Research warns that artificial intelligence may replicate patterns of colonial exploitation by extracting data from Indigenous communities without proper oversight or compensation. The study proposes a governance framework to prevent this “AI extractivism” by advocating for Indigenous data justice and equitable benefit-sharing.
As organizations increasingly integrate AI into their operations, the need for robust governance frameworks has become crucial. Boards are now tasked with overseeing AI deployment, ensuring accountability, and managing associated risks to harness AI’s benefits effectively.
The majority of Americans support increased regulation of artificial intelligence (AI), yet over 1,000 related bills have been introduced in the U.S. in the last three years. To help the public navigate this complex landscape, the Center for Technological Responsibility at Brown University has launched a portal that features a Bill Library and Bill Profiles for identifying trends and assessing proposals.
A new economic study highlights that existing regulatory frameworks are ill-suited for the rapidly evolving AI supply chain, where a few companies develop powerful foundation models that are adapted by many downstream firms. The research emphasizes how regulatory interventions can significantly impact competition, pricing, and ultimately consumer welfare in the AI industry.
A working group in Colorado has reached a consensus on new regulations for artificial intelligence, aiming to prevent discrimination by AI systems. The agreed framework will replace prior controversial rules and requires clearer disclosures from AI developers and deployers regarding their technologies and the decisions they make.
Cato Networks has introduced two major innovations for its SASE Platform: Cato Neural Edge, which utilizes NVIDIA GPUs for real-time AI-driven traffic analysis and threat detection, and Cato AI Security, which unifies AI governance and protection. These advancements aim to secure enterprises as they navigate the challenges of AI transformation while enhancing network performance and security capabilities.
This paper analyzes the varying capabilities of U.S. states in implementing AI legislation, revealing that younger, wealthier, and Democratic-leaning states are more active in proposing such bills. Despite a high number of introduced bills, many states struggle with passing legislation due to competing interests and complex societal issues.
The EU AI Act is set to impose strict compliance requirements for high-risk AI systems in the energy sector, with obligations starting August 2, 2026. Energy companies must assess their AI systems, as penalties for non-compliance can reach €15 million or 3% of global annual turnover.
By 2029, most governments are expected to deploy AI agents for routine decision-making, aiming for faster and more consistent outcomes. Alongside this shift, stronger oversight requirements will be implemented to ensure greater transparency and accountability in automated decisions affecting citizen services.
As AI agents increasingly operate autonomously within production systems, they often use shared credentials, which create significant governance risks. Transitioning to an identity-first governance model is essential, ensuring that each autonomous system is treated as a distinct entity with clear permissions and accountability.
