AI News Archive: May 27, 2026 — Part 12
Sourced from 500+ daily AI sources, scored by relevance.
- Apple Watch or Garmin? I asked ChatGPT which smartwatch makes more sense and here’s the verdict
I asked ChatGPT to help me choose between Apple Watch and Garmin based on my workouts, lifestyle, and fitness goals. AI breaks down which smartwatch fits me better for gym, running, recovery, battery life, and long-term use.
- HiBob Appoints Yael Klass as VP of Brand to Shape Market Leadership in the AI Era
HiBob Appoints Yael Klass as VP of Brand to Shape Market Leadership in the AI Era markets.businessinsider.com
- Why machine-speed exploits demand autonomous defense
When Anthropic’s Mythos model unearthed a 27-year-old OpenBSD flaw in the time it takes to brew a coffee, the “AI Vulnerability Storm” stopped being a theoretical threat and became our new reality. For years, the security industry has debated when AI would truly disrupt the exploit market. That debate is over. We are now defending against an adversary that doesn’t sleep, doesn’t get bored, and scans code at industrialised speeds. The death of the grace period We used to have the luxury of time, which is easy to say in hindsight. The traditional defensive playbook was a predictable rhythm: a CVE is released, you grab a coffee, raise some tickets, and your team spends the next few weeks “prioritising” the patch. I have worked in vulnerability management, and I know that is a huge oversimplification, but in comparison, that’s how it feels. You relied on the grace period between a vulnerability being announced and a reliable exploit hitting the wild. Mythos just set that playbook on fire. When a frontier model can scan your entire external attack surface and draft a working exploit in minutes, your 14-day or 30-day patching cycle isn’t a strategy, it’s a liability. The Australian Cyber Security Centre’s (ACSC) recent findings confirm this: while AI isn’t yet a “sentient hacker” capable of complex, end-to-end strategic takeovers, it is terrifyingly good at the “boring” parts of the tradecraft, such as reconnaissance, code analysis, and rapid prototyping. Currently, the real threat isn’t an AI brain; the threat is the machine-speed collapse of the exploit window. System design is the real vulnerability I’ve realised a hard truth recently: If your entire security posture fails because of a single unpatched vulnerability, patching isn’t your problem. Your system design is. Brittle systems rely on the absence of flaws. They are houses of cards waiting for the next CVE to blow them over. Resilient systems assume flaws are inevitable. We have to move past a defensive posture and start building a Modern Defensible Architecture (MDA). This isn’t just my opinion. The Cloud Security Alliance (CSA) recently issued 11 Priority Actions for a “Mythos-ready” world, and they align perfectly with the ACSC’s direction on MDA. The message is clear: Security is no longer about fixing a bug. It is an architectural mandate to ensure that no single failure leads to a catastrophe. The counter-move: Turning speed against the machine If we can’t out-patch the machine, we have to out-architect it. A Modern Defensible Architecture relies on Zero Trust as the floor, but it uses Deception as the walls. This is where it gets interesting. Under CSA Priority Action #9, there is a clear push to move toward active defense (90-day clock in fact). In a traditional network, a compromised server is a foothold. In a defensible architecture, that server is surrounded by honeypots, tokens, and decoy pathways. When an AI-driven tool like Mythos scans your environment, it doesn’t just see your assets; it sees a hall of mirrors. Because the AI moves at machine speed, it is actually more likely to trip a deception element than a human attacker would. This creates what we call a “High-Fidelity Signal”. A touch on a decoy isn’t a “maybe” alert; it’s a definitive indicator of intent. This allows for Action #10: Automated Containment. When seconds count, you can’t wait for a human analyst to get to this in their queue and verify an alert. You need the architecture to recognise the threat and shut down the endpoint/segment automatically. The shift To move from reactive patching to a Modern Defensible Architecture, organisations must first focus on eradicating the external attack surface by moving applications behind a Zero Trust framework. By making internal assets invisible to the public internet and eliminating open “listeners,” you effectively deprive models like Mythos of the reconnaissance data they need to draft an exploit. This aligns with CSA Priority Actions #1 and #5, shifting the goal from “patching everything” to “hiding everything” so that a vulnerability cannot be reached in the first place. Second, we must saturate the environment with active deception, deploying honeypots, tokens, and decoy pathways that turn an AI’s industrialised