Above Security raises $50M to redefine insider risk in the age of AI agents
Read more
Above Security
Apr 22, 2026
Above Security
Apr 22, 2026
People are messy. Every day, we forget things. From leaving the house keys on the kitchen counter to that appointment that never made it onto the official calendar, we make mistakes that make our lives more difficult. While we might not maliciously skip that meeting and keep someone waiting, our actions have consequences that we can’t always predict.
In business, these human mistakes can leak sensitive data. When a developer vibe codes, they can leak sensitive proprietary code to a public database. When an employee clicks on a malicious link in a phishing email, they can share credentials that compromise the organization. People are overwhelmed and try to do their best at protecting sensitive data, but humans make mistakes.
Most data loss prevention (DLP) tools focus on information, classifying sensitive data and tracking where it goes. However, by focusing on the data, they often fail to identify the mistakes that lead to true data leakage and loss.
By understanding what data loss prevention is, organizations can find the solutions, like insider threat monitoring, that fill in the gaps of what data loss prevention isn’t.
Data loss prevention (DLP) is the set of strategies, processes, and technologies that organizations use to prevent data loss, misuse, or unauthorized access. Organizations use DLP technologies and processes to mitigate data protection risks arising from generating, processing and sharing information so that they can maintain regulatory compliance and secure sensitive information, like personally identifiable information (PII) or trade secrets.
A typical DLP system uses multiple tools to monitor user activity and data flow across the organization’s network, endpoints, and cloud services. A DLP system often inspects content to identify sensitive data patterns, like:
This recognition technology enables the DLP system to identify potential data leakage or loss that can include:
Organizations use a DLP system to enforce policies that protect sensitive information and mitigate data exfiltration and leakage risks.
People categorize data loss prevention systems around where they monitor and protect data, typically dividing them into the three categories: endpoint DLP, network DLP, and cloud DLP. While these technologies provide a level of visibility at the data layer, they often fail to connect user actions to the data being move and fail to understand realistic user behavior.
Endpoint DLP solutions are agents on individual user devices, like laptops and mobile devices. They seek to monitor data activity on the device when a user creates, edits, copies, pastes, or prints it, including when uploading it to cloud storage or transferring it to a USB drive.
Endpoint DLP fails to address several critical use cases, including:
Network DLP solutions monitor data-in-motion as it travels across the organization's network by inspecting traffic as it moves through email, web gateways, and other network channels. They use predefined policies and patterns to detect and block sensitive information leaving the corporate environment.
Network DLP fails to address several critical use cases, including:
Cloud DLP focuses on managing sensitive data in approved cloud environments and public cloud storage like Google Cloud Storage or Amazon S3 buckets. These technologies apply policies that identify, classify, and protect sensitive information by scanning data-at-rest and data-in-use.
Cloud DLP fails to address several critical use cases, including:
A traditional DLP system typically relies on a multi-stage process to understand, protect, and monitor sensitive data. Problematically, by focusing on technological processes and not people’s behavior, the DLP stages do not account for human error or malicious human behavior that impact data, leaving organizations open to otherwise undetected data loss risks. These stages are based on a technological process - think 1’s and 0’s rather than human speak.
The data discovery and classification process starts by locating all data repositories, including the ones on-premises and in the cloud. It classifies the data based on content and sensitivity, relying on patterns and automation that might include:
Many organizations struggle to appropriately discovery and classify data, meaning that these traditional DLP solutions overwhelm security teams with noise false alerts that lead to failed threat detection.
The organization creates security policies that define how people should handle sensitive data. They allow or deny different types of activities and operational rules that govern data movement.
DLP monitors data across:
The DLP system triggers an alert when it detects a security policy violation, then it initiates predefined enforcement action, like blocking the action or encrypting sensitive data.
When they detect an event, DLP systems generate detailed logs and reports that identify the user, the affected data, and the incident’s time and location. Security teams use this forensic data during incident response investigations.
Data loss prevention is a continuous process that responds to changing business operations, data types, and threat landscapes. Organizations regularly update their detection engines, seeking to reduce false positives and negatives.
Often, people are the primary cause of data loss.
Most employees aren’t malicious, but they do make mistake, posing a threat through negligence or unintentional actions that can include:
External threats are traditional cyber attacks. Security teams typically use intrusion detection systems, antivirus, and firewalls to mitigate these risks.
