As artificial intelligence becomes more deeply integrated into businesses and everyday life, concerns over its security are growing just as quickly. To address these challenges, NVIDIA and a group of leading technology companies have launched the Open Secure AI Alliance, an industry initiative focused on developing open-source tools and shared standards to make AI systems safer, more secure and more trustworthy.
The alliance seeks to accelerate the development of transparent security frameworks, testing tools and best practices that help organisations deploy AI responsibly while strengthening trust in increasingly capable AI systems.
According to NVIDIA, the initiative is founded on the belief that open collaboration can help identify vulnerabilities faster, improve defensive capabilities and establish common standards across the AI ecosystem.
The founding members include a broad cross-section of the technology industry, spanning AI infrastructure, enterprise software, cybersecurity and cloud computing.
Among the alliance’s priorities are the development of open evaluation frameworks, red-teaming tools, vulnerability disclosure practices and other resources that enable developers to assess and strengthen AI systems against emerging threats. The initiative also encourages greater cooperation between industry, academia and the open-source community to address evolving security challenges.
The launch comes amid growing debate over the role of open AI models in cybersecurity and follows heightened industry attention on AI-related security risks. NVIDIA and its partners argue that open security tools can complement proprietary AI systems by giving defenders broader access to shared research and proven safeguards.
Imagine this: You are a student in a classroom looking at the class blackboard when suddenly, Albert Einstein appears on it and teaches you aspects of his Theory of Relativity. Fiction? No, it’s real. (Only, Einstein is AI created.)
A Chinese company, “iFLYTEK” showcased its AI Blackboard at the recent 2026 World Artificial Intelligence Conference (WAIC), presenting the AI school blackboard device as part of its AI-powered education portfolio designed for classroom teaching.
The AI Blackboard combines a traditional writing surface with artificial intelligence (AI) features intended to support classroom instruction.
According to iFLYTEK, this is no ordinary blackboard but “an intelligent hub integrated into the entire teaching process”.
Here are some of its features:
Handwritten content on the board can be recognized and converted into standardized graphics, while the system can identify related knowledge points and recommend teaching resources.
In mathematics lessons, the AI Blackboard can transform two-dimensional geometric figures into three-dimensional models for classroom display.
The system also includes AI virtual human technology that allows students to interact with AI representations of historical and cultural figures, including Confucius, Albert Einstein and the Tang Dynasty poet Li Bai.
Teachers can also customize their own “digital clones,” replicating their voices with a single sentence to provide personalized Q&A for students after class, making individualized teaching a reality rather than just a concept.
It can even be used to remotely conduct a class in another classroom thousands of miles away, and also eliminates language barriers.
Separately, teachers can create AI-powered digital avatars using voice-cloning technology to answer students’ questions after class.
Background
iFLYTEK is a Chinese artificial intelligence company founded in 1999 and headquartered in Hefei, Anhui Province. The company specializes in speech recognition, natural language processing, machine translation, and other AI technologies, with products and services spanning education, healthcare, smart cities, finance, and enterprise applications.
In education, iFLYTEK develops AI-powered learning platforms, digital classroom technologies, and teaching tools for schools and universities. Its education portfolio includes smart classrooms, AI-assisted teaching systems, language learning solutions, and interactive devices such as the AI Blackboard.
The company has expanded its presence in China’s education sector through partnerships with schools and local governments, while also promoting selected education technologies in international markets.
For classroom documentation, the AI Blackboard uses a “4+1” camera system and AI audio processing to automatically record lessons. The system can generate lesson records and produce short instructional video clips from classroom sessions, according to the company.
iFLYTEK said the AI Blackboard has been deployed in all 33 provincial-level administrative regions in China and is used in more than 1,400 counties and districts. The company’s announcement also highlighted a demonstration in which the system supported a joint lesson between a school in Zhejiang Province and a school in Indonesia using AI capabilities to facilitate cross-language communication.
A group of 29 countries has signed an agreement to establish the World AI Cooperation Organization, a China-backed intergovernmental body aimed at promoting international cooperation and governance in artificial intelligence (AI).
The agreement, signed in Shanghai ahead of the World AI Conference, marks the most significant institutional effort yet by Beijing to shape the rules governing AI development at a global level.
While the organisation is framed as a platform for collaboration, its broader significance lies in the emerging contest over who sets global AI standards.
The move positions China as an alternative centre of influence to the United States and its allies, which have largely pursued AI governance through smaller, like-minded coalitions focused on safety, security and democratic values. Beijing, by contrast, has consistently advocated a more inclusive framework that emphasises technology sharing, state sovereignty and access for developing economies.
