Maximising theAI opportunity

whitePaper | January 11, 2023

Improving the trustworthiness of artificial intelligence (AI) systems is a shared priority for the private and public sectors, as indicated by prolific research and guidelines in recent years.

whitePaper | August 10, 2020

Digital innovation is happening fast — and there’s no slowing it down. Financial institutions are shifting priorities as third-party apps and services become more prevalent among consumers. Now, staying connected is no longer just an option, it’s critical to achieving success and staying relevant. mx.com | 1 But moving fast isn’t as easy as it sounds. There’s a fine line that financial institutions have to balance when it comes to innovating quickly to give their customers the experiences they’ve come to expect and ensuring their data remains safe and secure. As data sharing and connected integration ecosystems become the new norm, here are three challenges many financial institutions are facing.

whitePaper | July 20, 2020

Accurate, trustworthy threat intelligence is a boon if you have it – but too much of it becomes a management headache. Analyst group 451 Research, surveying security leaders for its report Tackling the Visibility Gap in Information Security, found that 49% of enterprises using SIEM, EDR, and other security tools were overwhelmed by the day-to-day operation of managing and ingesting threat feeds into their growing technology stack.1

whitePaper | June 2, 2022

Licensing programs are channels where you can buy Power Apps, Power Automate and Power Virtual Agents. You can license Power Apps, Power Automate and Microsoft Power Virtual Agents through Microsoft Volume Licensing (VL), Cloud Solution Provider program (CSP), and/or Web Direct (MOSP) programs. In Volume Licensing, Power Apps and Power Automate are available through:

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 | October 13, 2022

Walk into any Fortune 500 organization, and you’re more than likely to find a room full of data scientists. These individuals are being described by many as the difference makers, the propagators of change, the fuel to the fire of innovation, and the essential ingredient to survival in today’s digital economy. Data scientist job openings are among the hardest positions to fill in the market, yet they are among the highest paying jobs at many companies.