Palo Alto Networks today introduced Enterprise Data Loss Prevention (DLP)—a cloud-delivered service that brings a fresh, simple and modern approach to data protection, privacy and compliance.
“Data breaches are a huge and growing problem worldwide, but the existing legacy and point solutions are not accessible, appropriate or effective for many of the companies that need them,” said Anand Oswal, senior vice president and general manager, Firewall as a Platform, Palo Alto Networks. “Our new Enterprise DLP solution is powerful; simple to deploy, with no new infrastructure needed; integrates with existing security technologies; and works for companies whether they keep their data in the cloud, on-prem or take a flexible approach.”
The solution helps protect sensitive data at rest and in motion across every network, cloud and user access and effortlessly helps solve three major enterprise data security problems:
- Helps prevent data breaches by automatically identifying confidential intellectual property and personally identifiable information (PII) consistently throughout the entire enterprise.
- Facilitates regulatory compliance by helping enterprises meet data security requirements for the General Data Protection Regulation (GDPR), Payment Card Industry Data Security Standard (PCI DSS), Health Insurance Portability and Accountability Act (HIPAA), California Consumer Privacy Act (CCPA) and many more.
- Inhibits risky user behavior to aid in blocking voluntary or involuntary data exposure and data movement.
As a single centralized cloud service, Palo Alto Networks Enterprise DLP can be deployed across an entire large enterprise in minutes with no need for additional infrastructure. In addition, the service makes it easy to define data protection policies and configurations once and automatically apply them to every network location and cloud where an organization has data. This also makes it easy for security teams to deploy DLP when organizations add new users or branch offices.
Palo Alto’s Enterprise DLP can automatically detect sensitive content via advanced machine learning-based data classification and data patterns that leverage over 500+ industry-defined data identifiers. Some examples of these include, but are not limited to, credit card numbers, Social Security numbers and financial records.