The establishment of a permanent institution could deepen this divide by creating parallel governance architectures. Countries in Africa, Asia and Latin America that seek greater access to AI infrastructure and expertise may increasingly align with the China-led framework, potentially giving Beijing greater influence over emerging technical standards, regulatory norms and digital infrastructure investments.
The development also raises questions about the future of international AI regulation. Rather than converging on a single global framework, competing institutions may evolve around different political and economic priorities, mirroring broader strategic competition between Washington and Beijing. That fragmentation could complicate efforts to develop universally accepted rules for frontier AI, cross-border data governance and responsible deployment of advanced systems.
Former OpenAI Chief Technology Officer Mira Murati has taken a major step in challenging the dominance of the artificial intelligence (AI) industry’s biggest players with the release of the first AI model from her startup, “Thinking Machines Lab”.
The company unveiled “Inkling”, an open-weight foundation model designed to give developers greater flexibility to customize AI systems using their own data, rather than relying on proprietary models controlled by companies such as OpenAI and Anthropic.
The model contains 975 billion parameters, but activates only 41 billion during any given task, a design intended to improve efficiency by reducing computing costs without sacrificing performance. Which means: The AI model has a vast amount of knowledge stored inside it, but it doesn’t use all of it every time you ask a question. Instead, it activates only the parts that are relevant to the task at hand. This makes the system faster and cheaper to run while still delivering high-quality answers.
Thinking Machines Lab said its strategy focuses on balancing affordability and adaptability instead of competing solely on raw computing power. Alongside Inkling, the company is promoting “Tinker”, a cloud-based fine-tuning platform that enables developers to customize large AI models without managing complex infrastructure.
Here’s What You Need To Really Know
Unlike fully closed AI systems, Inkling’s open-weight design allows organizations to adapt the model to specialized tasks while retaining greater control over their data and applications.
So what makes Inkling different is that it gives users more freedom. Most popular AI models are like rented apartments. You can use them, but you cannot change how they are built. Inkling is more like buying a house: businesses and developers can modify it to suit their own needs. It is also designed to work more efficiently by using only the parts of its “brain” needed for a task, making it faster and cheaper to run. This means companies can build AI tools tailored to their business without spending as much on computing or depending entirely on big tech firms.
Inkling was trained using text, images, audio and video data and includes safeguards against misuse, including protections against cyberattacks and biological threats. The company also announced that the model was developed using Nvidia hardware under a multiyear partnership with the chipmaker.
With its first model release, Thinking Machines Lab is positioning itself as a key contender in the rapidly evolving AI market, betting that openness, customization and lower costs will appeal to enterprises seeking alternatives to today’s dominant AI platforms.
The global music industry has introduced a new labelling system aimed at helping listeners distinguish between music created entirely by artificial intelligence and songs made by human artists with AI assistance.
The initiative seeks to promote greater transparency as AI-generated content becomes increasingly common on streaming platforms.
The voluntary framework has been developed jointly by leading music industry organisations, including the Recording Industry Association of America (RIAA), the International Federation of the Phonographic Industry (IFPI), the Grammys, SAG-AFTRA, and several independent music associations.
Under the new system, tracks will carry one of two labels: “AI-generated” for songs in which vocals or major instrumental performances are produced by artificial intelligence, and “AI-assisted” for music created primarily by humans using AI tools during production.
The move comes amid growing concerns over the rapid rise of AI-generated music and its impact on artists, copyright, and listener trust. Industry leaders believe the labels will provide consumers with greater clarity about how songs are created while allowing AI to remain a creative tool rather than replacing human artistry.
The proposed labels are expected to function similarly to existing explicit-content warnings on streaming services. However, implementation will rely on voluntary disclosures by artists, record labels, and distributors.
Some platforms have already begun adopting similar measures. Apple Music has introduced AI transparency tags, while Spotify allows artists to disclose AI involvement through song credits.
The organisations behind the initiative said the framework is designed to evolve alongside advances in AI technology.
Artificial intelligence (AI) is moving beyond threat detection and into continuous cybersecurity monitoring.
EY India is launching an AI-powered Cyber Performance Management platform that aims to help enterprises measure cyber risk in real time rather than relying on periodic security reviews.
What this indicates is a major shift in enterprise security, where AI is increasingly being used to monitor, analyse and prioritise cyber threats across complex digital environments.
The new platform, said the company, brings together data from an organization’s cybersecurity tools to provide a “live” view of its security posture.