scanning speed into its own undoing. As outlined in CSA Action #9, a defensible architecture should function like a hall of mirrors. Because an AI probes at machine speed, it is statistically far more likely to interact with a decoy than a human attacker would. This creates the “High-Fidelity Signal” necessary to distinguish a legitimate system failure from a targeted, machine-led intrusion. Finally, organisations must mandate automated containment to counter the total collapse of the exploit window. In a world where Mythos can weaponize a flaw in minutes, manual triage is a legacy process we can no longer afford. Following CSA Action #10, the architecture must be empowered to instantly isolate endpoints or revoke sessions the moment a high-confidence threat is detected. By moving from “Human-in-the-loop” to “Human-over-the-loop” for containment, we ensure that our defensive response finally matches the velocity of the adversary. The clock is ticking The Mythos era doesn’t require us to reinvent security, but it does require us to stop pretending that faster patching is a sustainable path forward. Nobody is saying patching doesn’t matter, but if it’s the foundation that the system is built on, you’re already behind. Organisations need to get off the endless treadmill of CVE remediation and start building Modern Defensible Architectures. By combining Zero Trust with active Deception, we create systems that don’t just resist attacks, they defend against them autonomously. The goal isn’t to build a ship that never leaks. The goal is to build a ship so well-compartmentalised that even when a hull plate fails, the mission continues. The CSA gave us the blueprint. Mythos gave us the deadline. It’s time to stop fighting the storm and start building better ships. To learn more, visit us here .
Score: 00🌐 MovesMay 27, 2026https://www.cio.com/article/4177922/why-machine-speed-exploits-demand-autonomous-defense.html - Cyber defense in the era of frontier AI: Insights from Mythos and GPT 5.5 Cyber
Frontier AI models like Anthropic Mythos and OpenAI GPT 5.5 Cyber present a critical inflection point for enterprise security. While they unlock transformative potential for security engineers seeking to embed AI into their workflows, they also expand the attack surface for organizations facing increasingly sophisticated attacks when used by threat actors. Mythos and GPT 5.5 Cyber do something fundamentally different from previous models. They reason across attack paths, weigh exploitability, and generate security-relevant workflows. The threat chain remains the same. Attackers will continue to find what’s exposed, break in through a weak point, move laterally, and steal data. What’s changed is the expertise required, speed, and scale. The question isn’t whether these models will impact your security posture; it’s whether your team will harness them faster than your attackers. In this blog, we share what we’ve learned from putting these models to the test at Zscaler: what they can do for your security operations, vulnerability management, and what they mean for your enterprise cyber defenses. Frontier model testing methodology To unlock the full potential of frontier AI in security testing, we engineered a purpose-built evaluation framework organized around three core testing harnesses—each designed to mirror real-world attack and defense scenarios. Think Like an Attacker – Black Box Testing : The model engages the target with zero internal system knowledge, simulating the perspective of a motivated external adversary. Findings validated through this harness are immediately elevated for remediation, given their direct exploitability by malicious actors in the wild. The Defender’s First Take – Artifact & Code Repository Testing : The model conducts deep inspection of source code, compiled binaries, and static files, looking for security weaknesses before they can be weaponized. While this harness yields fewer confirmed findings than its counterparts, we found it uniquely effective at decomposing complex systems and generating high-quality findings for downstream dynamic validation. The Informed Adversary – Gray Box & White Box Testing : The model conducts its most informed and precise analysis armed with partial or full system context, including threat models, architectural specifications, and results from prior scans. This approach generated the most actionable findings, enabling the model to identify paths to compromise more effectively, although results were heavily influenced by the quality and extent of the context provided. With this framework in place, we could finally measure what matters. Not whether AI can simply find security issues, but whether frontier AI finds the right ones, faster than any approach before it. Every run moved through the same pipeline: attack surface mapping, test planning, active testing, dynamic validation, deduplication, triage, ticketing, patching, and