Monday, January 5, 2026

Firewall Challenge Week 3 – DEV Community

Keep Your Ubuntu-based VPN Server Up to Date

Enterprise-Grade Security for Small Businesses with Linux and Open Source

Ethics for Ephemeral Signals – A Manifesto

When Regex Falls Short – Auditing Discord Bots with AI Reasoning Models

Cisco Live 2025: Bridging the Gap in the Digital Workplace to Achieve ‘Distance Zero’

Agentforce London: Salesforce Reports 78% of UK Companies Embrace Agentic AI

WhatsApp Aims to Collaborate with Apple on Legal Challenge Against Home Office Encryption Directives

AI and the Creative Industries: A Misguided Decision by the UK Government

Successful GenAI Depends on Effective Data Management

A recent Deloitte survey found that Business and IT executives are concerned about the costs associated with generative artificial intelligence (GenAI) projects. Organizations have a limited time to demonstrate significant and sustained value through their GenAI initiatives.

Deloitte emphasized that cost will play an increasingly important role in decision-making regarding GenAI. The survey, which included 2,770 business and IT leaders, revealed that only 16% of organizations regularly report to their CFO on the value created by GenAI.

As GenAI becomes more integrated into business operations, organizations will start to measure its initiatives against traditional financial metrics. Deloitte predicted that businesses will adopt a range of financial and non-financial measures to showcase the value generated from investments in GenAI projects.

The report suggested that new metrics specific to GenAI capabilities could emerge in the future. Deloitte urged leaders to focus on measuring and communicating the value of GenAI to set expectations and secure support from the C-suite and boardroom.

Deloitte highlighted the importance of using proprietary data effectively to drive the impact expected from GenAI initiatives. The report also discussed the challenges related to using public data to train LLMs, emphasizing the need to preserve access to original data sources and differentiate data generated by LLMs vs. other sources.

Researchers noted potential issues with training AI models using data from LLMs, stressing the importance of preserving access to original data sources and identifying data generated by LLMs. They suggested that collaboration among parties involved in LLM creation could help address these challenges.