Which factors are considered in the multi-data algorithm for risk assessment?

The multi-data algorithm for risk assessment in the context of Zscaler focuses on specific technical attributes and indicators that can signal potential risk in data traffic and user behavior. One of the key combinations used in this algorithm includes TLD (Top-Level Domain), user agent, and domain name entropy.

TLD refers to the last segment of the domain name and can provide insight into the credibility of a website. For instance, some TLDs are associated with higher levels of risk or are used primarily for malicious purposes. The user agent, which identifies the browser or application making the request for content, helps in assessing the context of the request and whether it aligns with typical user behavior or appears suspicious. Domain name entropy indicates how random or predictable a domain name is; higher entropy can suggest that the domain is likely newly registered or potentially malicious, as attackers often use complex domain names to avoid detection.

Together, these factors create a robust framework for identifying risks associated with traffic and users in the network, allowing organizations to maintain a secure environment while ensuring that legitimate activities are not unnecessarily hindered. This orientation towards technical attributes is a significant aspect of how security technologies like Zscaler utilize algorithms for proactive risk management.