How Machine Learning-powered Cyber Recovery Protects and Isolates Data From Advanced Cyber Threats
Machine learning (ML) lets computers learn without being explicitly programmed. Put another way, machine learning teaches computers to do what people do: learn by experience. Machine learning is a domain within the broader field of artificial intelligence.
In security, machine learning continuously learns by analyzing data to find patterns so we can better detect malware in encrypted traffic, find insider threats, predict where “bad neighborhoods” are online to keep people safe when browsing, or protect data in the cloud by uncovering suspicious user behavior. Lets understand how machine learning-powered cyber recovery protects and isolates data from advanced cyber threats:
- Critical data is kept in a dedicated, hardened digital vault with physical and operational air gaps that isolate it from the network and potential cyberattacks.
- Multiple separate logins are required to access the vault, protecting against insider attacks.
- Data written to the Cyber Recovery vault is immutable and unchangeable, preventing malware or ransomware from corrupting vaulted data.
- Malware that is stored in the vault cannot execute or infect data outside of the vault.
- The vault is updated through a replication process that is based on acceptable risk exposure limits for uptime connectivity and data loss parameters.
- Security administrators have complete visibility into the integrity of all data and metadata protected in the vault.
- This technology performs full content indexing to identify and stop suspicious activity through automated alerts and cyber security workflows.
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