01Introduction
Most organizations can name the data they're most afraid to lose: card numbers, health records, source code. Data loss prevention tries to stop that data leaving through the channels people use every day.
02What is DLP?
Data loss prevention (DLP) is a set of tools and policies that identify sensitive data and monitor or block its movement across endpoints, email, web traffic and cloud applications to prevent accidental or malicious exfiltration.
03How DLP works
DLP classifies content, then enforces policy at each exit.
- 1.
Classify
Detect sensitive data with patterns, fingerprints, exact data matching and machine learning.
- 2.
Define policy
Decide which data may go where, and by whom.
- 3.
Inspect channels
Monitor email, uploads, endpoint copy actions and SaaS sharing.
- 4.
Enforce
Warn, encrypt, block or log, and route incidents for review.
04Threats and risks
DLP programs are known for friction and gaps.
False positives
Overly broad patterns that block legitimate work and train users to bypass controls.
Unmonitored paths
Data leaving through APIs, cloud storage or AI tools that DLP doesn't inspect.
Insider exfiltration
Authorized users moving data slowly enough to avoid thresholds.
Breach, not leak
DLP watches users. An attacker reading a misconfigured bucket never crosses a DLP control.
05How Parameter helps
Parameter works on the other half of data loss: the paths attackers use to reach data at rest.
Data access paths
Cloud Security shows which identities and exposures reach sensitive stores, and fixes them in Terraform.
Application data exposure
The pentesting agents test for broken access control that returns other users' data.
Secrets that unlock data
Sentinel catches the database and storage credentials that DLP can't see.