validation. We designed this structure thoughtfully, incorporating context like what held up under dynamic validation, how severity shifted after deduplication, and how clean the remediation path looked. How Mythos & GPT 5.5 Cyber models operate: A fundamental shift in security reasoning The defining capability that separates new frontier AI models from conventional security tooling is multi-step reasoning . Rather than returning isolated findings, these models construct complete attack paths—connecting preconditions, privilege states, misconfigurations, and downstream exposures into chains that mirror how real adversaries actually operate. We pushed these models hard across the full spectrum of security capabilities. Below are the findings: Capability Value to Security Teams Attack Path Analysis Identifies how separate weaknesses can combine into a viable compromise. Demonstrable Exploitation Backs findings with working proof-of-concept exploit scripts and independently validates the outcome. Vulnerability Prioritization Separates theoretical risk from reachable, exploitable exposure so teams focus on what matters. Iterative Analysis Able to dynamically use multi-step reasoning across a problem rather than returning pattern-based one-shot answers. Detection Engineering Accelerates the creation and refinement of detections, threat hunts, and analytic logic. Investigation Support Rapidly assists with evidence gathering, summarization, and data analysis for incidents. Remediation Guidance Recommends controls and corrective actions aligned to likely attacker behavior. Operational Speed Reduces time from signal to decision, especially in complex environments. Of all the capabilities we evaluated, attack chaining and iterative analysis were the most consequential. Frontier models don’t just enumerate vulnerabilities; they reason across them, connecting privilege states, misconfigurations, and exposures into plausible, multi-stage attack paths. Here is an example illustrating the model’s advanced capabilities of reasoning. Multi-path attack chaining: Converging on the same objective from multiple angles. Mythos and GPT 5.5 Cyber can extend reasoning further than ever before, exploring multiple simultaneous attack paths toward the same adversarial objective. Starting from an initial endpoint mapping, the model branches across independent vulnerability chains, combines vulnerabilities with misconfigurations, preserves intermediate attacker state (credentials, tokens, session data), and converges on a single high-impact outcome. Frontier models are better sensors. They detect weaker signals while filtering more noise, and they do it fast. The data was always there; what changed was the ability to resolve it into a complete, actionable picture—something that is difficult or, in some cases, impossible for a human to do at this scale. Key learnings from testing Mythos & GPT 5.5 Cyber Across our benchmarks, frontier models surfaced twice as many high-severity findings, twice as fast as legacy tooling and pen-testing approaches. But the more important outcome is what survived validation. The findings that held up were all actionable with accurate severity, clear reproduction paths, and remediation guidance grounded in realistic attacker behavior. This represented a significant improvement in signal-to-noise ratio with actionable outcomes when compared to legacy tooling. Key Learnings The differentiator is reasoning depth, not just the scan speed : Frontier models win by thinking deeper, not scanning faster—chaining isolated, low-severity findings into critical attack paths that legacy tools miss entirely. Context is a double-edged sword : Providing architectural context, threat models, and known weaknesses significantly improved accuracy. But there’s a counterintuitive risk: feeding the model examples of previously found issue classes caused it to anchor on those patterns and stop hunting for what hadn’t been discovered yet. Ground the model in its environment. Don’t lead it to your conclusions. No context inflates severity : Without grounding, models misread dependencies and over-escalate findings. Context-aware reasoning is the minimum bar for meaningful results. Focused, expert-guided workflows outperform broad usage : Untargeted prompting wastes capacity and produces noise. Point the model at specific objectives (vulnerability hunting, code scanning, or targeted analysis) with relevant context. Expert-led, targeted workflows are what separate signals from slop. The harness is the force multiplier : While the model quality is table stakes, the real force multiplier is embedding frontier AI into structured, repeatable test harnesses. Our most effective workflows evolved from a core set developed by Product Security and refined by Security Champions across engineering teams. How security leaders can prepare Frontier AI capability is spreading