Opening of the session
Opening statements
– Under-Secretary-General and High Representative for Disarmament
Affairs, Ms. Izumi Nakamitsu (pre-recorded)
– H.E. Ambassador Egriselda López, Chair of the Global Mechanism
on developments in the field of information and communications
technologies in the context of international security and advancing
responsible State behaviour in the use of information and
communications technologies
Organization of work
Cybersecurity
Digital Omnibus on AI: The EU’s AI Act simplification and new AI Office powers
On 29 June 2026, the Council of the European Union gave its final green light to the Digital Omnibus on AI, a package of amendments that eases and delays parts of the EU AI Act, completing a legislative procedure that began when the European Commission published its proposal on 19 November 2025. It amends the EU AI Act, together with the EU’s civil aviation rules and machinery regulation. According to the European Parliament’s Legislative Observatory, the final act was signed on 8 July 2026, and the Digital Omnibus is now awaiting publication in the Official Journal of the European Union, a necessary step before it can enter into force, ahead of the original 2 August 2026 deadline for several high-risk AI obligations.
Much of the public attention on the Digital Omnibus has focused on the delay to high-risk AI rules and the new ban on AI-generated intimate imagery. The full legal text of the amending regulation also reorganises, in detail, responsibility for supervising AI systems that operate within very large online platforms regulated under the Digital Services Act, and amends several other elements of the way the AI Act is enforced, points that have drawn less attention so far.
The Council describes this regulation as part of a wider legislative package known as Omnibus VII, one of several ‘omnibus’ simplification efforts the Commission has proposed across different policy areas. It was also listed in the Parliament and the Council in their Joint Declaration on EU legislative priorities for 2026, signalling the priority both institutions attached to its rapid finalisation.
Why the Commission proposed the amendments

According to the recitals of the Digital Omnibus on AI, the amendments respond to problems identified once parts of the AI Act began to apply in August 2024. The recitals point to delays in the preparation of harmonised technical standards needed by providers of high-risk AI systems in order to demonstrate compliance, as well as delays by several member states in setting up the national authorities and conformity assessment bodies responsible for checking that compliance. Taken together, the recitals state that these delays created a heavier compliance burden than originally expected.
The Commission’s proposal also links the amendments to a broader competitiveness rationale, describing them as part of a wider effort by EU leaders to reduce administrative burdens on business, following the recommendations of the Draghi and Letta reports on European competitiveness. Industry associations also lobbied for the amendments throughout 2025.
The trade group DIGITALEUROPE told policymakers that compliance with the AI Act could cost companies in the region of EUR 3.3 billion a year across the EU, and that a company of around 50 employees developing an AI-based product could face initial compliance costs of between EUR 320,000 and EUR 600,000.
How the Digital Omnibus was negotiated

The AI-specific amendments were separated from the wider Digital Omnibus package, which also proposes amendments to the GDPR, the ePrivacy Directive, the Data Act, and the NIS2 Directive on cybersecurity, due to the approaching deadline for high-risk AI obligations. According to the Legislative Observatory’s procedure record, Parliament’s Internal Market Committee voted on the proposed regulation on 18 March 2026, and the Parliament adopted its first-reading position on 26 March 2026.
The Parliament and the Council negotiators reached a political agreement on the Digital Omnibus early on 7 May 2026. The Council’s Permanent Representatives Committee confirmed the agreement in a letter dated 13 May 2026. The Parliament formally adopted the Digital Omnibus on 16 June 2026, the Council gave its final approval on 29 June 2026, and the final act was signed on 8 July 2026.
The regulation’s preamble records that the European Central Bank was consulted and issued a formal opinion, published in the Official Journal in April 2026, as required under EU legislation for measures affecting payments and financial infrastructure. The European Economic and Social Committee delivered its opinion on 18 March 2026, and the Committee of the Regions gave its opinion on 7 May 2026. National parliaments, including those of Czechia, Italy, the Netherlands, Portugal, Romania, Germany, Poland and France, also submitted subsidiarity contributions during the process. The Parliament’s public transparency register separately records meetings on this regulation between the two co-rapporteurs and organisations, including Google, the AI start-up Mistral AI, the digital rights group EDRi, the privacy group noyb, and the standards and conformity body TIC Council, reflecting the range of interests, from large technology firms to civil society, that engaged with the negotiations.
