The Complete Cloud Native Security Platform

whitePaper | December 22, 2022

Contact centres are often viewed as cost drivers rather than business drivers. This is understandable, when you consider that differentiation efforts in this space have often struggled to be sustainable from a cost or staffing perspective. Figuring out the formula of investment to increased income is helped when you have an experienced guide to shorten your journey. Read on to get the benefit of our two companies’ experience, multiplied by hundreds of clients.

whitePaper | April 14, 2021

While the new era of remote work has provided some businesses with surprising productivity boosts as high as 13%, many are still struggling to secure their suddenly expanded network of remote endpoints and IT resources against a growing onslaught of cyberthreats.

whitePaper | March 30, 2022

This paper presents data from a large-scale study on how code quality impacts software companies in terms of time-to-market and product experience. We conclude with an analysis of the impact and specific recommendations towards successful software development.

whitePaper | December 26, 2022

IBM embraces five foundational pillars of trustworthy AI: Explainability, Fairness, Robustness, Transparency, and Privacy. These pillars underpin the development, deployment and use of AI systems. This document and IBM’s trustworthy AI pillars are meant to help you align on both process and outcomes.

whitePaper | October 19, 2022

The present White Paper provides information to concerned stakeholders, including SMEs, Industry, Academia, Government Regulation Agencies and others, on the current implementation status of standards potentially suitable for ensuring compliance to the original draft of the AI Act, from an ETSI perspective. The overall set-up within ETSI is discussed and most relevant Technical Committees and Industry Specification Groups and related available deliverables and plans are identified.

whitePaper | December 15, 2019

Artificial Intelligence (AI), and specifically Machine Learning (ML), are being tested in an increasing number of fields, including data-centric environments. Image or text analysis, speech recognition, chatbot interactions, custom machine learning models… all these are elements that could enable the AI journey of a public safety and security organisation. This document dives into different aspects of integrating AI & ML in Public Safety activities, at different levels and in different domains of activity. It presents ethical and regulatory considerations, real examples from Public Safety Answering Points (PSAPs) and Emergency Response Organisations (EROs), and also initiatives that can benefit the public sector greatly, with a series of recommendations at the end.