Topic

Data Privacy

Data privacy refers to the right of individuals to control how their personal information is collected, used, stored, and shared by organizations. In AI governance, data privacy is critical because machine learning models often process large volumes of sensitive personal data, and failures to protect this data can result in regulatory penalties, reputational damage, and erosion of customer trust. Enterprises must implement privacy-by-design principles, conduct data impact assessments, and ensure compliance with regulations like GDPR, CCPA, and similar frameworks to mitigate legal and operational risks.

3 items

ResearchGlobal2026-06-16

GenAI in ITSM Deployments Require Formal Hallucination Controls and Access Governance, GSDC Guide Finds

The GSDC Council has published a practitioner guide on deploying generative AI within IT service management functions, covering governance and risk controls including access management, data privacy, hallucination detection, and compliance checks. The guide also recommends ongoing performance measurement to link AI governance to service outcomes. It is aimed at organizations operationalizing GenAI in ITSM contexts globally.

ITSM generative AI hallucination controls data privacy operational AI governance

Corporate PolicyUS2026-05-26

AI Incidents Rose 26% From 2022 to 2023, NACD Guidance Urges Boards to Adapt Oversight

The National Association of Corporate Directors has published governance guidance titled 'Tuning Corporate Governance for AI Adoption,' calling on boards to adapt oversight mechanisms to address AI-specific risks including hallucinations, data privacy concerns, and algorithmic bias. The guidance references AI Incident Database figures showing a 26 percent increase in AI incidents from 2022 to 2023, with 2024 data suggesting a further rise exceeding 32 percent. It is directed at US corporate boards and positions AI risk oversight as a core board-level responsibility.

United States board oversight AI risk incident tracking corporate governance algorithmic bias transparency accountability data privacy risk management

ResearchGlobal2025-05-30

Shadow AI and Client-Side Widgets Create an Inventory Gap That Existing Controls Do Not Cover, Kiteworks Analysis Finds

Kiteworks published a research piece on May 30, 2025, framing the central AI governance challenge as an architecture and visibility problem rather than a policy problem. The analysis identifies shadow AI deployments, embedded client-side scripts, third-party AI widgets, and fragmented controls as the primary blind spots undermining enterprise AI oversight. It recommends continuous inventory, Content Security Policy and script allowlists, third-party AI monitoring programs, joint incident response planning, and treating AI widgets as data processors under applicable privacy frameworks.

shadow AI third-party risk data privacy AI inventory vendor management