European Commission officials are examining whether Meta’s policy on access to WhatsApp for AI providers may raise competition concerns in the European Economic Area.
Changes to the WhatsApp Business Solution terms are at the centre of the investigation, particularly as they affect how third-party AI providers can offer services on the platform. The Commission is assessing whether the policy could limit access for competing AI services and reduce choice for users and businesses.
Messaging platforms are becoming important distribution channels for AI-powered services. As chatbots and AI assistants become more integrated into everyday communication tools, access to widely used platforms such as WhatsApp may become an important factor in competition between providers.
Commission officials have said they will examine whether Meta’s conduct complies with the EU competition rules. Opening an investigation does not mean that the Commission has reached a conclusion or found an infringement.
The broader EU scrutiny of large digital platforms is increasingly focused on how access to infrastructure, services and user ecosystems is managed as AI tools become more widely adopted.
Why does it matter?
Competition questions are expanding into AI distribution channels. Messaging platforms can shape which AI services reach users and businesses at scale, making access rules an important part of the emerging AI market. The outcome could influence how major platforms design access policies for third-party AI providers while regulators seek to preserve competition and user choice.
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The UK has brought into force regulations requiring the Information Commissioner to prepare a code of practice on the processing of personal data in relation to AI and automated decision-making.
The Data Protection Act 2018 (Code of Practice on Artificial Intelligence and Automated Decision-Making) Regulations 2026 were made on 16 April, laid before Parliament on 21 April, and came into force on 12 May. The regulations apply across England and Wales, Scotland and Northern Ireland.
Under the regulations, the Information Commissioner must prepare a code giving guidance on good practice in the processing of personal data under the UK GDPR and the Data Protection Act 2018 when developing and using AI and automated decision-making systems.
The code must also include guidance on good practice in the processing of children’s personal data. Automated decision-making is defined by reference to provisions in the UK GDPR and the Data Protection Act 2018 inserted through the Data (Use and Access) Act 2025.
The instrument also modifies the panel requirements for preparing or amending the code. Any panel established to consider the code must not consider or report on aspects relating to national security.
The explanatory note states that no full impact assessment was prepared for the instrument because the regulations themselves are not expected to have a significant impact on the private, voluntary or public sectors. The Information Commissioner must produce an impact assessment when preparing the code.
Why does it matter?
The regulations move UK guidance on AI, automated decision-making and personal data onto a statutory track. The eventual code could become an important reference point for organisations using AI systems that process personal data, particularly where automated decisions or children’s data are involved. For now, the main development is procedural: the Information Commissioner is required to prepare the code, while the practical compliance details will follow through that process.
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Updated European Commission guidelines on the ethical use of AI and data in teaching and learning aim to help teachers and school leaders use the technology safely and responsibly in line with EU values.
The revised edition updates the Commission’s 2022 guidance to reflect the rapid growth of generative AI in education and the implications of the EU AI Act. The document is non-binding and is intended to support teachers, school leaders and education authorities, rather than serve as enforcement guidance on the AI Act.
AI tools can support lesson planning, personalised learning, assessment, feedback, school administration and the early identification of learning needs, according to the guidelines. At the same time, they warn that general-purpose AI tools were not designed specifically for education and may lack appropriate safeguards.
Ethical and legal considerations should not be treated as an add-on to AI use in schools, but as fundamental to how the technology is understood, adopted and applied, the Commission says. The guidelines highlight risks linked to bias, privacy, lack of transparency, over-reliance, academic integrity and the use of student data by commercial technology providers.
Rules under EU AI Act and the General Data Protection Regulation are also explained in the document. Some AI systems used for admissions, grading, behavioural monitoring, student progress tracking or detecting prohibited behaviour during tests may be classified as high-risk, while emotion recognition systems are prohibited in educational settings except for medical or safety-related reasons.
Key ethical considerations identified in the guidelines include human dignity, fairness, trustworthiness, academic integrity and justified choice. They also provide guiding questions for teachers and schools on human oversight, transparency, explainability, diversity, inclusion, privacy, safety and accountability.
Executive Vice-President Roxana Mînzatu says the ethical use of AI must remain the guiding principle and that teachers are ‘uniquely placed to act as ethical guardians for their students’. The Commission frames the update as part of wider EU work on digital education, skills, AI literacy and the future of education systems in the age of AI.
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The Center for Security and Emerging Technology (CSET), a policy research organisation within Georgetown University’s Walsh School of Foreign Service, has published an English translation of China’s draft trial measures on ethics reviews for AI technology.
