Both the honest mistake and the insider threat use the same exit route, making it hard to determine which is which at the control layer. Routine emails, forwarded threads, and attachments sent under time pressure may contain sensitive information. One PeerJ Computer Science review notes insider attacks were reported as the most common attack type in 2017 at around 60%, and it cites 1,154 real insider incidents reported by the U.S. Secret Service and CERT, spanning categories like fraud and theft.
Data loss prevention (DLP) looks for sensitive information in these outgoing messages and files, validates where the communication is going, and then enforces policy by taking measures like blocking, quarantining, or alerting. That avoids the classic mistake of sending something to the wrong person before it can be reported. DLP therefore acts as a single set of controls that takes care of both concerns because the system is based on data mobility and policy, not guessing what people want.
At the control layer, honest mistakes and insider threats often appear the same because both show up as identical observable behaviors. The reality is that intent is undetectable by most technological telemetry, allowing a hurried employee who mistakenly sends an attachment to produce identical signals as a malicious insider covertly exfiltrating data.
An International Journal of Multidisciplinary and Innovative Research paper on enhanced DLP frameworks argues that traditional DLP tools “rely on static rule-based mechanisms and keyword-based detection,” which limits their ability to catch subtle insider behavior and also causes noise that can “generate a high number of false positives, overwhelming security teams.”
When people are under a lot of stress at work, the overlap gets worse since multitasking and cognitive overload can lead to quick outbound sends and policy slips that look like stealing on purpose. Traditional controls rely on thresholds and anomaly detection like strange recipient domains, and repetitive access to sensitive folders. Analyzing can only show what happened, not why it happened, which leads to false positives and weak blocking decisions.
The HIPAA Security Rule requires regulated businesses to implement technical measures to protect ePHI, such as ensuring that ePHI is not accessed by anyone who shouldn't have it when it is sent over an electronic communications network. A CTS paper explains, “DLP comprises a suite of technology components that can control what data are allowed to leave a specific computer.”
It detects sensitive information in outbound messages and files, then enforces policy through blocking, quarantining, or alerting at the moment risk occurs.
DLP can be looked at through three practical questions:
Email DLP makes such questions real for outgoing communications by scanning them and their attachments, verifying the risk of the destination, and enforcing rules by taking actions like quarantining, blocking, or alerting. Platforms like Paubox for HIPAA compliant email offer a multi-dimensional inspection of the body, sender, recipients, subject, attachments, and headers. They also find sensitive data hidden in PDFs or forwarded reply chains while having administrative review workflows that let teams stop data leaks without delaying care delivery.
DLP cuts down on honest mistakes in healthcare by adding automated guardrails to the same channels that workers use every day. Before data leaves the environment, outbound content and attachments are scanned for sensitive elements, the destination is checked (internal vs. external, unusual domains, unexpected recipients), and policy enforcement takes place.
DLP is a technology that controls what data can leave an endpoint or cross the network. It also works well with policy options like only allowing egress if content passes a scan for PHI elements. The CTS paper offers practical options like “Allow removal of data only if it passes a scan of the data (e.g., for PHI data elements)”, requiring user acknowledgment, or requiring administrator permission, and it notes that “any attempted data movement is also logged for future audit.”
DLP helps reduce the risk of insider threats by treating data transfer as a concern, even when the user has the right to view it. DLP checks outgoing channels like email, online uploads, and file copy routes and enforces policy when sensitive content tries to depart. This is beneficial since insiders can utilize routine procedures to transport PHI off-network.
The paper Human Factors in Electronic Health Records Cybersecurity Breach: An Exploratory Analysis EHR breach drivers supports the urgency of controlling everyday human-driven pathways: “The mean number of records affected by a breach due to unintentional insider threats is more than twice that of breaches caused by malicious intent.”
Audit trails strengthen deterrence and accountability, but DLP still works best as part of a layered approach, since “There is no one holy grail of countermeasures sufficient to prevent human risks that lead to cyberattacks.”
See also: Paubox launches generative AI email security for healthcare
DLP inspects outbound channels (email, attachments, web uploads, file transfer routes) and enforces policy before data leaves, which reduces unauthorized disclosure risk during transmission.
They map to Audit Controls at 45 CFR § 164.312(b), which requires mechanisms to record and examine activity in information systems containing or using ePHI.
Risk analysis drives which protections are implemented and how strict they are.