New deadlines for high-risk AI obligations
Under the amended Article 113 of the AI Act, the obligations for high-risk AI systems set out in Sections 1 to 3 of Chapter III will now apply from 2 December 2027 for systems classified as high-risk under Article 6(2) and Annex III, which covers areas such as biometrics, critical infrastructure, education, employment, law enforcement, migration and border management. For systems classified as high-risk under Article 6(1) and Annex I, meaning AI systems embedded in products already covered by other EU safety legislation, such as machinery or medical devices, the new deadline is 2 August 2028. Both deadlines were originally set for 2 August 2026.
A separate provision clarifies how the AI Act’s grace period for so-called legacy systems, set out in Article 111(2), applies. Once at least one unit of a given type and model of high-risk AI system has been lawfully placed on the market before the relevant cut-off date, further units of the same type and model can continue to be placed on the market or put into service without additional certification, as long as the system’s design does not change significantly. Any significant redesign after the cut-off date triggers full compliance with the AI Act, including conformity assessment.
To help providers meet the new deadlines, the Digital Omnibus requires the Commission to request that European standardisation bodies develop technical standards aligned with existing product-safety standards, reducing duplication for companies that have to comply with both the AI Act and sectoral legislation. The Commission must also publish guidance on post-market monitoring plans by 2 September 2027, as well as guidance to help providers of Annex I high-risk systems apply the AI Act alongside sectoral rules by 1 August 2027. Watermarking obligations for AI-generated content, which allow such content to be detected and traced, benefit from a separate four-month transitional period for systems already on the market before 2 August 2026.
Changes to AI literacy and the use of sensitive data for bias correction

A further amendment loosens the AI Act’s AI literacy obligation. Instead of requiring providers and deployers to ensure a sufficient level of AI literacy among their staff, the amended Article 4 requires them to take measures supporting the development of that literacy among staff and other people involved in the operation of their AI systems. The European Artificial Intelligence Board is tasked with adopting recommendations that set common objectives to guide how the Commission and member states support this obligation.
A new Article 4a allows providers and deployers of AI systems to process special categories of personal data, such as data revealing ethnicity or health status, for the specific purpose of detecting and correcting bias, subject to a list of privacy safeguards, including data minimisation, restrictions on transferring the data to third parties, and deletion once the bias has been corrected. The final text requires this processing to be strictly necessary, a stricter standard than the version originally proposed by the Commission. This followed a joint opinion issued by the European Data Protection Board and the European Data Protection Supervisor in January 2026, which recommended reinstating the stricter standard.
AI Office gains exclusive powers over general-purpose AI and large platforms

Article 75 of the AI Act, which governs the market surveillance of AI systems, has been substantially rewritten. Under the new provisions, the Commission’s AI Office becomes exclusively responsible for supervising two categories of AI systems. The first category comprises AI systems built on general-purpose AI models, where the same provider, or providers belonging to the same undertaking, developed both the underlying model and the AI system built on it. This exclusive competence carries several exceptions. It does not apply to AI systems related to products already covered by EU product-safety legislation, AI systems used as critical infrastructure, systems provided by law enforcement authorities, border management authorities or financial institutions in specific circumstances, or certain systems used in the administration of justice, all of which remain under national supervision.
The second category covers AI systems that constitute, or are integrated into, a very large online platform or a very large online search engine designated under the Digital Services Act (DSA), the EU’s rulebook for online platforms. The recitals state that empowering the Commission, through the AI Office, to act as a market surveillance authority for these systems is intended to ensure that enforcement of the AI Act and the DSA is carried out consistently, given the scale and potential societal impact of very large platforms and search engines.