The translated draft says the measures would apply to AI-related scientific and technological activities conducted within China that may pose ethical risks to human health, human dignity, the ecological environment, public order, or sustainable development. It covers universities, research institutions, medical and health institutions, enterprises, and other organisations involved in AI research and development.
Under the draft, organisations with the necessary conditions would be expected to establish AI technology ethics committees, while others could commission specialised ethics service centres to conduct reviews. Review applications would need to include details on the AI activity, algorithms, data sources, data cleaning methods, testing and evaluation, expected applications, user groups, risk assessments, and risk prevention plans.
The review process would focus on fairness and impartiality; controllability and trustworthiness; transparency and explainability; accountability and traceability; and whether the activity has scientific and social value. Committees or service centres would generally have 30 days to approve, reject, or request revisions to an application.
Higher-risk activities would require expert reconsideration. The draft list includes human-computer fusion systems that strongly affect behaviour, psychological or emotional states, or health; AI models and systems able to mobilise public opinion or channel social consciousness; and highly autonomous automated decision-making systems used in safety or personal health-risk scenarios.
Approved AI activities would also be subject to follow-up reviews, generally at intervals of no more than 12 months, while activities requiring expert reconsideration would be subject to follow-up reviews at least every 6 months. Emergency ethics reviews would normally have to be completed within 72 hours.
CSET notes that China released a final trial version of the regulation in April 2026, which it is now translating. The newly published draft translation therefore provides insight into the regulatory structure that preceded the final version, including committee-based ethics review, external service centres, expert reconsideration, and oversight roles for the Ministry of Science and Technology, the Ministry of Industry and Information Technology, and other departments.
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Australia’s New South Wales state has clarified that creating, sharing, or threatening to share sexually explicit images, videos, or audio of a person without consent is a criminal offence, including where the material has been digitally altered or generated using AI.
The state government strengthened protections in 2025 by amending the Crimes Act 1900 to cover digitally generated deepfakes. The law already applied to sexually explicit image material, but now also covers content created or altered by AI to place someone in a sexual situation they were never in.
The reforms mean that non-consensual sexual images or audio are covered regardless of how they were made. Threatening to create or share such material is also a criminal offence in New South Wales, with penalties of up to three years in prison, a fine of up to A$11,000, or both.
Courts can also order offenders to remove or delete the material. Failure to comply with such an order can result in up to 2 years’ imprisonment, a fine of up to A$5,500, or both.
The law operates alongside existing child abuse material offences. Under criminal law, any material depicting a person under 18 in a sexually explicit way can be treated as child abuse material, including AI-generated content.
Criminal proceedings against people under 16 can begin only with the approval of the Director of Public Prosecutions, which is intended to ensure that only the most serious matters involving young people enter the criminal justice system.
Limited exemptions apply for proper purposes, including genuine medical, scientific, law enforcement, or legal proceedings-related purposes. A review of the law will take place 12 months after it comes into effect to assess how it is working and whether changes are needed.
The changes are intended to address the misuse of AI and deepfake technology to harass, shame, or exploit people through fake digital content. New South Wales says its criminal law works alongside national online safety frameworks, including the work of Australia’s eSafety Commissioner, as It seeks to keep privacy and consent protections aligned with emerging technologies.
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The platform includes Claude Managed Agents, code execution, web search, web fetch, prompt caching, batch processing, citations, support for the Files API, and support for Skills and MCP connectors. Anthropic said new Claude models and beta tools will become available on AWS at the same time they launch on the native Claude API.
Authentication runs through AWS Identity and Access Management, while audit logging is handled through AWS CloudTrail and billing through a single AWS invoice. Anthropic said the service is designed for organisations seeking native Claude Platform functionality while staying within existing AWS credentials, permissions and operational workflows.
The company also clarified the distinction between Claude Platform on AWS and Claude on Amazon Bedrock. Under the new platform, Anthropic operates the service and data is processed outside the AWS boundary.
By contrast, Claude on Amazon Bedrock keeps AWS as the data processor and operates within the AWS boundary, making it more suitable for customers with strict regional data residency requirements or those needing data processed exclusively within AWS infrastructure.
Why does it matter?
The launch shows how competition between major AI providers is shifting towards enterprise deployment, cloud integration and agent-based automation. For organisations, the choice is no longer only about model performance, but also about where data is processed, how access is controlled, how audit logs are handled and whether AI agents can be deployed within existing cloud governance systems.
