Abstract: Techniques for ranking a set of vulnerabilities of a computing asset and set of remediations for a computing asset, and determining a risk score for one or more computing assets are provided. In one technique, vulnerabilities of computing assets in a customer network are received at a vulnerability intelligence platform. Breach data indicating set of breaches that occurred outside customer network is also received. A subset of the set of vulnerabilities that are most vulnerable to a breach is identified based on the breach data. In another technique, multiple vulnerabilities of a computing asset are determined. A risk score is generated for the computing asset based on the vulnerabilities. In another technique, multiple remediations associated with a risk score and multiple vulnerabilities are identified. The remediations are ordered based on the remediations that would reduce the risk score the most if those remediations were applied to remove the corresponding vulnerabilities.

Abstract: Techniques for ranking a set of vulnerabilities of a computing asset and set of remediations for a computing asset, and determining a risk score for one or more computing assets are provided. In one technique, vulnerabilities of computing assets in a customer network are received at a vulnerability intelligence platform. Breach data indicating set of breaches that occurred outside customer network is also received. A subset of the set of vulnerabilities that are most vulnerable to a breach is identified based on the breach data. In another technique, multiple vulnerabilities of a computing asset are determined. A risk score is generated for the computing asset based on the vulnerabilities. In another technique, multiple remediations associated with a risk score and multiple vulnerabilities are identified. The remediations are ordered based on the remediations that would reduce the risk score the most if those remediations were applied to remove the corresponding vulnerabilities.

Abstract: Techniques for ranking a set of vulnerabilities of a computing asset and set of remediations for a computing asset, and determining a risk score for one or more computing assets are provided. In one technique, vulnerabilities of computing assets in a customer network are received at a vulnerability intelligence platform. Breach data indicating set of breaches that occurred outside customer network is also received. A subset of the set of vulnerabilities that are most vulnerable to a breach is identified based on the breach data. In another technique, multiple vulnerabilities of a computing asset are determined. A risk score is generated for the computing asset based on the vulnerabilities. In another technique, multiple remediations associated with a risk score and multiple vulnerabilities are identified. The remediations are ordered based on the remediations that would reduce the risk score the most if those remediations were applied to remove the corresponding vulnerabilities.

Abstract: Techniques for ranking a set of vulnerabilities of a computing asset and set of remediations for a computing asset, and determining a risk score for one or more computing assets are provided. In one technique, vulnerabilities of computing assets in a customer network are received at a vulnerability intelligence platform. Breach data indicating set of breaches that occurred outside customer network is also received. A subset of the set of vulnerabilities that are most vulnerable to a breach is identified based on the breach data. In another technique, multiple vulnerabilities of a computing asset are determined. A risk score is generated for the computing asset based on the vulnerabilities. In another technique, multiple remediations associated with a risk score and multiple vulnerabilities are identified. The remediations are ordered based on the remediations that would reduce the risk score the most if those remediations were applied to remove the corresponding vulnerabilities.