The Office of National Security of South Korea held a cybersecurity meeting to review how government agencies are responding to AI-driven cyber threats. The session focused on the growing risks posed by the misuse of advanced AI technologies.
Officials from multiple ministries attended, including science, defence and intelligence bodies, to coordinate responses. The government warned that AI-enabled hacking capabilities are becoming increasingly realistic as global technology companies release more advanced models.
Authorities have instructed relevant agencies to strengthen cooperation with businesses and institutions and distributed guidance on responding to AI-based security risks. Discussions also covered practical measures to support rapid responses to cybersecurity vulnerabilities across public and private sectors.
The government plans to establish a joint technical response team to improve information sharing and enable immediate action. Officials emphasised that while AI increases cyber risks, it also offers opportunities to strengthen security capabilities in South Korea.
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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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Cybercrime Atlas has launched Cosmos, an open-source platform designed to map global cybercrime networks and strengthen cooperation among defenders, investigators, prosecutors and policymakers.
Hosted by the World Economic Forum’s Centre for Cybersecurity, Cybercrime Atlas aims to build a shared understanding of cybercriminal ecosystems at a time when ransomware, fraud and illicit digital services are becoming increasingly organised and industrialised.
Cosmos links cybercriminal groups, tools, infrastructure, markets and services in an interactive framework. It was developed by the Cybercrime Atlas community, led by Orange Cyberdefense, with contributions from Banco Santander, Universitat de Girona, Scitum and TrendAI.
The initiative responds to a long-standing problem in cybercrime disruption: fragmented terminology, isolated investigations and inconsistent reporting structures. Cosmos aims to standardise definitions, organise threat intelligence into a shared structure and help different actors coordinate more effectively across borders.
The first version of the platform contains nine core categories, 229 identified cybercrime-related elements and 849 mapped connections showing how criminal networks, tools and services interact. The dataset is designed to expand as the wider community contributes new intelligence.
Why does it matter?
Cybercrime increasingly functions as an interconnected ecosystem, with specialised groups, tools, infrastructure providers and illicit services supporting one another across borders. A shared map of those relationships could help shift cyber defence from isolated incident response towards more coordinated disruption of criminal networks, while giving investigators and policymakers a clearer view of how digital crime is organised.
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Cybersecurity agencies from Australia, Canada, New Zealand, the United Kingdom and the United States have published joint guidance on the careful adoption of agentic AI services in organisational IT environments.
The guidance is intended to help organisations design, develop, deploy and operate agentic AI systems, and to make informed risk assessments and mitigations. It primarily focuses on large-language-model-based agentic AI systems.
The publication examines threats to and vulnerabilities within agentic AI systems, including risks introduced through system components, integrations and downstream use. It also considers broader risks arising from agentic AI behaviour in IT environments.
The guidance covers wider agentic AI security considerations, specific security risks, best practices for securing agentic AI systems and steps organisations can take to prepare for emerging and future threats.
It was co-authored by the Australian Signals Directorate’s Australian Cyber Security Centre, the US Cybersecurity and Infrastructure Security Agency, the US National Security Agency, the Canadian Centre for Cyber Security, the New Zealand National Cyber Security Centre and the UK National Cyber Security Centre.
Why does it matter?
Agentic AI systems can act with greater autonomy than conventional software tools, including by interacting with other systems, using integrations and taking steps towards defined goals. That creates new cybersecurity risks when such tools are embedded in organisational IT environments. The joint guidance shows that major cyber agencies are treating agentic AI as an emerging operational security issue, not only as a question of AI policy or experimentation.
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The Australian Securities and Investments Commission has urged regulated entities to strengthen cyber resilience, warning that frontier AI could intensify cyber risks by exposing vulnerabilities at greater speed, scale and sophistication.
In an open letter to industry, ASIC said licensees and market participants should act now to improve their cybersecurity fundamentals rather than wait as advanced AI tools reshape the threat environment. The regulator said cyber resilience should be treated as a core licensing obligation, not solely as an IT issue.