Instead of presenting security teams with thousands of alerts, the AI engine evaluates vulnerabilities, identifies the most critical risks and links them to potential business impact. This allows security teams and business leaders to understand which threats require immediate attention.
Traditional cybersecurity programmes often depend on manual assessments and periodic audits, making it difficult to keep pace with rapidly evolving threats. EY’s platform is designed to replace that approach with continuous AI-driven monitoring that can detect changes in an organisation’s risk profile as they happen.
The company said the platform combines cybersecurity posture, threat exposure, detection and response into a single system. AI analyses large volumes of security data, helping organisations quantify cyber risk in financial and operational terms rather than relying solely on technical metrics. This gives executives a clearer understanding of how cyber incidents could affect business operations, compliance and revenue.
The launch comes as enterprises continue expanding cloud infrastructure, connected devices and AI applications, creating larger attack surfaces that are increasingly difficult to monitor manually. As cyber threats become more sophisticated, organisations are looking to AI to automate routine monitoring, speed up threat prioritisation and improve response times.
The announcement highlights a growing trend in enterprise security, where AI is no longer focused only on identifying attacks but on providing continuous cyber performance monitoring. As businesses face increasing regulatory pressure and more frequent cyber threats, real-time AI monitoring is becoming an essential part of enterprise risk management rather than an optional security upgrade.
It may well prove to be a first in the global corporate world insofar as sheer numbers are concerned.
Cisco will begin deploying personalised AI agents to its entire workforce of around 90,000 employees starting this August, marking one of the largest enterprise-wide AI rollouts to date.
The initiative is aimed at boosting productivity by giving every employee an AI assistant capable of answering questions, automating routine tasks, and routing requests to the most suitable AI model.
According to Cisco Chief Financial Officer Mark Patterson, the company has designed the system to dynamically select the best AI model for each task, helping optimise both performance and costs. Much of the AI infrastructure will also run on Cisco’s own systems to improve control over data and reduce token usage.
The rollout will be accompanied by employee upskilling programmes and is expected to support innovation across functions such as finance and operations. However, Cisco executives acknowledged that adopting AI at this scale will not be without challenges.
But what makes this move historic? Many other companies before Cisco have done this. Like Microsoft and Salesforce.
What makes the announcement notable is its scale. Around 90,000 employees will each get a personalized AI agent. Multiple reports describe it as “one of the largest” enterprise-wide AI agent deployments.
Here’s some good news for website owners globally. Cloudflare has expanded its tools for managing artificial intelligence (AI) web crawlers, giving website owners more control over how AI companies access their content as concerns grow over declining referral traffic and content monetization. The announcement marks the company’s second annual “Content Independence Day” initiative.
The company said its previous approach, which allowed customers to block AI bots with a single setting, did not provide enough flexibility.
Instead, website owners can now distinguish between three categories of AI traffic: Search crawlers that index content for search engines, Agent crawlers that retrieve information on behalf of AI assistants, and Training crawlers that collect data for developing AI models. Customers can choose which categories to allow or block, including those using Cloudflare’s free tier.
Cloudflare also announced new default settings that will take effect on September 15, 2026. For new domains, Training and Agent crawlers will be blocked by default on pages displaying advertisements, while Search crawlers will remain allowed. The company argues that ad-supported pages are designed for human visitors and that unrestricted AI access could undermine publisher revenue. Existing customers will be able to opt out of the new defaults before they are applied.
Another significant change targets AI companies that use a single crawler for multiple purposes. Under the updated system, multi-purpose crawlers will be evaluated based on all of their activities. This means a crawler used for both search indexing and AI training could be blocked if a website owner chooses to restrict training access. Cloudflare said it encourages AI companies to separate their crawlers to improve transparency for publishers.
For enterprise customers, Cloudflare also introduced BotBase, a searchable database of verified bots and automated agents designed to improve visibility into automated traffic. The company said the new controls reflect a broader effort to help publishers balance AI innovation with sustainable business models for online content.
Adobe and LinkedIn have unveiled a global AI training initiative designed specifically for marketing professionals, signaling a growing push to address the widening AI skills gap within the industry.
Announced at Cannes Lions 2026, the program introduces four role-based learning paths tailored to marketers working in digital marketing, content and creative, social and communications, and data and analytics.
The courses will be available in 47 languages and offered free for the first 12 months through LinkedIn Learning, the LinkedIn feed, and Adobe Experience League.
Addressing Growing Skills Gap
The initiative comes as demand for AI expertise in marketing accelerates. According to LinkedIn Economic Graph data shared as part of the announcement, marketing job postings requiring AI literacy have increased by 113% year over year. Despite this growth, only 4% of marketers globally have added AI skills to their LinkedIn profiles, compared with 12% in engineering and product management roles.
