One Article Review
| Source |  Kovrr |
|---|---|
| Identifiant | 8393602 |
| Date de publication | 2021-10-19 00:00:00 (vue: 2023-10-10 07:25:34) |
| Titre | A Sneak Peek into Kovrrâs Data SourcesA sneak peek into Kovrr\\\'s unique data sources used exclusively for modeling purposesRead More |
| Texte | Modeling impacts from cyber events requires extensive understanding of the cyber threat landscape. A core aspect of Kovrrâs cyber risk modeling data pipeline combines unique data sources to better inform the data points taken into account when building out the frequency and severity of cyber events. Access to these data sources is derived via partnerships reserved for Kovrrâs use exclusively for modeling purposes, developed among others with Israeli cybersecurity emerging vendors which continuously bring new exciting data and create a unique ecosystem. Hudson RockHudson Rock is a cybercrime intelligence startup with a database composed of millions of machines compromised in global malware spreading campaigns. The data is augmented monthly with tens of thousands, to hundreds of thousands of new compromised machines. Data includes Info Stealers, ransomware bots and other types of malware. Hudsonâs high-fidelity data help protect employees, partners, customers, and digital assets with unprecedented granularity of threat vectors including Ransomware, Business Espionage, Breaches & Network Overtakes.âHow Kovrr uses this dataâKovrr has extended capabilities to recognize ransomware trends and emerging techniques. This information is crucial for formulating accurate attack distributions. Kovrr leverages the data in order to enrich different parameters of its datasets. We can improve our understanding of the target audience profile by applying additional analytics on the data, Kovrr can deduct the entities who have suffered from the breach, this information may include location, job description and company. We also have extended information on the attack vector. Kovrr uses metadata regarding the attack to understand the attack vector used to install the malware, which is critical to understanding attack and exploitation patterns. Cynerio Medical and IoT devices in healthcare environments grow more numerous and vulnerable every day, and mitigating their risk is becoming more complex. The Cynerio platform uses a granular inventory classification taxonomy which tracks device types, functions, vendors, models, serial numbers, firmware/OS, MAC, and IP+ methods of medical devices. Drilldowns into VLANs, ports, kernels, HW, services, browsers, and FDA class, classification, and recalls are also provided. Cynerio then leverages this data to monitor, verify, and reduce the risk of IoT and IoMT device vulnerabilities through direct communication with vendors, third-party solution providers, and cybersecurity governance organizations.How Kovrr uses this dataKovrr has secured unique cyber information sources per industry to have more detailed data reflecting the cyber risk landscape. Kovrr receives aggregated data on compromised medical IoT devices and relevant vulnerabilities, corresponding to companiesâ geographic location, size and industry that shows instances of potential attack per type of device. For this specific source, Kovrrâs extended insights surrounding healthcare cybersecurity feeds into the industry exposure database. In turn this provides more accurate data on the frequency and severity of events affecting organizations in the healthcare industry and assists in better analysis of understanding a companyâs cyber resilience.Sedric.me Sedric integrates into all communication systems of organizations and provides cyber risk management teams with a solution to securely store company interactions with internal and external users. By monitoring a wide range of interactions, Sedric uses AI to detect intentions related to regulatory, compliance, and company misconduct without the need for explicit exact phrase or rule matches.  The platform securely cleans ,encrypts and stores data associated with GDPR, PCI, PHI, and other violations before it enters a companyâs system.âHow Kovrr uses this dataKovrr receives aggregated data of sensitive data records corresponding to companiesâ g |
| Envoyé | Oui |
| Condensat | access account accurate additional affect affecting aggregated all also among analysis analytics applied applying are aspect assets assists associated attack attacks audience augmented becoming before belief better bots breach breaches bring browsers building business but campaigns can capabilities captured class classification cleans combines communication companiesâ company companyâs complex compliance composed compromised continues continuously core corresponding create critical crucial customers cyber cybercrime cybersecurity cynerio data database datakovrr datasets dataâkovrr day deduct derived description detailed detect developed developing device devices devicesâ different digital direct distribution distributions drilldowns ecosystem emerging employees enables encrypts enough enrich enters entities environments espionage events every exact exciting exclusive exclusively expertly explicit exploitation exposure extended extensive external fda feeds fidelity firmware/os formulating framework frequency from functions further fused gdpr geographic global governance granular granularity grow has have healthcare help high historical how hudsonâs hundreds impacts improve incident include includes including industry info inform information insights install instances insurance integrates intelligence intentions interactions internal inventory involved iomt iot ip+ isnât israeli its job just kernels kovrr kovrrâs landscape leader leverages likelihood location mac machines malware management matches may medical metadata methods meâ sedric millions misconduct mitigating modeling models monitor monitoring monthly more need needs network new numbers numerous order organization organizational organizations other others out overtakes parameters partners partnerships party patterns pci peek per phi phrase pipeline platform points ports potential predict profile properly protect provided providers provides purposes purposesread range ransomware recalls receives recognize records reduce reflecting regarding regulation regulatory related relevant requires reserved resilience right risk rock rockhudson rule secured securely sedric sensitive serial services severity shows size sneak solution some source sources sourcesa specific spreading startup stealers store stores suffered support surrounding system systems taken target taxonomy teams techniques tens then these third thousands threat through tracks trends turn type types understand understanding unique unprecedented updated use used users uses vector vectors vendors verify violation violations vlans vulnerabilities vulnerable when which who wide within without âhow â cynerioâ medical â hudson â the â âthere |
| Tags | Ransomware Malware Vulnerability Threat Prediction Medical |
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| Notes | ★★★ |
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