For AI systems that are embedded in, or form part of, a designated very large platform or search engine, the Digital Omnibus specifies that the DSA’s own risk assessment, mitigation, and audit obligations, laid down in Articles 34, 35, and 37 of that regulation, serve as the first point of entry for assessing the AI system. This is without prejudice to the AI Office’s separate power to investigate and enforce breaches of the AI Act after the fact. The Commission services that enforce the DSA and the AI Office are required to coordinate, exchange views regularly, and take account of any fines already imposed on the same company for the same conduct, so that the combined penalties remain proportionate and do not amount to double punishment for the same infringement.
Outside this narrower platform-related category, national market surveillance authorities retain a role. Where a national authority has well-founded reasons to suspect that a provider or deployer of an AI system under the AI Office’s exclusive competence has breached the AI Act, it may ask the AI Office, through a designated national contact point, to investigate. The AI Office must tell that authority within four months whether it intends to act, and keep it informed of major developments and the eventual outcome.
The recitals acknowledge that taking on this expanded role will require the AI Office to be adequately staffed and resourced. Whether the Commission allocates sufficient capacity for the AI Office to supervise both general-purpose AI models and large platforms is an operational question that will only become clear as implementation proceeds, rather than one resolved by the legislation itself.
New ban on AI-generated intimate imagery and child sexual abuse material

The Digital Omnibus amends Article 5 of the AI Act, which lists AI practices that are prohibited outright. It adds a prohibition against placing on the market, putting into service, or using AI systems that generate or manipulate realistic images, video or audio of an identifiable person’s intimate parts, or of that person engaged in sexually explicit activity, without that person’s free, specific, informed and unambiguous consent. It adds a parallel prohibition covering AI systems that generate or manipulate child sexual abuse material, subject to a narrow exception for activities that are lawful under national law, such as material generated by law enforcement authorities for the purposes of criminal investigation.
For providers, the prohibition applies in two situations: where generating or manipulating such material is the system’s intended purpose, or where that outcome is a reasonably foreseeable and reproducible result of the system’s design and the provider has not put in place reasonable and adequate safeguards, such as content filtering or abuse-detection mechanisms, to prevent it. For deployers, the prohibition applies only where the AI system is actually used for that purpose, meaning the ordinary use of a lawful system for unrelated purposes is not covered, nor is accidental generation of such content.
The prohibited material is defined narrowly. It covers realistic depictions, meaning a person’s face, voice or body shown in a credible, real-life manner, and specifically named intimate parts or depictions of sexually explicit activity. Cartoonish or physically impossible depictions fall outside the prohibition, as does content generated with the depicted person’s consent, non-realistic artistic nude work that does not depict an identifiable person, and legitimate medical applications such as anatomical simulations. Simple enhancements to existing images, such as adjusting brightness or adding a caption, are not treated as prohibited manipulation unless they increase the level of nudity or explicitness shown. Companies have to ensure that their systems comply with these rules by 2 December 2026.
Other simplification measures
The Digital Omnibus extends several compliance simplifications that previously applied only to small and medium-sized enterprises to a new category of small mid-cap enterprises, companies that have outgrown the SME definition but remain much smaller than large corporations. It also gives all SMEs, including start-ups, the option to comply with parts of the AI Act’s quality management system requirements in a simplified way, an option previously limited to microenterprises.
The deadline for each member state to have at least one operational national AI regulatory sandbox, a controlled environment in which providers can test AI systems under regulatory supervision, has been extended to 2 August 2027. The same provisions allow the AI Office itself to set up an EU-level sandbox for AI systems that fall under its exclusive competence, with priority access for SMEs, start-ups and small mid-cap enterprises, operating alongside, and not instead of, national sandboxes.
A further change moves the EU machinery regulation from one section of the AI Act’s product-safety annex to another, shifting AI-enabled machinery towards a more sector-specific approach. Under the new arrangement, the Commission must adopt delegated acts by 2 August 2028 incorporating the AI Act’s health and safety requirements directly into the machinery regulation, rather than requiring manufacturers to apply both frameworks in parallel.
Data protection authorities raise fundamental rights concerns

Before the political agreement was reached, the European Data Protection Board and the European Data Protection Supervisor issued a joint opinion on the Commission’s initial proposal. The two authorities said they supported the general aim of addressing implementation issues, but raised concerns that several measures could weaken human rights protections built into the AI Act. They warned that extending the legacy systems exception would allow more high-risk AI systems to reach the market without being subject to the Act’s safeguards and urged the co-legislators to keep any delay to transparency obligations as short as possible.