ASIC Commissioner Simone Constant said frontier AI creates opportunities but also materially increases cyber risk, including by exposing weaknesses faster than many organisations realise. She warned that vulnerabilities once seen as isolated could have system-wide effects and enable previously out-of-reach forms of exploitation for many malicious actors.
The letter follows ASIC’s recent court outcome against FIIG Securities Limited, which the regulator said reinforced the need for cyber risk management controls to be demonstrably effective and proportionate to a business’s size, nature and complexity.
ASIC is urging entities to reassess cyber plans, identify and protect critical systems, reduce exposure to untrusted networks, review user access, patch systems promptly, strengthen incident response planning and manage third-party risks. It also says organisations should use AI defensively where appropriate, including to identify vulnerabilities and secure software before release.
Constant said entities need robust incident response plans and that the underlying principles of cyber risk management remain the same: govern, protect, detect and respond. She also said boards and executives must ensure systems are tested, weaknesses are addressed early, and action is taken before threats can be exploited.
ASIC says entities must table the letter at their ultimate board and risk governance committees. It also encourages regulated entities to use guidance from trusted sources, including the Australian Signals Directorate and the Australian Government’s Cyber Health Check.
Why does it matter?
ASIC’s warning shows that financial regulators are beginning to treat frontier AI as a force multiplier of cyber risk, not just a technology issue. By framing cyber resilience as a licensing and board-level governance obligation, the regulator is signalling that firms may be judged not only on whether they suffer cyber incidents, but on whether their controls, escalation processes and resilience planning are proportionate to an AI-accelerated threat environment.
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Advanced AI models are reshaping cybersecurity by accelerating both offensive and defensive capabilities, forcing organisations to rethink how they detect, assess and respond to cyber threats.
A new World Economic Forum report argues that AI is becoming a defining force in cybersecurity, with organisations increasingly moving from pilot projects to operational deployment. According to the WEF, AI is already being used to improve vulnerability identification, threat detection, response speed and resilience.
The report highlights how AI can help security teams process large volumes of data, detect threats faster and support more efficient responses. At the same time, it warns that threat actors are also using AI to automate deception, generate malware and scale attacks at machine speed.
WEF’s analysis says the growing speed and scale of AI-enabled cyber operations are putting pressure on traditional cybersecurity models. Instead of relying mainly on prevention and scheduled patching cycles, organisations are being pushed towards continuous detection, automated response, stronger access controls and more resilient infrastructure.
The report also stresses that AI’s value in cybersecurity depends on strategy, governance and human oversight. Rather than treating AI as a standalone tool, organisations are encouraged to test use cases carefully, build appropriate safeguards and invest in the skills and processes needed to defend at machine speed.
Why does it matter?
AI is changing cybersecurity on both sides of the equation. It can lower the barriers for faster and more scalable attacks, but it can also help defenders improve detection, response and resilience. The wider significance is that cybersecurity strategies built around periodic assessment and manual response may become less effective as AI-driven threats and defences operate at greater speed and scale.
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Cybersecurity researchers at Kaspersky have identified a growing network of so-called ‘grey’ websites that exploit user trust to generate financial gain and harvest personal data. Unlike traditional phishing attacks, these platforms rely on manipulation, misleading design and hidden conditions rather than direct credential theft.
The report shows that gray websites often imitate legitimate services, including financial tools, e-commerce platforms, AI services and subscription-based content.
Common categories include fake browser extensions, fraudulent investment schemes, subscription traps and counterfeit online shops, many of which are designed to encourage voluntary payment or data sharing.
Kaspersky notes that these threats are spreading globally but vary by region.
Europe is seeing a rise in fake privacy tools and browser hijackers, Africa is heavily affected by fraudulent trading platforms, while Latin America faces betting scams and pyramid schemes. Asia-Pacific shows a broader mix, including crypto fraud, AI-themed scams and malicious download services.
Across all regions, attackers are increasingly aligning scams with current digital trends to appear more credible. Kaspersky warns that even well-designed platforms can hide risks, making user awareness, verification and security tools key to reducing financial and data harm.
Why does it matter?
The rise of ‘grey’ websites signals a shift in online fraud away from obvious phishing towards more subtle, trust-based manipulation. Instead of breaking systems, attackers increasingly exploit user behaviour, interfaces, and familiarity with digital services.
