DLP: Prevent sensitive data exposure across cloud repositories.

Intelligent Protection of Sensitive Data

FIM detects when files have been modified, and DSPM verifies that your buckets are properly configured, but neither answers a fundamental question: what do these files actually contain?

That is precisely the role of Data Loss Prevention (DLP). This technology analyzes the actual content of your cloud-stored files to automatically identify sensitive or regulated data, including payment card numbers, social security numbers, health data, technical secrets (API keys, tokens), personal information subject to GDPR requirements, and much more.

In today's environment of increasingly stringent regulations and growing legal exposure arising from data breaches, failing to understand what you actually store in your S3 buckets is an extremely high-risk gamble that no mature organization can afford to take.

The operational challenge is significant: your teams generate database exports, Excel reports containing customer data, application logs, debugging dumps, VM snapshots potentially containing system files with credentials, and ZIP archives of projects on a daily basis.

These files end up in your S3 backup or archival buckets, often without any prior validation of their contents. A database export created “for analysis” containing 50,000 payment card numbers. A log file containing plaintext access tokens. A workstation VM snapshot accidentally containing medical records.

Without DLP, these ticking time bombs remain invisible until a misconfigured bucket is exposed publicly, a malicious employee exfiltrates data, or a GDPR auditor uncovers the issue.

tamper's DLP uses a complementary, AI-assisted detection technique to maximize coverage and minimize false positives. Contextual AI analysis examines not only the presence of sensitive data but also its context: an Excel file simultaneously containing first names, last names, email addresses, and phone numbers is automatically classified as a high-risk customer database containing personally identifiable information (PII).

Finally, optical character recognition (OCR) applied to images and scanned PDFs also detects sensitive data embedded in images, including photographs of payment cards, scanned passports, and screenshots of data tables.

DLP's granular classification capabilities enable differentiated handling based on actual criticality. A file containing a single, isolated email address is classified as “Sensitive” and triggers only an informational alert. A file simultaneously containing a first name, last name, address, social security number, and banking details is classified as “Critical,” triggering an immediate high-priority alert.

This tiered approach prevents alert fatigue while ensuring that genuine risks are addressed immediately. Remediation actions are configurable, ranging from simple tagging for traceability (tamperDataClass:PII, tamperSensitivity:HIGH) to automatic deletion following validation.

DLP's business value is measured in terms of risks avoided and strengthened compliance. From a risk perspective, every unidentified piece of sensitive data represents a potential GDPR fine (up to €20 million or 4% of annual turnover), breach notification costs (an average of €150 per affected individual in Europe), reputational damage potentially amounting to millions, and potential legal proceedings.

From a compliance perspective, DLP automatically documents your sensitive data inventory, an explicit requirement under Article 30 of the GDPR (records of processing activities). During a CNIL inspection, being able to demonstrate that you have scanned your 230,000 cloud-stored files, precisely identified the 847 containing personal data, and implemented proportionate protective measures (encryption, restricted access, limited retention) turns a potential finding of non-compliance into demonstrable evidence of accountability.

For your B2B customers who require contractual assurances regarding the protection of the data entrusted to you, DLP becomes a tangible and verifiable sales argument.