The two authorities also opposed the Commission’s original plan to remove the registration obligation for providers who conclude that their Annex III systems are not high-risk, arguing that this would weaken accountability and make it harder for market surveillance authorities to respond quickly to problem systems. That registration obligation was retained, in a streamlined form, in the Digital Omnibus as finally approved in June. As set out above, the authorities’ recommendation to apply a strict necessity standard to the processing of sensitive data for bias correction was also reflected in the final version of the Digital Omnibus.
Not all of the authorities’ recommendations were taken on board in the same way. Their broader concern, that postponing obligations for high-risk AI systems may leave fundamental rights protections unenforced for longer in a fast-moving technological area, remains a live point of disagreement between the co-legislators and civil society groups, as discussed further below.
Reactions: competitiveness framing meets rights concerns

Council and Parliament negotiators presented the changes as a way to make the AI Act more workable without altering its underlying risk-based structure. Co-rapporteur Arba Kokalari said the agreement showed that politics can move just as quickly as technology, linking the simplification to the Commission’s broader competitiveness agenda. Co-rapporteur Michael McNamara said the deal combined simplification measures with new safeguards against nudification apps and AI-generated child sexual abuse material.
Civil society organisations took a more critical view of the overall direction of the package. The digital rights group Liberties argued that the final agreement weakens several safeguards contained in the original AI Act, and described the postponement of high-risk obligations as a delay to fundamental rights protections that were due to take effect in August 2026.
Industry associations generally welcomed the changes. DIGITALEUROPE, which had been among the most vocal critics of the AI Act’s original compliance costs and timeline, broadly supported the direction of the simplification package, while continuing to call for further alignment between the AI Act and other overlapping EU digital rules.
What happens next
The Digital Omnibus on AI will enter into force once it is published in the Official Journal of the European Union. Until then, the AI Act’s original provisions and timeline remain legally in force, including the prohibitions on unacceptable AI practices and the obligations applicable to general-purpose AI models that have applied since August 2025.
A separate Commission exercise, the Digital Fitness Check, is expected to examine the DSA and the wider digital rulebook directly, with a report on its findings due in the first quarter of 2027 according to legal commentary on the process. That exercise, rather than the AI Omnibus itself, is where the more direct question of simplifying the DSA is likely to be decided and where the institutional link now established between the AI Office and DSA-regulated platforms may be revisited.
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Google open-sources k8s-aibom to detect shadow AI
Google has open-sourced k8s-aibom, a lightweight Kubernetes controller designed to detect unregistered AI workloads and generate standardised inventories of the AI models, runtimes and frameworks operating inside a cluster.
The tool targets shadow AI: workloads deployed by developers without formal registration or integration with an organisation’s security and governance systems. Such deployments can evade conventional security scanners, particularly where organisations avoid privileged agents, kernel-level access or manual changes to Kubernetes workloads.
Google says k8s-aibom addresses that gap by continuously monitoring Kubernetes APIs and container environments. It detects running AI components and generates CycloneDX 1.6 Machine Learning Bills of Materials (ML-BOMs) based on what is actually executing, rather than what was intended during the build process.
The controller runs as a single unprivileged deployment in the k8s-aibom-system namespace. It does not require sidecars, eBPF modules, privileged DaemonSets or modifications to developers’ continuous integration and deployment pipelines.
The controller monitors KServe resources, deployments, StatefulSets, DaemonSets and jobs across a cluster. It then analyses container images, environment variables and command-line arguments to identify different categories of AI workloads.
Supported systems include inference runtimes such as vLLM, Triton Inference Server, TGI, and Ollama; agent frameworks including LangChain, AutoGen, and CrewAI; retrieval and vector database tools such as Milvus, Qdrant, and pgvector; and distributed training and evaluation workloads.
