Zafran Threat Exposure Management Platform

Aggregates and deduplicates findings from network scanners, endpoint agents, cloud scanners, and third-party tools into one normalized record for cross-estate risk ranking.

Incorporates asset metadata (network exposure, business criticality, data classification) into vulnerability prioritization so that a critical CVE on an isolated internal test system ranks lower than a medium CVE on an internet-facing payment server.

Assigns likelihood-of-exploitation scores using threat intelligence, vulnerability characteristics, and active exploit availability, independent of CVSS, which measures severity rather than exploitability.

Cross-references the vulnerability inventory against live threat feeds tracking CVEs under active exploitation in the wild, surfacing vulnerabilities with confirmed attacker activity.

Continuously discovers external-facing assets (domains, IPs, cloud services, APIs, certificates) including assets deployed outside the official inventory.

Scans cloud resource configurations and container image CVEs alongside traditional OS and application vulnerabilities in a unified risk view.

Recommends the minimum patch set that eliminates the highest-risk exposure (accounting for shared libraries and patch co-dependencies), rather than presenting a ranked CVE list.

Creates tickets, assigns owners, and tracks remediation progress in ITSM platforms (ServiceNow, Jira), closing the loop between finding and fix rather than producing a static report.

Enforces remediation deadlines by severity, reports on SLA compliance, and escalates overdue findings through configured approval chains.

Time-boxed risk acceptance workflow with documented approvals that keeps exceptions active and tracked as new scan data is ingested, rather than silently closing or re-opening findings.

Discovers assets and their exposures across the external, internal, cloud, and end-user attack surfaces, covering endpoints, network and on-premises infrastructure, identities and entitlements, hosts, containers, IoT and OT, and cloud platforms and applications, either through native discovery or by integrating third-party discovery sources, and reports vulnerabilities, misconfigurations, unmanaged assets, and compliance gaps in one inventory.

Ranks exposures by their accessibility, visibility, and exploitability combined with asset criticality, business impact, and the security controls already in place, so a medium-severity issue on a critical, reachable, unprotected service outranks a high-severity issue on an isolated or compensated one.

Creates and tracks remediation tasks across teams and ticketing systems, measuring exposure reduction over time rather than simply listing open findings.

Maps the discovered exposure inventory against active threat actor targeting and in-the-wild exploitation data to surface vulnerabilities under active attack.

Tracks the life cycle of exposures through a centralized, aggregated view supported by automated workflows.

Models how exposures chain across assets and identities to reach critical systems, mapping attack paths and blast radius to separate reachable crown-jewel risks from dead ends.

Generates trend reports on exposure posture (new exposure, remediated exposure, outstanding exposure by severity), in business language suitable for security program reviews.

Pushes a mitigation for a prioritized exposure directly to a security control, for example a firewall, endpoint, or posture-management rule, as a compensating measure when a patch is unavailable or delayed, and tracks that mitigation alongside the exposure until it is remediated.

Uses generative AI to produce exposure-specific fix instructions, scripts, or remediation playbooks from the finding and its asset context, so remediation owners receive an actionable plan instead of a generic advisory.