Donald Trump, Bernie Sanders, and Sam Altman are surprisingly converging on the idea of public ownership for artificial intelligence. This discussion highlights growing concerns about AI's societal impact and the need for broad public benefit from the technology.
As AI becomes more integrated into critical business functions, trust is paramount for successful adoption, requiring transparency, accountability, and human oversight. Building this trust is a strategic business priority, relying on responsible governance, high-quality data, and clear decision-making processes.
The content introduces SAFi (Self-Alignment Framework Interface), an AI governance architecture that deviates from typical prompt-level alignment by distributing cognition across five specialized faculties. This system aims to decouple AI generation, evaluation, and execution, starting with a pre-generation security barrier to prevent prompt injections and other threats.
This article discusses the importance of measuring the impacts of artificial intelligence on humans and society. It explores why the measurement of these effects is not yet a common practice and suggests the need for a more human-centered approach to evaluating AI progress.
The ancient Greek virtue of sophrosyne, embodying self-control and moderation, is argued to be more vital than ever in the age of artificial intelligence. The article explores how this quality can guide AI development and use, preventing excesses and promoting human well-being.
OpenAI has released a blueprint for the democratic governance of frontier AI, outlining a framework for how advanced artificial intelligence systems can be controlled and managed. This initiative aims to ensure safety and accountability through societal input and democratic principles.
Colorado is rolling back its landmark AI governance law, responding to industry pressure to narrow the scope of the legislation. The revised approach will focus solely on high-risk AI models, alleviating compliance concerns for businesses.
The article outlines guidelines for the respectful use of artificial intelligence. It focuses on principles and practices to ensure ethical and responsible interaction with AI.
OpenAI outlines its vision for the future of AI, aiming to ensure Artificial General Intelligence benefits everyone. The plan emphasizes access, safety, and shared prosperity.
An AI customer service agent in a fintech company autonomously issued a $4,200 refund approval based on a hallucinated complaint. The article proposes implementing an "approval gate" or human-in-the-loop system, specifically using LangGraph's interrupt feature, to prevent unsupervised AI actions and ensure human review.
Berkeley City Council has adopted the nation's most comprehensive municipal AI governance policy, mandating strict human oversight for high-impact AI systems. The policy introduces innovative civic AI tools, including a resident-facing AI advocate, aiming to prioritize democratic accountability over technological efficiency.
Trend Micro announced the TrendAI Agentic Governance Gateway at RSAC 2026, a platform designed to provide visibility and control over autonomous AI agent operations. It features real-time observation, context analysis, policy enforcement, human oversight, and pre-deployment simulation.
The article explores the maintenance of bio-inspired soft robotics, addressing the non-linear nature of material degradation that challenges traditional maintenance schedules. The research focuses on Self-Supervised Temporal Pattern Mining to identify precursor signals to failure and integrate zero-trust governance guarantees.
Across history, technological capability has consistently outpaced governance, leading to issues like instability and exploitation. Artificial intelligence is now rapidly accelerating this pattern, with advanced models lacking crucial deployment safeguards and mature governance layers.
A new reference implementation for governance metadata has been shipped within A2A Agent Cards, addressing the lack of governance posture in existing agent descriptions. This extension includes fields for trust score, data retention, and derivation rights, signed for authenticity, with full documentation available.
This content, featuring Nyah Macklin, explores the methodologies for constructing auditable AI agents. It specifically focuses on utilizing context graphs to enhance transparency and accountability in AI systems.
This article compares AI agent governance with existing security tools like IAM, DLP, and API gateways. It explains why traditional tools fall short for AI agents and emphasizes the need for a dedicated AI agent governance layer to address security gaps.
Senior leaders must recognize AI risk as an operating model problem requiring integrated governance across all business processes. Establishing comprehensive AI governance, continuous investment in AI literacy, and robust monitoring are crucial to mitigate risks and build stakeholder trust.
This paper addresses the urgent governance crisis caused by uncontrolled agentic AI sprawl in enterprises, with only 21% having mature governance models. It introduces the Agentic AI Governance Maturity Model (AAGMM), a five-level framework grounded in standards like NIST AI RMF and ISO/IEC 42001.
As enterprises adopt large language models, robust AI governance and security are essential to prevent data leaks, regulatory penalties, and reputational damage. A defense-in-depth approach is crucial to mitigate threats like prompt injection and data contamination, ensuring compliance with regulations such as GDPR and the EU AI Act.