Data Loss Prevention detects and blocks sensitive information leaving approved systems, reducing breach impact when paired with access controls and monitoring.
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Data Loss Prevention (DLP) encompasses technologies and policies designed to detect and prevent unauthorized transmission or exposure of sensitive data outside an organization’s controlled environment. It monitors data in use, in motion, and at rest to block or alert on actions like copying, emailing, or uploading confidential information. DLP aims to reduce risks from insider threats, accidental leaks, or external attackers exploiting compromised accounts.
Effective DLP requires accurate data classification, contextual analysis of user behavior, and integration with access controls to minimize false positives and operational disruption. It is most relevant for protecting intellectual property, personal data subject to privacy regulations, and critical business information. While DLP cannot stop all data exfiltration attempts, it strengthens defense-in-depth by enforcing data handling policies and supporting forensic investigation of suspicious activities.
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The first wave of enterprise AI concern was straightforward. It was simply employees pasting sensitive data into public AI tools. Security teams responded with usage policies, domain blocks, and data loss prevention rules. That response made sense at the time
According to the new Browser Security Report 2025, security leaders are discovering that most identity, SaaS, and AI-related risks converge in a single place, the user’s browser. Yet traditional controls like DLP, EDR, and SSE still operate one layer too low
Traditional data leakage prevention (DLP) tools aren't keeping pace with the realities of how modern businesses use SaaS applications
In the modern enterprise, data security is often discussed using a complex lexicon of acronyms—DLP, DDR, DSPM, and many others. While these acronyms represent critical frameworks, architectures, and tools for protecting sensitive information, they can also overwhelm those trying to piece together an effective security strategy. This article aims to demystify some of the most important acronyms
As the shift of IT infrastructure to cloud-based solutions celebrates its 10-year anniversary, it becomes clear that traditional on-premises approaches to data security are becoming obsolete. Rather than protecting the endpoint, DLP solutions need to refocus their efforts to where corporate data resides - in the browser
ChatGPT has transformed the way businesses generate textual content, which can potentially result in a quantum leap in productivity. However, Generative AI innovation also introduces a new dimension of data exposure risk, when employees inadvertently type or paste sensitive business data into ChatGPT, or similar applications. DLP solutions, the go-to solution for similar challenges, are
A cyberespionage actor known as Tick has been attributed with high confidence to a compromise of an East Asian data-loss prevention (DLP) company that caters to government and military entities