Once identified, the components are compiled into CycloneDX ML-BOM documents. These records can be stored as Kubernetes custom resources or exported to destinations including Google Cloud Storage and webhook endpoints.
Google also designed the tool to produce identical ML-BOM documents when given identical cluster inputs. This deterministic behaviour is intended to support GitOps workflows, allowing security and reliability teams to compare records and identify changes when AI dependencies drift.
Unlike build-time scanners, which document what organisations intended to deploy, k8s-aibom observes live clusters to identify which AI systems are actually running, how they are connected and how those findings were established.
A confidence model separates detected components into three categories. Declared assets are explicitly specified in workload configurations, inferred assets are identified through runtime patterns, and unresolved assets indicate that an AI presence was detected but the precise model, version, or weights could not be established.
Unresolved findings can therefore be prioritised for further security review, while declared and inferred classifications help auditors distinguish documented engineering intent from conclusions reached by the controller.
Google says the controller follows least-privilege principles and can export records using a dedicated identity with permission to create objects in Cloud Storage. Creation preconditions can prevent existing ML-BOM records from being silently overwritten, strengthening the historical evidence available to security and compliance teams.
Google also positions k8s-aibom as a tool for regulatory and standards compliance. Runtime inventories could help organisations gather evidence relevant to the EU AI Act, the NIST AI Risk Management Framework and ISO/IEC 42001 requirements for AI asset management.
Why does it matter?
Shadow AI has become a growing governance challenge as developers deploy AI tools outside formal security and compliance processes. Without visibility into what is actually running in production, organisations may struggle to assess risk, investigate incidents or demonstrate regulatory compliance.
By generating inventories of live AI workloads rather than relying solely on build-time records, k8s-aibom could help organisations improve AI governance while supporting audits, security operations and compliance with emerging AI standards and regulations.
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AI is beginning to carry out live cyberattacks, Check Point warns
AI is moving beyond assisting cybercriminals to carrying out operational tasks during live intrusions, according to Check Point Research’s Annual AI Security Report 2026.
The report argues that AI-enabled cyber operations are entering a new phase in which AI systems can execute parts of an attack rather than simply helping attackers write code, research targets or prepare phishing campaigns. The shift could make cyber operations faster and less dependent on continuous human oversight.
Check Point said it observed AI carrying out hands-on tasks during incidents ranging from China-linked campaigns to a criminal breach affecting several Mexican government agencies. According to the company, these capabilities are spreading beyond state-backed actors to financially motivated cybercriminals.
AI is also being used to create deployment-ready malware and offensive frameworks. One developer reportedly used an AI coding environment to build VoidLink, an 88,000-line command-and-control framework, in less than a week. Check Point noted that AI involvement may be difficult to identify once the finished tool is deployed.
According to the report, attackers increasingly favour commercial AI models over self-hosted alternatives. Rather than relying solely on jailbreak prompts, some are targeting agentic architectures by planting configuration files that AI agents continue to trust across multiple sessions.
The market supporting AI cyberattacks is also becoming more established. Check Point identified phishing-as-a-service products that embed language models with built-in restrictions bypasses, alongside conversational voice-agent services used for vishing and one-time-password theft.
The report warns that synthetic identities are weakening traditional trust signals. Convincing imitations of voices, faces, identity documents, and live video can now be combined across multiple channels, making social engineering operations more coordinated and harder to detect.
AI systems themselves are also emerging as an important attack surface. Models may struggle to distinguish instructions from the content they process, allowing attackers to manipulate AI agents through malicious files, webpages and other external data sources.
Indirect prompt injection is emerging as one of the most important threats to AI systems. Check Point said detections of longer malicious payloads increased roughly fivefold between March and May 2026, reaching close to 1% of observed prompts. Longer payloads are commonly associated with content-based and agentic attack paths.
Enterprise data leakage through generative AI also remains a growing concern. The share of prompts classified as high risk doubled from 2% to 4% over the previous year, while organisations used an average of ten AI applications each month, including tools that had not received official approval.
Exposure varied considerably by sector. Business services recorded the highest rate of high-risk generative AI prompts, at 5.91%, meaning approximately one in every 17 interactions presented a significant risk of exposing sensitive information.
