• Helping build shared standards for advanced AI — OpenAI, together with the Linux Foundation, launched the Appia Foundation to develop open, modular specifications that translate international standards into practical assessment criteria across the AI value chain, creating a shared trust layer for verifying compliance across organizations and jurisdictions. 🔗 Graph: ai-governance, openai, frontier-safety 📅 Published: 2026-06-23 📰 https://openai.com/index/helping-build-shared-standards-for-advanced-ai 📌 Key takeaways: • OpenAI co-founded the Appia Foundation (hosted by the Linux Foundation) to develop open, modular specifications for verifying AI model conformance with international standards — effectively creating a "trust layer" for third-party assessment across the AI supply chain • The effort builds on OpenAI's Frontier Safety Blueprint and its testing partnerships with U.S. CAISI and UK AISI, aiming to make evaluation practices interoperable across organizations, jurisdictions, and the supply chain • Appia will focus on producing reusable evidence when models, infrastructure, and applications are developed by different organizations, enabling national and international institutions to trust each other's work • The initiative reflects a growing recognition that strong national institutions (like CAISI) need to be paired with international cooperation and shared technical standards for frontier AI governance to work

• RIFT-Bench: Dynamic Red-teaming For Agentic AI Systems — A new arXiv paper introduces RIFT-Bench, a graph-driven framework for unified security evaluation across diverse agentic AI architectures, tested across 45 agentic systems with adaptive adversarial probes. 🔗 Graph: agentic-ai, ai-security, llm-security 📅 Published: 2026-06-24 📰 https://arxiv.org/abs/2606.23927 📌 Key takeaways: • RIFT-Bench addresses the gap in security evaluations for agentic AI systems — which expose attack vectors beyond traditional LLM vulnerabilities due to their autonomous decision-making capabilities • The framework operates in two automated phases: Discovery (extracts system structure via hierarchical graph representation) and Scanning (deploys adaptive adversarial attacks and produces a comprehensive evaluation report) • Tested across 45 agentic systems spanning diverse implementations, demonstrating the approach generalizes effectively to heterogeneous agentic architectures • RIFT-Bench also supports direct evaluation of mitigation strategies, positioning it as a scalable foundation for security evaluation of agentic AI systems — directly relevant as organizations deploy AI agents with tool access

• AI Shifts Cybersecurity Focus from Finding Flaws to Fixing Them — Anthropic's Project Glasswing has expanded to 150+ organizations across 15+ countries, using Claude Mythos Preview to identify 10,000+ high- or critical-severity vulnerabilities and shifting the cybersecurity bottleneck from discovery to remediation prioritization. 🔗 Graph: ai-security, anthropic, cybersecurity 📅 Published: 2026-06-22 📰 https://campustechnology.com/articles/2026/06/22/ai-shifts-cybersecurity-focus-from-finding-flaws-to-fixing-them.aspx 📌 Key takeaways: • Anthropic's Project Glasswing provides vetted organizations access to Claude Mythos Preview, an AI model specifically designed to identify software vulnerabilities and attack paths at scale • BT Group became the first U.K. company to publicly join, planning to use the technology across its networks — BT currently blocks approximately 4 million cyber attacks per day • The same capabilities that help defenders raise concerns about offensive misuse; Anthropic restricts access to vetted organizations and does not make Claude Mythos Preview broadly available • Security experts say AI's ability to analyze large code bases and connect multiple lower-severity vulnerabilities into realistic attack chains is its most significant advantage — shifting the cybersecurity challenge from finding flaws to determining which ones to fix first

• Let's look at the human advantage in the AI economy — Pace University Lubin School of Business dean Ajay Khorana argues that as algorithms become commoditized, authentic human relationships and in-person networks remain the ultimate competitive advantage, with direct implications for how universities design curricula and career pathways. 🔗 Graph: higher-ed-ai, future-of-work 📅 Published: 2026-06-23 📰 https://universitybusiness.com/lets-look-at-the-human-advantage-in-the-ai-economy/ 📌 Key takeaways: • Khorana argues that as AI automates routine cognitive tasks, the ability to build trusted human networks becomes a modern necessity rather than just a traditional career strategy • The op-ed makes a case for preserving in-person interaction in education, warning that substituting digital screens for physical presence risks trading away the spaces where professional networks are actually created • Career lessons are "absorbed through osmosis" — learned through spontaneous whiteboard sessions, hallway conversations, and observing mentors handle difficult situations, not through formal Zoom meetings or automated training modules • The piece has direct relevance for higher-ed leaders designing hybrid learning models and career development programs: technology accelerates progress, but people determine the direction of that progress

• Data Governance and Reporting Transformation: ULM and Scaffold DataX — The University of Louisiana Monroe partnered with K16 Solutions' Scaffold DataX platform to build a centralized data governance framework and automated reporting system, serving as a pilot for the entire UL System with blueprints for completers, census, applicant funnel, and cash reporting. 🔗 Graph: ai-governance, data-analytics, higher-ed-ai 📅 Published: 2026-06-22 📰 https://er.educause.edu/articles/2026/6/data-governance-and-reporting-transformation-ulm-and-scaffold-datax 📌 Key takeaways: • ULM's data modernization effort addresses a problem nearly universal in higher ed: siloed data across disconnected systems with inconsistent definitions, uneven access, and limited interoperability — the most reported barrier to advancing data strategies • The project deployed four key reporting blueprints via the DataX platform: completers reporting for system-wide Board of Regents compliance, census enrollment reporting, a Student Applicant Funnel for recruitment analytics, and automated cash reporting to meet a Board of Regents directive • Key lessons: time investment in validation is the most valuable resource; build a culture of continuous learning and shared accountability around data; data modernization is as much an organizational change effort as a technical one • The case study directly parallels the data governance challenges Brett works on — disconnected systems, manual reporting burdens, and the need for trusted data foundations before AI-driven decision support can deliver

💡 Signal: This week's digest is bookended by governance — from OpenAI's push for interoperable AI standards at the frontier to a detailed EDUCAUSE case study on the data governance trenches in higher ed. The connecting thread is that governance infrastructure (whether for AI models or institutional data) is becoming the prerequisite for the next wave of capability. For Brett's work at UCSD, the ULM/Scaffold DataX case study offers a concrete reference point for the kind of data governance modernization needed before AI analytics can deliver on its promise.