quickly. The challenge will no longer be access to the models, but instead how to use them defensively before your adversaries use them to attack. Defenders need to prepare for this inevitable crossroads now. We developed these high-impact recommendations that go beyond active vulnerability management to start reducing your risks today: Hide your apps : Reduce your external exposure by moving your applications behind a Zero Trust Architecture like Zscaler Private Access. Attackers can’t breach what they can’t reach. Understand your assets and associated risks : Establish complete visibility of exposed and internal assets, including AI assets. This is where Zscaler can help with AI Asset Management, Asset Exposure Management, External Attack Surface Management, and Unified Vulnerability Management, powered by AI. Prioritize deploying proactive defense with Deception : AI will use multiple paths to get to the action-on-objective stage and, in the process, inadvertently trigger carefully planted decoys in the environment. Zscaler customers can deploy our built-in Deception technology to auto-contain the asset or identity from accessing all real applications while capturing full activity in the decoy environment. Prioritize Zero Trust everywhere architecture : Apply Zero Trust consistently across remote and on-prem environments. Enforce user-to-application segmentation to prevent lateral propagation and reduce the blast radius from AI-driven attacks. AI red teaming and guardrails for your production models : Treat your production AI like a real attack surface. Protect it from prompt injection, toxic content, hallucinations, and model drift over time. AI-Powered Exposure Management : Prioritize remediation and patching using Zscaler Exposure Management Remediation Agent for high-risk areas (applicable to both external and internal assets). In conclusion, AI is moving from simple assistants to a mission-critical operational capability. That creates both opportunity and urgency. Defenders now have the chance to improve speed, precision, and scalability in ways that were difficult to achieve with human effort alone. At the same time, adversaries will pursue the same advantages. The organizations that lead in this next phase will be those that combine frontier AI with strong architecture, trusted context, and disciplined enforcement. At Zscaler, we believe this is where frontier cyber models and Zero Trust naturally converge. The future of cyber defense will not be defined by more alerts or more dashboards. It will be defined by systems that understand exposure, reason across attack paths, and help defenders act faster and more precisely than the adversary. That is the future security teams should be preparing for now. To learn more, visit us here .
- The agentic wave: Why advanced AI demands foundational security
The agentic wave The use of agentic and predictive AI in enterprise applications is arguably the fastest evolving frontier our industry has ever encountered. We are currently navigating a “mega-wave” of innovation and a period of rapid expansion where the capabilities of autonomous agents are outstripping our initial frameworks for managing them. While the technology feels revolutionary, the pattern of its arrival is remarkably familiar. History rhymes: From microservices to agents As an industry, we have stood on this shoreline before. We saw this with the rise of microservices and the containerisation movement, the birth of code pipelines, and the cultural shift of DevSecOps…and this is just one example. In the early days of those transitions, there was a palpable sense of anxiety regarding the lack of “battle-hardened” best practices. Critics argued the tech was too raw for the enterprise. But the reality is simple: there are no battle-hardened best practices until there have been battles of significance. Best practices are forged in the heat of implementation, not in the vacuum of theory. We are repeating this cycle with agentic AI. We are moving at breakneck speed to create governance and security strategies in real-time. This isn’t a sign of chaos; it is the natural state of innovation. Crucially, it is worth recognising that doing nothing is not a viable alternative. Innovation will not pause to wait for the creation of a governance framework; it will not wait for us to provide the ultimate security framework; it will continue to move forward with or without our participation. The blueprint for “careful adoption” Recently, the Australian Cyber Security Centre (ACSC), along with its Five Eyes partners, published a document on the careful adoption of agentic AI services. It is a timely reminder that while we must move fast, we must also move with intent. The guidelines reinforce a core truth: AI security is not a standalone silo. It is a subset of your existing cybersecurity strategy. To be successful, these services must be integrated into what