the era of AI. Effectively managing and safeguarding data traversing the organization necessitates integrated tools that can act as safeguards against risky actions, promote preferred behaviors among users, and apply automated rules, freeing your security and IT teams to tackle pressing threats. Innovative solutions empower security and IT teams to successfully prevent data leakage, even in dynamic environments.
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