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With the rapid expansion of AI technologies, agentic AI is rapidly moving from experimentation to deployment on a scale larger than ever before. As a result, these systems have been given far greater autonomy to perform tasks with limited human input, much to the delight of enterprise magnates.
Companies such as Microsoft, Google, Anthropic, and OpenAI are increasingly developing agentic AI systems capable of automating vulnerability detection, incident response, code analysis, and other security tasks traditionally handled by human teams.
The appeal of using agentic AI as a first line of defence is palpable, as cybersecurity teams face mounting pressure from the growing volume of attacks. According to the Microsoft Digital Defense Report 2025, the company now detects more than 600 million cyberattacks daily, ranging from ransomware and phishing campaigns to identity attacks. Additionally, the International Monetary Fund has also warned that cyber incidents have more than doubled since the COVID-19 pandemic, potentially triggering institutional failures and incurring enormous financial losses.
To add insult to injury, ransomware groups such as Conti, LockBit, and Salt Typhoon have shown increased activity from 2024 through early 2026, targeting critical infrastructure and global communications, as if aware of the upcoming cybersecurity fortifications and using a limited window of time to incur as much damage as possible.
In such circumstances, fully embracing agentic AI may seem like an ideal answer to the cybersecurity challenges looming on the horizon. Systems capable of autonomously detecting threats, analysing vulnerabilities, and accelerating response times could significantly strengthen cyber resilience.
Yet the same autonomy that makes these systems attractive to defenders could also be exploited by malicious actors. If agentic AI becomes a defining feature of cyber defence, policymakers and companies may soon face a more difficult question: how can they maximise its benefits without creating an entirely new layer of cyber risk?
Why cybersecurity is turning to agentic AI
The growing interest in agentic AI is not simply driven by the rise in cyber threats. It is also a response to the operational limitations of modern security teams, which are often overwhelmed by repetitive tasks that consume time and resources.
Security analysts routinely handle phishing alerts, identity verification requests, vulnerability assessments, patch management, and incident prioritisation — processes that can become difficult to manage at scale. Many of these tasks require speed rather than strategic decision-making, creating a natural opening for AI systems to operate with greater autonomy.
Microsoft has aggressively moved into this space. In March 2025, the company introduced Security Copilot agents designed to autonomously handle phishing triage, data security investigations, and identity management. Rather than replacing human analysts, Microsoft positioned the tools to reduce repetitive workloads and enable security teams to focus on more complex threats.
Google has approached the issue through vulnerability research. Through Project Naptime, the company demonstrated how AI systems could replicate parts of the workflow traditionally handled by human security researchers by identifying vulnerabilities, testing hypotheses, and reproducing findings.
Anthropic introduced another layer of complexity through Claude Mythos, a model built for high-risk cybersecurity tasks. While the company presented the model as a controlled release for defensive purposes, the announcement also highlighted how advanced cyber capabilities are becoming increasingly embedded in frontier AI systems.
Meanwhile, OpenAI has expanded partnerships with cybersecurity organisations and broadened access to specialised tools for defenders, signalling that major AI firms increasingly view cybersecurity as one of the most commercially viable applications for autonomous systems.
Together, these developments show that agentic AI is gradually becoming embedded in the cybersecurity infrastructure. For many companies, the question is no longer whether autonomous systems can support cyber defence, but how much responsibility they should be given.
When agentic AI tools become offensive weapons
The same capabilities that make agentic AI valuable to defenders also make it attractive to malicious actors. Systems designed to identify vulnerabilities, analyse code, automate workflows, and accelerate decision-making can be repurposed for offensive cyber operations.
Anthropic offered one of the clearest examples of that risk when it disclosed that malicious actors had used Claude in cyber campaigns. The company said attackers were not simply using the model for basic assistance, but were integrating it into broader operational workflows. The incident showed how agentic AI can move cyber misuse beyond advice and into execution.
The risk extends beyond large-scale cyber operations. Agentic AI systems could make phishing campaigns more scalable, automate reconnaissance, accelerate vulnerability discovery, and reduce the technical expertise needed to launch certain attacks. Tasks that once required specialist teams could become easier to coordinate through autonomous systems.
Security researchers have repeatedly warned that generative AI is already making social engineering more convincing through realistic phishing emails, cloned voices, and synthetic identities. More autonomous systems could further push those risks by combining content generation with independent action.
The concern is not that agentic AI will replace human hackers. Cybercrime could become faster, cheaper, and more scalable, mirroring the same efficiencies that organisations hope to achieve through AI-powered defence.
