Data Loss Prevention (DLP)

Overview#

A healthcare data controller found that a departing employee had exported 40,000 patient records to a personal cloud storage account over three weeks. The exports happened in small batches, at odd hours, through the same API the employee used legitimately every day. A rule-based DLP system saw nothing unusual. A behavioural baseline would have flagged the volume anomaly on day two.

Argus DLP combines machine learning detection, pattern-based content inspection, and real-time policy enforcement to catch exactly that kind of slow-burn exfiltration alongside the more obvious threats. Sensitive information is monitored across all communication channels, including email, file transfers, API interactions, and clipboard operations, and blocked, quarantined, or escalated based on configurable policies.

Key Features#

• ML-Powered Detection: Machine learning models trained on diverse data sets identify sensitive information with high accuracy, reducing both false positives and false negatives compared to rule-based approaches alone.

• Real-Time Content Inspection: Inline analysis examines data as it moves through your organisation, applying policies before sensitive information can leave controlled environments.

• Policy-Based Controls: Granular DLP policies define what types of sensitive data to detect, which channels to monitor, what actions to take on violations, and which users or departments are in scope.

• Multi-Channel Protection: Comprehensive coverage across email, file uploads and downloads, API requests and responses, clipboard operations, print jobs, cloud sync, chat, and database exports.

• Contextual Analysis: Detection considers the full context of data movement, including the user's role, location, time of day, destination, and data classification level to reduce false positives and prioritise genuine risks.

• Automated Remediation: Configurable response actions include blocking, quarantining, redacting, encrypting, alerting, and creating incident tickets, applied automatically based on policy rules.

• Pre-Built Policy Templates: Ready-to-use templates for common scenarios including PII protection, credit card data protection, source code and secrets protection, and healthcare data protection accelerate deployment.

How It Works#

Detection Approach#

Argus DLP uses a layered detection approach combining multiple methods:

1. Pattern Matching: Regex-based detection with contextual validation identifies structured sensitive data such as credit card numbers, social security numbers, and API keys. Keyword proximity analysis and format validation reduce false positives.

2. Machine Learning Classification: Trained models classify unstructured content to identify sensitive information that pattern matching alone would miss, including financial data, proprietary information, and healthcare records.

3. Anomaly Detection: Behavioural baselines for each user enable detection of unusual data access patterns, volume anomalies, suspicious timing, and unexpected destinations that may indicate exfiltration attempts.

Policy Enforcement#

DLP policies bring together detection and response:

• Scope defines which departments, users, locations, and data classifications the policy applies to
• Rules define what to look for, using pattern matching, ML classification, or a combination
• Actions define what happens when a violation is detected, from logging and alerting to blocking and quarantine
• Exceptions provide a controlled way to grant temporary exemptions with approval workflows and audit trails

Data Classifications#

Argus supports hierarchical data classification levels from Public through Internal, Confidential, Restricted, and Top Secret. Each classification level can have associated detection patterns, retention policies, encryption requirements, and regulatory framework mappings.

Incident Management#

When a DLP policy violation occurs, the system creates an incident record with full context including the triggering policy, affected user, detection method, confidence score, risk assessment, and any automated actions taken. Security teams can investigate incidents, confirm or dismiss findings, and track resolution through the incident lifecycle.

Open Standards#

• GDPR (EU Regulation 2016/679): DLP policies map directly to Articles 25 (data protection by design), 32 (security of processing), and 33 (breach notification), with automated incident records and redaction workflows providing the required technical safeguards.
• NIST SP 800-53: Access control (AC-2), event logging (AU-2), identification and authentication (IA-2), and transmission confidentiality (SC-8, SC-13) controls are explicitly mapped in the compliance matrix and drive the policy enforcement and audit-trail behaviour of the DLP engine.
• ISO/IEC 27001:2022: Multiple Annex A controls including A8.2 (privileged access), A8.8 (vulnerability management), A8.15 (logging), A8.16 (monitoring), and A8.24 (cryptography) are actively checked and reported against, framing the DLP capability within the broader information security management system.
• PCI-DSS: Credit card and primary account number detection uses regex patterns covering standard PAN formats; violations trigger quarantine or blocking actions aligned with the cardholder data protection requirements of the Payment Card Industry Data Security Standard.
• HIPAA Privacy and Security Rules: Protected health information is a named detection category in the DLP engine, and the healthcare policy template maps to the HIPAA minimum-necessary and safeguard requirements for covered entities and business associates.
• ISO/IEC 27701:2019: Registered as a compliance framework and applied to PII handling workflows, extending the ISO 27001 controls with privacy information management requirements relevant to DLP's detection, redaction, and retention lifecycle.
• EU AI Act (Regulation (EU) 2024/1689): The ML-based face detection and biometric processing path enforces Article 5(1)(g) compliance by prohibiting ethnicity inference and defaulting demographics estimation to off, with the prohibition coded as an unconditional constraint rather than a configurable policy.
• NIS2 Directive (EU 2022/2555): Article 21 (cybersecurity risk-management measures) and Article 23 (incident reporting obligations) are mapped as explicit compliance constants, with DLP policy violations feeding the incident reporting pipeline for critical and important entity operators.

Compliance#

DLP supports compliance with:

• GDPR: Data minimisation, right to be forgotten, personal data protection
• HIPAA: Protected health information safeguards and access controls
• PCI-DSS: Credit card and payment data protection
• SOC 2: Access logging, data protection controls, incident response
• CCPA: Consumer privacy rights and data protection

Availability#

• Enterprise Plan: Full DLP suite included
• Professional Plan: Core DLP with standard policy templates; ML-powered detection available as add-on

Last Reviewed: 2026-02-05 Last Updated: 2026-04-14