Articles about governance

Workers are increasingly using shadow AI to draft emails, analyze data, or summarize meetings, but are pretending they haven’t.

New data from marketing agency OutreachX finds 52 percent of US workers are worried about how AI will be used in their workplace in the future and that 48 percent of desk workers say they would be uncomfortable telling a manager they used AI for common tasks.

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New research shows 81 percent of organizations knowingly ship vulnerable code, and 98 percent experienced a breach stemming from vulnerable code in the past year, that’s a sharp rise from 91 percent in 2024.

The survey from Checkmarx, of more than 1,500 CISOs, AppSec managers and developers around the world, also shows that AI‑generated code is becoming mainstream, but governance is lagging.

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The rise of generative and agentic AI is transforming how data is accessed and used, not just by humans but by non-human AI agents acting on their behalf. This shift is driving an unprecedented surge in data access demands, creating a governance challenge at a scale that traditional methods can’t handle.

If organizations can’t match the surge in access requests, innovation will stall, compliance risks will spike, and organizations will reach a breaking point. Joe Regensburger, VP of research at Immuta, argues that the solution isn’t more powerful AI models; it’s better governance. We talked to him to learn more.

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According to new research 93 percent of firms in the UK today use AI in some capacity, but most lack the frameworks to manage its risks and don’t integrate AI governance into their software development processes.

The study from Trustmarque shows only seven percent have fully embedded governance frameworks to manage AI risks. In addition a mere four percent consider their technology infrastructure fully AI-ready, and just eight percent have integrated AI governance into their software development lifecycle.

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A new survey finds that 32 percent of employees who use GenAI tools at work say they're keeping it a secret from their employer.

The research from Ivanti finds that some use discretion when using AI because they like the 'secret advantage' it offers (36 percent); others because they worry their job may be cut (30 percent); and 27 percent have AI-fueled imposter syndrome, saying they don’t want people to question their ability.

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Nearly 80 percent of IT professionals responding to a new survey rank governance as 'extremely important,' underscoring the fact that while organizations are eager to innovate, they still want to do so responsibly

The study by API management firm Gravitee looks at the use of Agentic AI systems and Large Language Models (LLMs) by large and midsize companies and finds 72 percent of respondents report that their organizations are actively using agentic AI systems today.

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The rise of AI and increasing global regulations have raised the stakes for businesses, as they navigate complex requirements to protect sensitive data and ensure ethical practices.

A new survey from trust management platform Drata reveals that 48 percent of governance, risk, and compliance (GRC) professionals struggle to keep pace with updates to existing compliance frameworks and identifying areas needing attention.

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This year, IT teams across the globe are facing a tech landscape that’s undergoing a major transition. Cybersecurity has emerged as a paramount concern, with escalating threats and sophisticated cyber-attacks posing significant risks to business operations.

At the same time, the widespread move towards cloud adoption introduces complexities in managing data across different platforms, making it essential for companies to follow strict data management and oversight practices.

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With ever more organizations rushing to adopt AI solutions, a new report suggests that implementing stronger data governance and security controls will be a higher priority for data teams as we head into 2024.

The report from data security company Immuta finds that only half of respondents say their organization's data security strategy is keeping up with AI's rate of evolution.

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Achieving business success and making informed decisions requires high-quality data that organizations can trust. However, achieving data quality can be complex and time-consuming, forcing data and analytics leaders to choose between supporting business outcomes and ensuring compliance with data privacy and regulatory needs. Here’s how organizations can achieve business outcomes and compliance with regulations and privacy policies.

Data is often referred to as the new oil for a good reason. In today’s digital age, we are generating and collecting data at an unprecedented pace. As Sir Tim Berners-Lee said, "This precious resource will last longer than the systems themselves." IDC projects that by 2025, we will hit 175 Zettabytes of data. However, the challenge for businesses is how to turn this raw data into valuable insights. This is where data quality and governance come in.

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Digital technology company, Kainos, along with award-winning slow news organization, Tortoise, has produced a report looking at how business leaders should respond to challenges around trust in AI.

Through interviews with twenty international AI experts -- from companies including Accenture, The Alan Turing Institute, and UNESCO -- the report identifies three guiding principles to improve trust in AI.

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Recent years have seen an increasing emphasis on data governance, with growing levels of regulation accompanied by a widespread awareness that data is an asset with -- potentially -- huge latent value.

But, with experience and skillsets at a premium, many organizations are applying an inexact set of methodologies and tools in an attempt to manage and exploit their data assets while staying compliant.

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