Data Exfiltration Prevention (DXP)

Data Exfiltration Prevention (DXP)

Stop silent data theft before it leaves your endpoints. Modern data breaches rarely rely on brute force. They rely on stealth. Attackers quietly read sensitive files, compress them, and exfiltrate them over seemingly normal outbound channels. Traditional tools miss this because they look for known signatures, not unusual behavior.

Our Data Exfiltration Engine takes a different approach. It learns how your endpoints typically read, process, and transfer data, then flags anything that steps out of line.

What is data exfiltration?

Data exfiltration is the unauthorized transfer of sensitive information from your environment to an external destination. Left undetected, this “silent theft” can continue for weeks or months before anyone notices.

Why traditional tools miss it

Most legacy security tools focus on known indicators such as malicious hashes, domains, or signatures. While this might work for known attacks, it fails when adversaries:

To stop this, detection must shift from “known bad” to “behavior that doesn’t belong”.

Data Exfiltration prevention in Malware Protection Plus

The behavior detection engine continuously observes processes on every endpoint—tracking file read attempts, monitoring outbound network activity, and learning routine usage patterns unique to your environment. It builds a behavioral fingerprint for each process and uses it to detect unusual or risky data-transfer behavior in real time.

File monitoring

Malware Protection Plus monitors file-read operations across high-risk file categories for critical file types. Every read operation is tied to the process that accessed the file, creating a reliable chain of evidence that correlates with outbound network activity.

Network activity monitoring

This metric involves monitoring outbound network transfers, allowing the engine to pinpoint processes that read data and transmit them.

Behavioral modeling

Our engine profiles process behavior and captures it in a feature snapshot. This feature snapshot defines a baseline of “normal” activity and enables the engine to quickly recognize and flag any deviation from that baseline.

Data collection and training

Instead of rushing through training, the engine builds an accurate baseline by enforcing strict observation standards. This ensures the first model is always trained on a stable, representative dataset:

It is important to note that with Malware Protection Plus, all training happens locally on the endpoint and data doesn't leave the machine. The model is optimized to consume minimal CPU and memory while capturing highly granular behavioral patterns of processes interacting with your data and network endpoints.

Real-time anomaly detection

Once trained, the engine evaluates every new behavioral pattern and generates an anomaly score. If the score crosses a certain threshold, the activity is flagged as a potential data exfiltration attempt. The sample (normal or anomalous) is added to the dataset for future accuracy improvements.

Continuous learning and retraining

Our behavior detection engine automatically retrains every 3 days, using the previously trained dataset and newly observed patterns from the last 3 days. This rolling window ensures the model stays accurate despite changes in software usage, introduction of new tools, or legitimate workflow shifts.