For digital marketers, these figures highlight a pressing challenge. As AI becomes embedded across campaign planning, audience targeting, content production, and analytics, professionals who fail to develop AI competencies risk falling behind evolving industry expectations.
Role-specific Learning
Unlike broad AI literacy programs aimed at general business audiences, Adobe and LinkedIn are positioning this initiative as practical training built around marketers’ daily workflows.
The curriculum focuses on use cases such as AI-powered content creation, campaign optimization, audience segmentation, and integrating data into agentic workflows. The companies said the courses were developed using workforce insights from LinkedIn’s Economic Graph and designed to align with how marketers actually work.
The emphasis on short-form learning is also notable. Rather than requiring lengthy certification programs, the courses are structured around bite-sized modules intended to fit into busy schedules.
According to Adobe, the program will evolve continuously with updated content throughout the year to keep pace with rapid changes in AI technologies and marketing applications. Participants who complete the courses will earn LinkedIn Learning certificates that can be displayed on their professional profiles. Adobe will also provide free product trials to support hands-on learning experiences.
Why It Matters
The Adobe and LinkedIn partnership highlights a maturation of AI adoption in marketing. Early conversations focused heavily on the technology itself. The focus is now shifting toward workforce readiness and practical implementation.
As AI reshapes content creation, campaign management, and data-driven decision making, marketers who invest in upskilling may be better positioned to lead transformation efforts within their organizations.
The initiative suggests that AI literacy is rapidly becoming a core marketing competency rather than a specialist skill. For digital marketers navigating an increasingly automated landscape, the message is clear: developing AI expertise is no longer optional. It is becoming a requirement for long-term career growth and organizational success.
After two years of watching enterprises oscillate between AI hype and pilot purgatory, Accenture and Carnegie Mellon University’s Software Engineering Institute (SEI) are betting that the next big challenge isn’t building AI applications; it’s operationalizing them.
The two organizations have unveiled the AI Adoption Maturity Model, a framework designed to help companies assess how prepared they are to scale AI initiatives across their businesses with predictable outcomes.
The announcement signals a growing realization in the industry: deploying a few chatbots or coding assistants isn’t the same thing as becoming an AI-native organization.
For those familiar with software engineering history, the move feels familiar. SEI was instrumental in developing the Capability Maturity Model (CMM) and later CMMI, frameworks that transformed software development from an ad hoc practice into a disciplined engineering function. The new initiative appears to apply that same philosophy to enterprise AI.
The Enterprise AI Reality Check
The timing is notable.
According to research cited by Accenture, 86% of C-suite leaders plan to increase AI spending in 2026, yet only 21% of organizations are redesigning end-to-end processes with AI at the core. Nearly half of executives report that AI has delivered little impact on profits so far.
That disconnect mirrors what many early adopters have observed firsthand. The technology works. The demos impress. The prototypes ship. But scaling AI beyond isolated use cases often exposes deeper organizational issues around governance, data quality, workflows, talent readiness, and engineering discipline.
In other words, the bottleneck increasingly isn’t the models. It’s the organization.
Beyond the Prompt Engineering Era
The AI Adoption Maturity Model evaluates organizations across eight dimensions:
Organizational strategy
Workforce and culture
Workflow re-engineering
Risk and governance
Data
Engineering
Operations
Ecosystem
Rather than focusing solely on technical capabilities, the framework attempts to measure whether an organization has institutionalized the practices necessary to sustain AI initiatives over time.
That’s a significant shift from the first wave of enterprise AI adoption, which often centered on experimentation: standing up proof-of-concepts, testing foundation models, and encouraging employees to use generative AI tools.
The next phase appears to be about repeatability.
As agentic systems become integrated into core business operations, enterprises are discovering that traditional software governance frameworks don’t fully address questions around model evaluation, human oversight, workflow redesign, and organizational accountability.
Why Early Adopters Should Pay Attention
For AI enthusiasts and early adopters, maturity models may sound bureaucratic — more boardroom than breakthrough.
But history suggests otherwise.
Software engineering itself went through a similar transition. What began as an experimental discipline eventually required standards, governance models, testing methodologies, and operational frameworks to support mission-critical systems at scale.
AI appears to be reaching a comparable inflection point.
The organizations succeeding with AI in 2026 are increasingly distinguished not by access to the best models, but by their ability to integrate those models into workflows, manage risk, align incentives, and continuously improve outcomes.
The era of “we have a GPT strategy” may be ending.
The era of AI operations as organizational capability is beginning.