Malicious insiders are individuals who intentionally misuse their authorized access to steal, alter, or destroy sensitive data for personal gain, revenge, or to cause harm to the organization.
Whether distracted by personal issues or rushing to meet deadlines, people may not always be thinking about data loss. Realistically, most people go to work and do the best they can. While they usually mean well, data leakage and loss is not usually at the forefront of their daily activities, often not recognizing that their actions could lead to data loss.
Data loss prevention solutions often fail to consider the human side of working with information, focusing instead on the data classification. This creates various problems, especially across distributed and remote work environments.
Like it says in the name, DLP is built around knowing your organization’s sensitive data, where you store it, and where people use it. DLP policies are reactive data security controls that rely on static definitions of sensitive data while failing to consider how people use data. By the time it identifies a potential data breach or loss event, the user has already taken the risky action.
Unfortunately, by relying on data classification, DLP fails to provide warnings before peopleengage in risky behaviors, purposefully or accidentally.
Data classification is only one component that should drive data loss risk management. The data itself is not risky. People’s interactions with data create the risk. Security teams need to correlate data sensitivity with human data use to truly understand the risks that people’s actions can create.
User and Entity Behavior Analytics (UEBA) use rules that define a baseline for normal data use and look for abnormal activity. UEBA applies rules around:
Problematically, UEBA’s rules rarely provide insight into whypeople access data. Despite filling in a DLP gap, it still takes a technology-focused approach to a human problem, failing to address human intent.
Whether malicious or accidental, insider threats use approved and authenticated access to data. Without understanding the user’s intent, the UEBA technology can trigger high volumes of false positives, especially when people have remote access so they can work around their personal schedules. A risky login at 10pm can be someone trying to steal information or a parent working after putting a child to bed. Intent matters, but most UEBA fails to capture this information.
Data loss prevention solutions will provide context at the data protection layer so that organizations can build alerts and rules that correlate telemetry about data, systems, user roles, and business contexts.
However, they fail to provide context and visibility around user intent and behavior.
Data doesn’t move on its own. People access, move, and manipulate data. While data may be sensitive, people’s actions are the real risk. To mitigate data breach risks arising from insider threats, organizations need technologies that correlate user actions across various exposure points.
For example, correlating a user’s internet search about a competitor while sharing confidential information about a company’s product in a generative AI tool could be:
Flagging both activities as policy violations only adds more noisy security alerts to an already loud security operations environment. While DLP can block the data sharing, it provides no context about users or their intentions. Security teams need to understand user intent before people move data by capturing information about searches, behavior patterns, and early signals.
DLP solutions remain tied to events, making them reactive to defined data interactions. Unfortunately, many organizations struggle with unmanaged technologies. While organizations can control the applications that users download to corporate devices, many applications have a browser-based version. When banned from downloading the software, many people use these web versions as a workaround.
Problematically, only tracking data interactions means that organizations lose control over how people share information with these browser-based tools.
On their own, many unmanaged browser-based applications or extensions are not inherently risky. The risk arising when people share sensitive information with them, creating a data leak.
Security teams need solutions that distinguish accidental and malicious sensitive data sharing. They need solutions that gently guide users away from accidental data leakages while collecting forensic evidence about malicious data usage.
Even the best DLP solutions fail to address the fundamental insider threat and data security risk: human behavior and intent. Most people are just out in the world, doing their best to complete their job functions. When they accidentally share confidential information, personal data, or customer data, they rarely mean to harm an organization. While less prevalent, malicious insiders know the organization’s security controls and work to avoid them. In both cases, tracking data use alone fails to mitigate risk.
Security teams need more than data loss prevention software; they need insight into why users interact with data so they can truly understand risk. Above Security brings together the series of events spanning different platforms so that security teams have the context necessary to understand user behavior.
Our solution unifies telemetry into a single investigation across the larger behavioral surface, including SaaS, clipboard, 0Auth, and extensions so that teams can understand intent and expose real risk. Above Security connects behavior, permissions, and conversation context into one clear, human-readable picture to mitigate accidental and malicious insider threat risks without interrupting users’ legitimate work.
To see how Above Security’s platform changes the DLP game, book a demo today.
%201.png)