The findings suggest organisations must prepare for threats from two directions: adversaries using AI to automate cyber operations and employees or AI systems exposing sensitive data through insecure adoption.
Why does it matter?
The report suggests AI is reshaping cybersecurity on both sides of the equation. Attackers are increasingly using AI to automate complex tasks, while organisations adopting AI are creating new attack surfaces and data security risks.
As AI systems become more autonomous, cybersecurity strategies will need to extend beyond traditional endpoint and network protection to include AI agents, model security, prompt injection defences, identity verification and governance over how AI is deployed across the enterprise.
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Eurobarometer finds strong support for protecting children online
A new Eurobarometer survey released by the European Commission shows that Europeans are overwhelmingly concerned about the risks children face online, with cyberbullying, online grooming and harmful content ranking among their biggest worries.
The Flash Eurobarometer 584 survey, conducted between 19 and 24 June 2026 among 25,904 people across all 27 EU Member States, found that 71% of respondents were concerned about cyberbullying and online harassment. Online grooming and sexual exploitation worried 70%, while 69% cited exposure to harmful content such as violence, self-harm and extremism, as well as misuse of children’s personal data.
The survey also highlighted concerns about children’s online habits. Adolescents spend an average of 4.5 hours online on school days and 6.1 hours at weekends, while 14% reported spending more than 10 hours a day on screens.
The findings come as the European Commission prepares new child safety proposals. The Special Panel on Child Safety Online, which met between March and June 2026, will present its recommendations to Commission President Ursula von der Leyen on 13 July. The panel drew on expertise in health, neuroscience, psychology, child rights and digital literacy, with its recommendations expected to inform future EU action.
The European Commission plans to present policy proposals after the summer. The survey also found broader public concern about online risks, with 87% of respondents agreeing that disinformation, foreign interference and AI-generated content threaten democratic processes in the EU.
Why does it matter?
The survey provides strong public backing for stricter EU measures to protect children online. As policymakers consider stronger age assurance, safer platform design and enhanced protections for minors, the findings suggest there is broad public support for more robust regulation of digital services.
The results also reinforce the growing view that online safety is no longer only a technology issue but a public health and child protection challenge. Concerns about cyberbullying, harmful content and excessive screen time are increasingly shaping debates on platform accountability across Europe.
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Two in five UK children say they bypass online age checks
Nearly two in five UK children aged 11 to 17 say they have successfully bypassed an online age check, according nationally representative research commissioned by the Department for Science, Innovation and Technology (DSIT).
The study surveyed 2,299 children in May 2026 to examine their experiences with age assurance, VPN use and methods of bypassing age checks. It also included an additional sample of recent VPN users.
Overall, 39% said they had successfully bypassed an age check at least once, while another 14% had tried unsuccessfully. Success rates rose from 28% among 11- to 12-year-olds to 43% among older teenagers.
Many children avoided age checks altogether by choosing websites, apps or games that either had no age verification or appeared easy to bypass. Among those who successfully circumvented checks, 63% said they simply pretended to be older, most commonly by entering a false date of birth.
Most successful circumvention involved simple self-declaration systems such as tick boxes and date-of-birth fields, which children also rated as the least effective.
By contrast, 86% of respondents who had encountered government ID verification considered it effective, while third-party identity services, payment card verification and facial age estimation also received substantially higher ratings.
Privacy was the most common reason for using a VPN. However, 22% of VPN users said they had used one to access age-restricted websites, apps or games, equivalent to 7% of all children surveyed.
Parents were involved in some VPN use. Among children who had used one, 22% received help from a parent to set it up, while 43% of current users said a parent paid for the service. However, older teenagers were more likely to install VPNs without parental knowledge.
Friends were the main source of information about bypassing age checks, cited by half of children who had done so. Practical consequences appeared to be the strongest deterrents, including harder-to-defeat checks, permanent account bans, and notifying parents about circumvention attempts.
The report also found an association between bypassing age checks and exposure to harmful content. Among children who had circumvented age checks, 51% reported later encountering at least one form of harmful material, including explicit content, contact from unknown adults and requests for personal information.
