Audience

Companies, professionals and organizations searching for a solution to ensure the protection of their hybrid cloud

About AhnLab CPP

An increasing number of businesses are migrating their workloads to cloud environments to accelerate digital transformation. However, cloud environments require a new security platform for centralized visibility and management of cloud workloads. AhnLab CPP is a single, centralized cloud workload protection platform that focuses on providing optimized protection, unified management, and flexibility for workloads in hybrid environments. Provides comprehensive visibility and easy management for workloads in on-premise and cloud server (AWS, Azure) environments. Delivers easy operation and management through a single, web-based management platform. Provides module-based CPP management, which enables flexible configuration according to the business environment. Saves cost by allowing selective installation and application of security solutions. Provides real-time malware scan on Windows and Linux servers with minimal impact on resources and performance.

Integrations

API:
Yes, AhnLab CPP offers API access

Ratings/Reviews

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Company Information

AhnLab
South Korea
global.ahnlab.com/site/product/productSubDetail.do

Videos and Screen Captures

AhnLab CPP Screenshot 1
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Product Details

Platforms Supported
Cloud
Windows
Linux
On-Premises
Training
Documentation
In Person
Support
Phone Support
Online

AhnLab CPP Frequently Asked Questions

Q: What kinds of users and organization types does AhnLab CPP work with?
Q: What languages does AhnLab CPP support in their product?
Q: What kind of support options does AhnLab CPP offer?
Q: What other applications or services does AhnLab CPP integrate with?
Q: Does AhnLab CPP have an API?
Q: What type of training does AhnLab CPP provide?

AhnLab CPP Product Features

Cloud Workload Protection

Cloud Gap Analysis
Cloud Registry
Asset Discovery
Governance
Logging & Reporting
Data Security
Data Loss Prevention (DLP)
Security Audit
Anomaly Detection
Workload Diversity
Machine Learning