Defeat Evasive Malware Countering Sandbox Evasion Techniques Used by Malware

whitePaper | September 15, 2022

No broad industry trend, not client/server computing, not affordable hardware, not even the cloud itself, promises to so completely reshape the enterprise than artificial intelligence (AI). Melding decades-old mathematical principles with cutting edge algorithms and readily available, high performance hardware, AI is creating a seismic shift in the way companies across all industries build, maintain, and understand their core and departmental business operations.

whitePaper | July 10, 2020

There’s no arguing that preventing failures and accidents is critical for industry. Unexpected incidents can grind operations to a halt for extended periods of time and necessitate expensive repairs. Just 12 hours of downtime for an oil production platform could cost six to eight million dollars in lost production opportunity alone. A single day of grounding for a plane costs roughly four to five million dollars. Because of these disruptions, industrial sectors are always on the lookout for newer, better maintenance methods, and the approach on everyone’s lips right now is predictive maintenance. While everyone agrees on the name, there is less consensus on what it means or how to implement it. But to truly unlock the potential of predictive maintenance, it needs to be paired with artificial intelligence (AI) and machine learning (ML).

whitePaper | June 3, 2022

A key ingredient of eBusiness is the ability to offer customers the very best user experience, irrespective of whether they use a web browser, a mobile browser or a mobile app. In this guide, we explore the challenges organisations face in managing the numerous ways customers can transact with businesses

whitePaper | July 20, 2022

The growing diversity of actors and circumstances involved in AI/ML deployment makes establishing a set of ethical participatory practices especially difficult for this field. Even among practitioners working at the same organization, there can be substantial differences in both their knowledge about how AI/ML systems were created or will be used and their ability to incorporate inclusive practices. Additionally, the greater availability of AI development platforms, including “no-code” platforms, means that algorithmic models can be deployed without having deep expertise, expanding the number of circumstances automated systems are deployed in. This means many more instances where automated systems are being deployed without consideration of how the algorithms were developed, the provenance of the datasets and nature of the bias on which they were trained and tested, and the ethical implications of their development and deployment.

whitePaper | November 1, 2019

Artificial Intelligence is becoming an ever more important part of our lives. Many of us are already familiar with its everyday applications, such as decluttering our inboxes with spam filters or receiving personalised shopping recommendations. Yet, whether it is powering a new generation of self-driving cars, guarding us against fraud or helping doctors better diagnose health conditions, AI’s transformational abilities are being felt in increasingly ubiquitous and innovative ways.

whitePaper | May 15, 2023

The future of AI is hybrid. As generative AI adoption grows at record-setting speeds1 and drives higher demand for compute,2 AI processing must be distributed between the cloud and devices for AI to scale and reach its full potential – just like traditional computing evolved from mainframes and thin clients to today’s mix of cloud and edge devices. A hybrid AI architecture distributes and coordinates AI workloads among cloud and edge devices, rather than processing in the cloud alone. The cloud and edge devices, such as smartphones, vehicles, PCs, and IoT devices, work together to deliver more powerful, efficient, and highly optimized AI.