The researchers cautioned that the findings rely on self-reported behaviour and do not establish that VPN use or circumvention directly caused exposure to harmful content.
Why does it matter?
The findings suggest that basic self-declaration systems provide limited protection for children and are easily circumvented. As regulators increasingly require stronger age assurance under frameworks such as the UK’s Online Safety Act, the challenge will be deploying systems that are both effective and proportionate while protecting users’ privacy.
The research also highlights that technology alone is unlikely to solve the problem. Children’s motivations, platform design, parental involvement and digital literacy all influence whether age restrictions are respected, suggesting that meaningful online safety will require a combination of technical safeguards, regulation and education.
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EU expands cybersecurity and resilience support for Armenia
The Council of the EU has officially launched the EU Partnership Mission in Armenia (EUPM Armenia), a new civilian mission under the Common Security and Defence Policy (CSDP) that will help strengthen the country’s resilience against hybrid threats, including cyberattacks and disinformation.
The advisory mission, established in April 2026 at the request of the Armenian government, will initially operate for two years.
EUPM Armenia will provide strategic advice, technical expertise and institutional capacity-building in areas including cybersecurity, foreign information manipulation and interference (FIMI), and illicit financial flows.
The mission will also establish a dedicated project cell to deliver targeted assistance while promoting a whole-of-government approach to tackling hybrid threats. The Council stressed that the mission is advisory in nature and will not participate in Armenia’s national decision-making.
According to the Council, the mission forms part of the EU’s broader strategy to strengthen Armenia’s resilience, democratic institutions and security capabilities while fully respecting the country’s sovereignty and ownership.
The mission follows the adoption of the EU-Armenia Strategic Agenda in December 2025, which identified countering hybrid threats and disinformation as key priorities for bilateral cooperation. Cosmin George Dinescu has been appointed Head of Mission.
EU High Representative Kaja Kallas described the deployment as part of a broader package of political and economic support for Armenia. She said the mission would help strengthen Armenia’s ability to respond to cyber threats, disinformation and illicit financial flows while increasing its resilience to external pressure.
Why does it matter?
The launch of EUPM Armenia reflects the EU’s growing focus on civilian security and resilience alongside traditional defence cooperation. By providing expertise on cybersecurity, disinformation and institutional resilience rather than military assistance, the mission illustrates how the EU is increasingly addressing hybrid threats through governance, capacity-building and technical cooperation.
The mission also highlights the expanding role of cybersecurity and information resilience in international partnerships. As hybrid threats become more sophisticated, governments are placing greater emphasis on strengthening institutions and public-sector capabilities before crises emerge rather than responding after attacks occur.
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ENISA introduces cybersecurity assessment tool for SMEs
The European Union Agency for Cybersecurity (ENISA) has introduced a Cyber Resilience Maturity Assessment Model to help micro, small and medium-sized enterprises (SMEs) strengthen cybersecurity and prepare for the EU’s Cyber Resilience Act (CRA). The framework offers a structured way for organisations to assess their current cyber resilience, identify weaknesses and improve product security over time.
Designed primarily for manufacturers of products with digital elements, the framework provides a structured way for organisations to assess their cyber resilience, identify weaknesses and improve product security over time. It evaluates five areas, such as governance, risk management, vulnerability management, product lifecycle management and cybersecurity skills.
Businesses are classified as having basic, intermediate or advanced cybersecurity maturity. A downloadable assessment tool allows organisations to track progress through repeated self-assessments, although ENISA notes that achieving a higher maturity level does not replace compliance with the CRA.
Alongside the framework, ENISA published the results of a survey of 194 organisations across 31 countries. While 66% of respondents were aware of the CRA, many said they had only a limited understanding of its practical requirements. Medium-sized companies generally demonstrated stronger cybersecurity maturity than micro-enterprises, with incident response and product lifecycle management emerging as the weakest areas.
More than 70% of SMEs said they needed practical support, including technical guidance and secure development templates. Respondents also cited limited budgets, staff and time as major barriers to compliance, prompting ENISA to recommend targeted guidance, financial support and stronger outreach to smaller businesses.
