Data Loss Prevention (DLP)
Slim.io provides comprehensive Data Loss Prevention capabilities that operate across two domains: Endpoint DLP for real-time inspection of API traffic and AI interactions, and Cloud DLP for deep scanning of data at rest in cloud storage.
What DLP Protects Against
Data loss prevention addresses the risk of sensitive information being exposed, exfiltrated, or mishandled. Slim.io’s DLP engine protects against:
- Accidental exposure — PII, PHI, or PCI data included in API responses, AI prompts, or shared files
- Shadow AI risks — Sensitive data sent to LLM providers (OpenAI, Anthropic, Google) without appropriate redaction
- Regulatory violations — Non-compliant storage or transmission of data governed by HIPAA, PCI-DSS, GDPR, or CCPA
- Insider threats — Unusual patterns of data access or bulk extraction detected through behavioral analysis
- Cloud misconfiguration — Sensitive files stored in publicly accessible buckets or overly permissive storage accounts
How Slim.io Implements DLP
Endpoint DLP
Endpoint DLP operates at API boundaries, inspecting data in transit before it reaches external services. It is designed for:
- Real-time inspection of AI prompts and completions
- API gateway integration for request/response scanning
- Inline redaction or tokenization before data leaves your perimeter
See Endpoint DLP for configuration and supported providers.
Cloud DLP
Cloud DLP scans data at rest across your cloud storage providers. It integrates with native cloud DLP services and supplements them with Slim.io’s own detection engine:
- Scheduled and event-driven scanning of S3, GCS, and Azure Blob Storage
- Integration with Google Cloud DLP, AWS Macie, and Azure Purview
- Unified findings view that correlates native cloud detections with Slim.io results
See Cloud DLP for setup instructions per provider.
Endpoint DLP vs. Cloud DLP
| Capability | Endpoint DLP | Cloud DLP |
|---|---|---|
| Inspection target | API traffic, AI prompts, real-time data flows | Files at rest in cloud storage |
| Timing | Real-time (inline) | Scheduled, incremental, or event-driven |
| Latency impact | Sub-100ms per request | None (asynchronous) |
| Remediation | Block, redact, or tokenize before delivery | Tokenize, mask, quarantine, or alert |
| AI provider support | OpenAI, Anthropic, Google Vertex AI | N/A |
| Cloud provider support | Any HTTP-based API | AWS S3, GCS, Azure Blob |
| Detection engine | Slim.io classifiers | Slim.io classifiers + native cloud DLP |
| Deployment | Proxy or SDK integration | Connector-based (cross-account IAM) |
Most organizations deploy both Endpoint and Cloud DLP together. Endpoint DLP catches sensitive data in real-time flows, while Cloud DLP provides comprehensive coverage of historical and at-rest data.
Data Classification
Slim.io ships with 170 built-in detection patterns covering PII categories across 50+ countries (SSN, credit cards, email, phone numbers, health records, government IDs, and more). Detection patterns include regex matching, checksum validation, proximity keywords, and AI-assisted classification. You can extend detection with custom classifiers using the YAML Detection-as-Code system.
See Data Classification for the full list of supported types and custom classifier configuration.
Tokenization and Masking
When DLP detects sensitive data, protective actions can be applied automatically:
- Tokenization — Reversible authenticated AES-256 encryption that preserves data utility while protecting the original value
- Masking — Irreversible redaction that permanently removes the sensitive content
See Tokenization & Masking for implementation details and API reference.
Learn More
- Endpoint DLP — Real-time inspection at API boundaries
- Cloud DLP — Cloud storage scanning and native DLP integration
- Data Classification — Built-in and custom classifiers
- Tokenization & Masking — Encryption and redaction strategies