Definition
Data Leakage Prevention (DLP) is a set of technologies, processes, and control mechanisms designed to detect, monitor, and prevent unauthorized disclosure of sensitive information. It applies to personal data, special-category data, financial information, medical records, intellectual property, and any content whose exposure may violate legal, contractual, or regulatory requirements.
DLP solutions inspect the flow of data at rest, in use, and in transit. They enforce classification- and policy-based rules to prevent unauthorized transmission of sensitive information via networks, storage systems, cloud platforms, or user interactions.
Core components of DLP
- Endpoint DLP - monitoring of user devices (file copying, screenshots, USB transfer).
- Network DLP - inspection of network traffic (email, HTTP, FTP).
- Storage DLP - scanning data repositories (cloud, NAS, internal file servers).
Detection techniques
- Content inspection - analysis of raw file content, including images and PDFs.
- Pattern matching (regex) - detection of identifiers such as SSNs, card numbers or national IDs.
- Machine learning classification - identification of content categories using trained models.
- OCR - extraction of text from scanned images or video frames.
- Contextual analysis - evaluation of user behavior, application type, destination, and device context.
Key metrics and parameters
Metric | Importance |
True Positive Rate | Accuracy in detecting actual data leak incidents. |
False Positive Rate | Incorrectly triggered alerts that disrupt normal workflow. |
Latency | Time required to analyze data, critical for video and streaming. |
Coverage | Scope of protected data types, storage systems, and communication channels. |
Relevance to image and video anonymization
DLP systems are increasingly essential in environments where sensitive data appears in multimedia content. Combined with visual AI, DLP prevents the distribution of unredacted footage or images containing personal information, such as:
- faces, license plates, biometric data,
- documents captured in camera frames,
- background elements that reveal personal or proprietary details.
Common integrations include:
- blocking upload or transmission of non-anonymized video,
- real-time analysis of video streams for privacy risks,
- protecting medical imaging systems from unauthorized export,
- verifying compliance with organizational data handling policies.
Challenges and limitations
- False positives when using highly sensitive detection rules.
- High computational cost for multimedia analysis.
- Complex deployments in hybrid environments.
- OCR limitations in low-quality video footage.
- Strict regulatory requirements for log retention and audit trails.
Use cases
- monitoring distribution of video recordings containing faces or identifiers,
- preventing unauthorized export of industrial camera footage,
- protecting patient data visible on medical video streams,
- detecting data leaks in corporate communication channels,
- blocking exfiltration of confidential visual assets.