The agentic AI governance gap
The governance challenge surrounding agentic AI is no longer theoretical. As autonomous systems gain access to internal networks, cloud infrastructure, code repositories, and sensitive datasets, companies and regulators are being forced to confront risks that existing cybersecurity frameworks were not designed to manage.
Policymakers are starting to respond. In February 2026, the US National Institute of Standards and Technology (NIST) launched its AI Agent Standards Initiative, focused on identity verification and authentication frameworks for AI agents operating across digital environments. The aim is simple but important: organisations need to know which agents can be trusted, what they are allowed to do, and how their actions can be traced.
Governments are also becoming more cautious about deployment risks. In May 2026, the Cybersecurity and Infrastructure Security Agency (CISA) joined cybersecurity agencies from Australia, Canada, New Zealand, and the United Kingdom in issuing guidance on the secure adoption of agentic AI services. The warning was clear: autonomous systems become more dangerous when they are connected to sensitive infrastructure, external tools, and internal permissions.
The private sector is adjusting as well. Companies are increasingly discussing safeguards such as restricted permissions, audit logs, human approval checkpoints, and sandboxed environments to limit the degree of autonomy granted to AI agents.
The questions facing businesses are becoming practical. Should an AI agent be allowed to patch vulnerabilities without approval? Can it disable accounts, quarantine systems, or modify infrastructure independently? Who is held accountable when an autonomous system makes the wrong decision?
Agentic AI may become one of cybersecurity’s most effective defensive tools. Its success, however, will depend on whether governance frameworks evolve quickly enough to keep pace with the technology itself.
How companies are building guardrails around agentic AI
As concerns around autonomous cyber systems grow, companies are increasingly experimenting with safeguards designed to prevent agentic AI from becoming an uncontrolled risk. Rather than granting unrestricted access, many organisations are limiting what AI agents can see, what systems they can interact with, and what actions they can execute without human approval.
Anthropic has restricted access to Claude Mythos over concerns about offensive misuse, while OpenAI has recently expanded its Trusted Access for Cyber programme to provide vetted defenders with broader access to advanced cyber tools. Both approaches reflect a growing consensus that powerful cyber capabilities may require tiered access rather than unrestricted deployment.
The broader industry is moving in a similar direction. CrowdStrike has increasingly integrated AI-driven automation into threat intelligence and incident response workflows while maintaining human oversight for critical decisions. Palo Alto Networks has also expanded its AI-powered security automation tools designed to reduce response times without fully removing human analysts from the decision-making process.
Cloud providers are also becoming more cautious about autonomous access. Amazon Web Services, Google Cloud, and Microsoft Azure have increasingly emphasised zero-trust security models, role-based permissions, and segmented access controls as enterprises deploy more automated tools across sensitive infrastructure.
Meanwhile, sectors such as finance, healthcare, and critical infrastructure remain particularly cautious about fully autonomous deployment due to the potential consequences of false positives, accidental shutdowns, or disruptions to essential services.
As a result, security teams are increasingly discussing safeguards such as audit logs, sandboxed environments, role-based permissions, staged deployments, and human approval checkpoints to balance speed with accountability. For now, many companies seem ready to embrace agentic AI, but without keeping one hand on the emergency brake.
The future of cybersecurity may be agentic
Agentic AI is unlikely to remain a niche experiment for long. The scale of modern cyber threats, combined with the mounting pressure on security teams, means organisations will continue to look for faster and more scalable defensive tools.
That shift could significantly improve cybersecurity resilience. Autonomous systems may help organisations detect threats earlier, reduce response times, address workforce shortages, and manage the growing volume of attacks that human teams increasingly struggle to handle alone.
At the same time, the technology’s long-term success will depend as much on restraint as on innovation. Without clear governance frameworks, operational safeguards, and human oversight, the same tools designed to strengthen cyber defence could introduce entirely new vulnerabilities.
The future of cybersecurity may increasingly belong to agentic AI. Whether that future becomes safer or more volatile may depend on how responsibly governments, companies, and security teams manage the transition.
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The US Economic Development Administration has announced approximately $25 million in funding for a new AI Upskill Accelerator Pilot Program to support AI workforce training.
The programme will fund industry-driven partnerships that design and implement AI training models for workers and businesses in sectors considered important to regional economies. EDA says the initiative is intended to support workforce development approaches that can scale, adapt and become self-sustaining as AI technologies continue to evolve.
