Most DLP tools fail for a simple reason: they’re built to look at a single aspect of data loss. Sensitive data leaks out of organizations for three main reasons: human error, insider risk, and external attackers. Each behaves differently, requires a different approach to recognize and solve, and has a different frequency vs. impact curve.
A policy tuned for accidental sharing will miss slow insider exfiltration, while controls hardened for external attackers will frustrate employees. The only durable path is to detect intent – why the action is happening, then respond accordingly.
This article will break down the three hazards of data loss, map them on the frequency/impact axis, share real-world examples, and highlight the contextual intent signals (who’s acting, what’s the data, where it’s going, and how behavior deviates from normal) that separate harmless mistakes from real threats.
Human Error: Common but Manageable
Human error sits at the “frequent but lower impact” end of our frequency vs. impact curve.
It’s by far the most common cause of data loss – employees accidentally emailing the wrong file, sharing the wrong Google Drive folder with the wrong people, or consulting GPT about a sensitive matter. Most of these incidents are caught quickly and cause less damage compared to insider abuse or targeted attacks.
That being said, these incidents are extremely common, and the potential damage they cause can fluctuate quite widely.
If we look at a simple example, all it takes is a sales manager accidentally sharing a Google Drive folder with “Anyone with the link” instead of restricting it to the client team, for sensitive pricing data to be exposed. This will rarely result in malicious exploitation, but when it does, the consequences can range from mild to disastrous.
In July 2025, TalentHook, a recruitment software firm, inadvertently left an Azure Blob storage container misconfigured, exposing nearly 26 million resumes. The breach revealed sensitive personal information, including names, email addresses, phone numbers, and educational and employment histories.
Traditional DLP policies are often meant to block or flag such mistakes, but they do so bluntly by either flooding security teams with false positives or interrupting legitimate workflows.
A modern approach should therefore infer user intent: They should analyze context (Was this file ever shared externally before? Does this domain look like a partner or not?) and nudge the user in real-time, resulting in fewer breaches and fewer frustrated employees.
AI Agents are Changing the Picture
Another thing to consider here is AI agents – Just like human employees, they can make mistakes, but at a much larger speed and scale. An AI assistant given broad access to corporate systems might accidentally share the wrong files, misroute sensitive information, or expose data in the process of answering a seemingly benign request. What makes this especially dangerous is volume: where a human might misconfigure a single folder, an AI could replicate the same mistake across thousands of records in seconds.
Insider Risk: Less Frequent, More Damaging
Insider risk sits in the middle of the frequency vs. impact curve – less common than human error, but far more damaging when it happens. Unlike accidents, these incidents often involve intent: an employee misusing legitimate access to steal, leak, or sabotage sensitive data.
In late 2024, a new staffer at Toronto-Dominion Bank hired to detect money laundering used her access to leak sensitive customer data to criminals via Telegram. Prosecutors reported that her phone contained images of 255 customer checks and personal information for 70 additional clients, and what began as trusted data access inside a bank turned into a direct pipeline for fraud.
This case shows how difficult insider risk is to contain. On the surface, the employee’s activity of accessing customer records was within her role. Traditional DLP rules like “large downloads = suspicious” or “sensitive files sent externally = block” wouldn’t have caught this, because the behavior didn’t break those patterns until it was too late.
A real solution for the problem can only be found in behavioral analytics that learn what “normal” looks like for each role and user. Context matters:
- Is this employee suddenly accessing 10× more customer data than usual?
- Is data being copied at odd hours or just before the employee leaves the company?
- Are peers in the same department performing the same operations?
Malicious Actors: Rare, Catastrophic
At the far end of the frequency vs. impact curve sit external attackers — the least common hazard, but by far the most destructive. These are the ransomware groups, cybercriminals, and state-sponsored attackers whose goal is to extract maximum value from your most sensitive data. Unlike human error or insider misuse, their tactics are deliberate, well-resourced, and often devastating.
An example of one such case was the attack on Change Healthcare in January 2025, when ransomware operators disrupted services across U.S. hospitals for weeks, demanding a $22 million ransom. The breach not only exposed data but also crippled operations, caused patient care issues, and inflicted massive reputational damage.
Traditional DLP policies are rarely effective in these scenarios. Once attackers penetrate the perimeter, they often blend into legitimate traffic, moving data in ways that mimic normal business processes. Relying on static rules (“block uploads over 50MB” or “flag external domains”) either misses the exfiltration or floods analysts with noise.
The only viable defense in these cases, from a DLP standpoint, is real-time, intent-aware detection: spotting unusual data flows, recognizing when files are headed to suspicious destinations, and correlating this with attacker-like behaviors (use of admin accounts at odd hours or from odd locations, mass compression of files, or lateral movement across systems). In other words, security teams need the same behavioral context used for insider risk — but tuned for external actors who adapt quickly.
What Can We Do? Unified and Intelligent DLP
The real problem with most DLP tools isn’t that they’re useless. It’s that they’re incomplete. One solution is designed to prevent accidental sharing. Another tries to catch insider misuse. A third focuses on malware and exfiltration. Each may work in isolation, but together they leave significant blind spots behind. Attackers, as well as well-meaning employees, exploit those gaps every day.
The only durable way forward is a unified approach that understands intent. That means:
- Contextual intelligence that knows the difference between an employee sending a customer contract to the wrong inbox vs. an engineer siphoning code to a personal repo.
- Real-time prevention that can stop an exfiltration attempt in the moment, not weeks later in an audit log.
- Adaptive, AI-driven learning that continuously tunes itself to normal behavior, supporting employees instead of blocking them with rigid policies.
We must move from a one-dimensional filter to a living system that interprets why something is happening, not just what.
Ask yourself this: Does our current DLP strategy actually cover all three hazards: human error, insider risk, and malicious actors, or are we betting everything on just one?
An organization that answers this question honestly and builds for intent will be the one that prevents tomorrow’s data losses, not just react to them.