That lowers the ‘visibility’ of fraud. Users are not being forced into breaches; they are being guided into consent- signing up, subscribing, investing, or installing tools that appear legitimate. It makes scams harder to detect, harder to regulate, and easier to scale globally.
It also shows how cybercrime is adapting to current technological trends, especially AI services, crypto tools, and digital platforms that people already expect to be trustworthy. As a result, the boundary between legitimate innovation and fraud becomes less clear, increasing systemic risk for both consumers and digital economies.
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Data centres are increasingly central to digital economies, but their rapid expansion is reshaping both electricity demand and cybersecurity risks. According to the International Energy Agency, data centres used about 1.5% of global electricity in 2024, with demand rising as AI and cloud services expand.
These facilities operate as both energy consumers and producers, relying on grid power while also maintaining on-site generation and battery systems. Their ability to switch power sources instantly supports service continuity but can also cause sudden load shifts that challenge grid stability during outages or cyber incidents.
Cybersecurity is now closely tied to energy resilience. Data centres depend on interconnected systems such as backup power, cooling, and digital control networks, all of which require continuous monitoring and protection.
Weaknesses in any part of this ‘system of systems’ can affect both service availability and wider electricity infrastructure.
Why does it matter?
Data centres are becoming a critical infrastructure that directly affects both digital services and electricity systems. Shared planning for power disruptions, cyber events, and load management is increasingly seen as necessary to ensure stability across both digital services and national energy systems.
Their rising energy demand and reliance on complex on-site and grid power arrangements mean disruptions or cyber incidents can have wider knock-on effects, making resilience and cross-sector coordination essential for overall system stability.
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Swisscom has published its 2026 Cybersecurity Threat Radar, warning that cyber threats have grown more complex over the past year as geopolitical tensions and disruptive technologies put added pressure on digital systems. The report presents AI, supply chain exposure, digital sovereignty, and operational technology security as four strategic risk areas for organisations.
The report highlights state-linked cyber activity, hybrid influence operations such as disinformation, and supply chain attacks as key drivers of the current threat environment. It argues that digital transformation has increased dependence on cloud services, third-party software, AI systems, and networked industrial infrastructure, making organisations more exposed to cascading failures and external dependencies.
On AI, Swisscom describes insecure AI use as a risk multiplier. While AI can improve productivity, the report warns that poor governance, weak visibility into models, and uncontrolled use of AI tools in operational environments can expand attack surfaces, affect data quality, and create new compliance challenges.
Software supply chains are also identified as a persistent vulnerability. Swisscom says a single compromised component or manipulated update process can have far-reaching consequences across interconnected systems, making software integrity, origin verification, and traceability increasingly important as mitigation measures.
The convergence of information technology and operational technology is presented as another growing area of concern. In sectors such as energy, healthcare, manufacturing, and building automation, incidents can have consequences that go well beyond financial loss, affecting critical infrastructure, production, and even human safety.
The report also places greater emphasis on digital sovereignty, arguing that organisations need clearer visibility over where data is processed, which legal regimes apply, and how dependent they are on cloud and technology providers. In that sense, Swisscom frames cybersecurity less as a narrow IT function and more as a strategic governance issue tied to resilience, control, and trust.
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Ransomware is responsible for 90% of total cyber-related financial losses in the manufacturing sector, despite accounting for only 12% of claim volume by number, according to an analysis of insurance claims data published by Resilience.
The findings indicate that while ransomware incidents are not the most frequently filed claim type, they produce disproportionately large financial losses when they occur. The manufacturing sector’s low tolerance for operational downtime is identified as a contributing factor to loss severity.
Additional findings from the claims dataset include:
30% of manufacturing claims are linked to phishing and transfer fraud
26% of total losses are associated with multi-factor authentication (MFA) misconfiguration
12% of claims involved wrongful data collection
The report identifies MFA misconfiguration as a notable area of exposure, alongside procedural gaps in financial transfer controls. Recommended mitigation measures include auditing MFA deployment, implementing transfer verification procedures, and investing in ransomware containment capabilities.
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