the ACSC refers to as a Modern Defensible Architecture. This framework moves us away from reactive security and toward systems that are “secure by design”, where the architecture itself is built to withstand and recover from the inevitable compromise. The ACSC document highlights that we should not fear the agent, but we must respect its potential for autonomy by anchoring it within these resilient architectural pillars: The Principle of Least Privilege & Segmentation: Just as we did with microservices, agents must be granted only the minimum access required to perform their tasks. In a defensible architecture, this means treating every AI agent as a distinct identity, segmented from the core network to ensure that if an agent is “jailbroken” or compromised, the blast radius is strictly contained. Visibility and Logging: A key tenet of a defensible system is knowing what is happening in real-time. We must have full visibility into the “chain of thought” and the actions taken by an agent, ensuring that every autonomous decision leaves a verifiable audit trail. Human-in-the-Loop (HITL) as a Circuit Breaker: Autonomy does not mean an absence of oversight. High-stakes decisions, especially those affecting system integrity or sensitive data, require a human “green light.” This acts as the ultimate fail-safe in a resilient system, ensuring that logic remains grounded in human intent. Phased Implementation & Continuous Validation: Start with low-risk internal tasks to “earn” the right to move toward customer-facing or sensitive operations. This iterative approach allows us to test the “defensibility” of our AI integrations in controlled environments before they are exposed to the complexities of the open web. By aligning agentic AI adoption with these principles in a Modern Defensible Architecture, we aren’t just protecting a single application; we are building a resilient ecosystem that can adapt to new threats as quickly as the AI itself evolves. The best practice of today is the legacy of tomorrow In an era of significant speed, we must accept a difficult reality: the best practice today may not be the best practice tomorrow. The models are changing weekly, and the threat vectors are evolving alongside them. To thrive in this environment, we don’t need rigid, static rules. We need flexibility and leadership. One of the most enduring lessons from all of the innovation waves that our industry has endured is that security cannot exist in a vacuum. IT and Security leaders must partner with their business counterparts. And it is not enough to just be “seen” as enabling innovation; there must be leadership and objective outcomes. They must be seen to deliver competitive advantages and efficiencies. Our goal should be to build resilient systems that can withstand the “hallucinations” of a model or the sophisticated prompt injections of an adversary. We must bring our users along for the ride, educating them on the “why” behind the guardrails so that security becomes an enabler of innovation, rather than a hurdle. Moving forward with deliberate innovation The message for enterprise leaders is clear: Innovate…but do so carefully and deliberately. We need robust governance that doesn’t just say “no,” but instead asks “how can we do this safely?” We need to build systems that are modular enough to swap out models as they improve and resilient enough to fail gracefully when they don’t. We’ve navigated these waves before, from the mainframe to the cloud, and from monolithic code to microservices. The Agentic Wave is our next chapter. By applying the discipline of the past to the technology of the future, we can ensure that this wave carries us forward rather than pulling us under. To learn more, visit us here .
Score: 00🌐 MovesMay 27, 2026https://www.cio.com/article/4177900/the-agentic-wave-why-advanced-ai-demands-foundational-security.html - 'Self-reporting is the best way we can get this information out to the public': Erin Brokovich's next crusade is tracking new data centers across the US — and she wants your help
Erin Brockovich launches public reporting platform tracking environmental concerns surrounding AI data centers across the US.
- Erin Brockovich starts tracking AI data centers, calls on affected communities to submit issues — website shows more than 2,700 reports from across the US raising various concerns
Erin Brockovich, who made her name making a case against PG&E in the '90s that resulted in a $333 million settlement, is now looking at the impact of data center developments on communities and is recording community reports along the way.
- Italian council sets 200% tax on data center development in agricultural zones — aims to spur the use of old industrial areas instead and limit environmental impact
This 200% tax will make it more expensive to build in rural areas and is aimed at spurring data center development in disused industrial zones instead.
