Origami Risk GRC vs Security Vision UEBA

Origami Risk GRC helps organizations identify, assess and mitigate enterprise risk while managing governance and compliance. Its coverage includes enterprise risk management, business continuity, third-party risk, policy and procedure management, internal controls, internal audit and compliance. Teams can score risks using heat maps, lenses and multifactor models aligned with their risk appetite, manage control effectiveness and connect controls to frameworks and mitigation plans. Issues can be tracked through assigned action plans and workflows; interactive Bowtie models map relationships among risks, consequences and controls. The native AI Risk and Control Explorer analyzes GRC data to identify relationships, assess controls and surface emerging risks. AI-enabled workflows can route assessments, send escalation alerts and remind teams about control testing. The cloud-native, low-code platform includes drag-and-drop workflow builders and API-driven data connections. The vendor describes implementation help for configuration, historical data migration and phased rollout. Mobile apps are available for capturing data on the go, and pricing is available on request.

Security Vision UEBA builds behavioral models for users, accounts, devices, hosts and processes, then detects deviations in activity and traffic. Its analysis combines correlation rules, statistical methods and machine learning to identify suspicious behavior and incidents. It can analyze raw data from data lakes, SIEM, next-generation firewalls, proxy servers, and Windows or Linux devices. Configured connectors support SIEM systems, Cisco, CheckPoint, Palo Alto and Juniper network devices, Squid and Blue Coat proxies, Kafka, Elasticsearch, and Windows and Linux logs. Users can add or adapt Sigma and correlation rules with a correlation rule editor. UEBA can enrich incident data and send events or incidents to SOAR or SIEM. Response actions can include adding entries to NGFW blocklists or SIEM active lists. Its API provides alarms, suspicious events and object alerts. Deployment is on premises; the maker says its machine-learning models run on customer infrastructure without sending data to the cloud. It is paid with individual pricing, and license type and support level affect cost.