Why does it matter?
SMEs make up a large share of Europe’s digital economy and supply chains, yet many lack the resources needed to meet increasingly demanding cybersecurity requirements. ENISA’s maturity model gives organisations a practical way to assess their readiness, strengthen product security and prepare for compliance with the Cyber Resilience Act.
The findings also highlight that regulation alone is unlikely to improve cybersecurity. Smaller businesses will need practical guidance, technical support and investment to meet new standards, making implementation as important as the legislation itself.
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MIT develops safer way to detect harmful AI models
MIT researchers have developed a new auditing method to detect whether generative AI models have been adapted to produce child sexual abuse material without generating illegal content during testing.
The technique was developed with Thorn, a child safety nonprofit focused on protecting children from sexual abuse and exploitation online.
Traditional AI safety testing often involves prompting a model and checking its outputs, but that approach cannot be used for child sexual abuse material, which is illegal to generate in the US and many other jurisdictions.
MIT said the problem has become more urgent as open-source generative AI models become easier to download, adapt and redistribute.
The researchers’ method examines internal changes during fine-tuning, rather than testing the model by generating images.
In tests, the auditing procedure identified model variants adapted to generate child sexual abuse material with 100% accuracy.
MIT said hosting platforms could use the method to flag unsafe models, block uploads or remove harmful adaptations before they spread more widely online.
The researchers also plan to test whether the approach can detect harmful capabilities in a larger set of model variants and in base models before adaptation.
Why does it matter?
The research addresses a serious AI safety blind spot: some harmful model capabilities cannot be tested safely or legally by generating outputs. A non-generative auditing method could give hosting platforms, auditors and law enforcement a safer way to detect models adapted for child sexual abuse material before they are distributed. It also points to a broader governance challenge around open-source generative AI: platforms may need scalable tools to assess harmful adaptations without exposing reviewers to illegal or traumatic content.
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Ofcom proposes tougher rules on scam ads
Ofcom has proposed new rules requiring major online platforms to do more to prevent scam advertising, including verifying advertisers, blocking repeat fraudsters and making fraudulent adverts easier to report.
The draft Fraudulent Advertising Code is being developed under the UK’s Online Safety Act and would apply to some of the country’s largest social media platforms, search engines and other online services.
According to Ofcom, more than half of UK adults have encountered potentially fraudulent adverts online, while victims lose an estimated £200 million each year. The regulator said online platforms have not done enough to stop criminals exploiting their advertising systems.
The proposed code sets out nearly 40 measures, including banning accounts that publish scam adverts, preventing repeat offenders from opening new accounts, verifying the identity of advertisers and confirming that firms promoting banking or investment services are properly authorised.
Platforms would also be expected to strengthen account security, reduce the risk of account hijacking, test AI-powered advertising tools against misuse and establish dedicated reporting channels for trusted organisations, including law enforcement agencies, to flag fraudulent adverts for rapid removal.
Ofcom also wants platforms to use proactive technologies to detect and block fraudulent advertising before it reaches users. A separate consultation on those proposals is expected this autumn alongside a broader package of online safety measures.
The consultation remains open until 2 October, with final decisions expected next year. Once approved by Parliament, companies that fail to comply could face fines of up to £18 million or 10% of global annual revenue, whichever is higher.
Alongside the advertising proposals, Ofcom also published draft rules for Category 1 services under the Online Safety Act. These include stronger protections for journalistic content and democratic debate, improved user controls over harmful content, more effective complaints procedures and greater transparency through published risk assessment summaries.
Why does it matter?
The proposals would expand platform responsibility beyond user-generated content to the advertising systems that increasingly enable online fraud. By introducing requirements for advertiser verification, proactive detection and stronger enforcement against repeat offenders, Ofcom is seeking to make scam prevention a core responsibility of online platforms rather than relying primarily on users to identify fraudulent adverts.
The draft code also reflects a broader regulatory trend towards greater accountability for digital advertising ecosystems. As AI-generated content and increasingly sophisticated scams become more common, regulators are placing greater emphasis on platform governance, advertiser verification and proactive risk management.
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