Brochures
Patented Method for Detecting and Preventing Data Exfiltration Attacks
Data exfiltration refers to the malicious transfer of data from a computer or other device. It may be done manually by someone with physical access to a computer or by an automated program over a network. Such exfiltrated data streams closely mimic routine network traffic, and hence detecting these attacks can be challenging. Since endpoints are one of the easiest access points for hackers, an all-encompassing approach to data security that monitors and protects every endpoint in an organization’s network is critical.
Download PDFZIFTM Digital Xperience (Dx)
ZIF™ is a prominent player in the Digital Experience space, helping organizations to continuously assess and optimize Digital Experiences across the entire customer life cycle and all digital channels of interaction. ZIF™ makes it possible to view Infra and Application performance issues from a user experience perspective and provides actionable insights to improve the same.
Download PDFZIFTM AIOps Platform for Service Reliability Engineering (SRE)
The Zero Incident Framework™ (ZIF™) is a robust AIOps solution that enables proactive detection and remediation of IT incidents, thus enabling SRE teams to move from a reactive to proactive approach towards incidents impacting applications and infrastructure. ZIF™ is a DevOps friendly platform and is available both as an On-premises and SaaS solution.
Download PDFReduce Vulnerabilities Using AIOps
Zero Incident FrameworkTM (ZIFTM) is a comprehensive AIOps platform that proactively detects and remediates Cybersecurity threats, thereby enabling Security Operations to transcend to a proactive approach.
Download PDFIT Discovery with ZIF
Much like the blind spots that evade a driver’s vision, blind spots also exist in IT environments. Unfortunately, they can be equally disastrous and impact an organization on various levels such as performance, cost, and effort. They could also lead to security compromise and negatively affect end user experience and satisfaction.
Download PDFAIOps is driving next-gen Managed Service Providers to new heights
Managed Service Providers (MSPs) must continuously innovate their offerings by expanding into evolving markets and consistently delivering superior customer experiences. Consequently, many savvy MSPs are ditching legacy tools for AI/ML-driven platforms. Artificial Intelligence for IT Operations (AIOps) is key for MSPs to deliver against broadening customer SLAs and dramatically improve operational efficiency.
Download PDFZIF End User Monitoring
ZIF Monitor is a component of ZIF™ – our AIOps tool that ensures business continuity through real-time and proactive monitoring of IT environment using applications of ML, predictive analytics & automation. The ZIF Monitor platform monitors all the layers involved in the user experience in real time. The layers that are monitored include but are not limited to – applications, databases, servers, APIs, end points, and network devices.
Download PDFZIFTM AIOps Platform for Accelerated Business Outcomes
Artificial Intelligence in IT Operations (AIOps) is the new normal, and having the right platform makes all the difference – it significantly increases ITOps efficiency, IT engineer productivity, and can save you from the ITOps iceberg!
Download PDFPatented ZIF Capability Network Management
Proactive Determination of Potential Outages in an IT Environment A Patented Capability of ZIFTM
Download PDFPatented ZIF Capability IT Outage Prediction
Streaming MIB Data Using SNMP A Patented Network Management Capability
Download PDFZIF Auto Discovery and Dependency Mapping
AIOps Platform Zero Incident FrameworkTM (ZIF) provides Application Auto Discovery and Dependency Mapping (ADDM). ZIF automatically discovers and maps the applications and topology of the end to end deployment, hop by hop.
Download PDFZero Incident FrameworkTM
An AIOps based TechOps platform that enables proactive detection and remediation of incidents, helping organizations drive towards a Zero Incident EnterpriseTM
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