The funding opportunity links the programme to the Trump administration’s 2025 Artificial Intelligence Action Plan, which includes goals to accelerate AI development, support adoption across industries and strengthen US leadership in the technology. EDA says the programme is part of efforts to empower American workers to use AI tools and support industries tied to regional growth.
Deputy Assistant Secretary and Chief Operating Officer Ben Page said AI is becoming ‘a core driver of productivity and growth across industries’ and that workers need AI skills so regions can attract investment, adopt advanced technologies and sustain long-term economic growth.
The pilot will support workforce development in an emerging technology area while helping businesses and workers build the skills needed to use AI in the workplace. Applications for the programme are open until 10 July 2026.
Why does it matter?
The programme shows how AI policy is increasingly being linked to regional economic development and workforce readiness, not only research or infrastructure. By funding industry-driven training models, the EDA is trying to prepare workers and local economies for AI adoption while helping businesses close skills gaps that could affect productivity, investment and competitiveness.
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Writing for the World Economic Forum, Avathon CEO Pervinder Johar argues that traditional supply-chain software is struggling to cope with a more volatile operating environment because many systems still rely on rigid rules, static configurations and manual workflows.
The article says the emerging model places greater emphasis on knowledge rather than raw data, combining context and reasoning across suppliers, logistics routes, energy markets and policy environments. AI-native systems are presented as a way to support continuous learning, improve disruption forecasting and help organisations assess alternative responses before problems escalate.
Physical AI is also described as part of the shift, embedding intelligence more directly into operational infrastructure. According to the article, this could allow logistics systems, equipment and connected assets to sense, compute and coordinate responses more quickly across supply-chain networks.
As automation expands, human roles are expected to move towards strategic oversight. Supply-chain professionals may spend less time managing dashboards and exceptions, and more time setting priorities, weighing trade-offs and guiding AI agents through intent expressed in natural language.
The broader argument is that supply-chain management is moving from reactive workflows towards more adaptive coordination, where systems can anticipate disruption, assess options and support decisions across organisations and partners.
Why does it matter?
Supply chains are facing persistent disruption from geopolitical tensions, climate risks, logistics bottlenecks and changing market conditions. If AI-enabled systems can improve forecasting, coordination and response, they could help companies build more resilient operations. However, the shift also raises governance questions around accountability, human oversight, data quality and reliance on automated decision-making across critical trade and logistics networks.
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Immigration, Refugees and Citizenship Canada has released its first AI Strategy, outlining how the department plans to use AI across immigration, citizenship, refugee, passport and settlement services while maintaining human oversight, privacy protection and accountability.
The strategy aligns with Canada’s AI Strategy for the Federal Public Service 2025-2027 and frames AI as a tool to improve service delivery, reduce administrative burdens, strengthen programme integrity and respond to fraud and cybersecurity threats. IRCC says its approach is based on responsible adoption, governance, workforce readiness, transparency and public engagement.
The department says it has used advanced analytics and machine learning since 2018 to support application triage, workload distribution and risk detection. It says machine learning can help identify straightforward, low-risk files for expedited officer review, while outcomes remain subject to officer verification.
IRCC states that it does not use autonomous AI agents or intelligent automation systems that can refuse client applications. It says systems that learn and adapt independently are generally unsuitable for administrative decision-making because their logic can be difficult to explain or reproduce.
The strategy identifies several areas of interest, including client service, fraud detection, document anomaly detection, settlement support, data analysis, accessibility and internal knowledge management. IRCC is also experimenting with AI tools for tasks such as document fraud detection, anomaly detection and support for administrative processes.
Privacy is presented as a central guardrail. IRCC says AI systems must use only the minimum personal information necessary for specific, justified purposes, and must include privacy assessments, mitigation measures, testing, auditing and Canadian-controlled environments for sensitive information. The department also says it will avoid black-box AI models for application decisions and keep AI systems explainable, supervised, secure and regularly tested.
The strategy sets five implementation priorities: establishing an AI Centre of Expertise, strengthening governance, building an AI-ready workforce, accelerating experimentation and developing an engagement strategy with employees, clients, vulnerable groups and partner organisations. IRCC describes the strategy as a living document that will evolve with domestic and international AI policy developments.
Why does it matter?
Immigration decisions can have life-changing consequences, making AI use in this field especially sensitive. IRCC’s strategy shows how governments are trying to use AI to improve efficiency and detect risks while drawing limits around autonomous decision-making, black-box models and the handling of personal information. The real test will be whether safeguards around human oversight, explainability, privacy and bias are strong enough as AI becomes more embedded in public administration.
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