- Innovate fast, owe less: A practical path to help reduce AI security debt
Artificial intelligence continues to evolve rapidly, with solutions emerging to enhance worker productivity , help businesses develop products more quickly, and improve business operations. Implementing these solutions, however, means introducing a bevy of new AI apps and agents –– and that means introducing security risks. “AI technology has evolved rapidly, from single modal foundation models to multi-modal to reasoning models to agentic AI,” says Vimal Navis, Principal with PwC focused on Cyber, Data and Tech Risk. “Industry frameworks, standards, and cybersecurity controls are taking time to catch up. The gap becomes debt.” It’s a tricky balancing act because employees have easy access to a slew of coding assistants and other AI tools, creating at least a couple of issues. First, as new capabilities come on board, they may make others redundant, adding to the problem of technical debt. Second, each new solution can introduce a potential security risk and, to the extent they operate without the knowledge of IT, they add to the problem of shadow IT – or shadow AI, in this case. Shadow AI, in turn, adds to the AI security debt. “Even within approved tools, new AI features are being added that IT may not be prepared for,” Navis says. Think of a new AI chatbot or query engine being added to a CRM tool, or connectors to external applications, for example. While such technologies may be useful, they also pose security challenges, such as new forms of attack that companies may not be prepared to defend against. Managing AI Starts with the Right Tools: A Look at Microsoft Microsoft is starting to treat AI agents as first-class enterprise assets that can be inventoried, governed, and monitored. That is important because AI security debt often comes from moving fast on innovation while losing track of what was deployed, what it can access, and who is accountable for it. With Microsoft Agent 365 , IT gets a view of agents that are registered and interact with the organization’s Microsoft stack. IT can set policies for who can create, onboard, and manage agents. Agent 365, together with Microsoft Defender, helps organizations observe, secure, and govern AI agents across the enterprise. It can help detect suspicious and malicious agent activity, visualize potential attack paths from agents to critical assets, and support remediation of agent misconfigurations, exposure risks, and related vulnerabilities, among other capabilities. Similarly, Microsoft Purview can be used to check for incorrect or excessive permissions on sensitive data and to enable strict controls apply to it. Purview DLP can help organizations tackle sensitive data leaking through prompts, chat histories, connectors, and retrieval paths. Additionally, Microsoft Entra now includes identity and network access capabilities that apply to AI agents – aimed at applying zero trust principles to non-human actors. Microsoft Defender for Cloud Apps can help govern agent-related SaaS and genAI usage by discovering shadow AI apps, assessing risk, controlling unsanctioned apps, governing OAuth access, and applying real-time session and data protection controls. If all this sounds like a lot to tackle, companies can turn to PwC , a Microsoft Agent 365 launch partner , for assistance. “We help companies assess the rapidly evolving threat landscape, identify where AI security debt is accumulating fastest, and translate requirements into workable controls aligned to Microsoft’s expanding capabilities,” Navis says. “We help them get a view of the threat model and help confirm their controls can keep up without compromising on the speed of innovation.” Learn more about how PwC can help you employ AI securely. Read, “Building trust in AI from the ground up: How you can secure the data behind it,” by PwC’s Vimal Navis and Joe Ponder.
Score: 00🌐 MovesMay 27, 2026https://www.cio.com/article/4177467/innovate-fast-owe-less-a-practical-path-to-help-reduce-ai-security-debt.html - Erin Brockovich reveals crowdsourced AI data center map
Erin Brockovich is taking on the AI industry with a new website that tracks AI data centers.
Score: 00🌐 MovesMay 27, 2026https://mashable.com/tech/erin-brockovich-ai-data-center-reporting-website - Anthropic names first Korea chief ahead of Seoul office launch
Anthropic, the US developer of the Claude AI model and a chief rival to OpenAI, said Wednesday it has appointed Choi Ki-young, former head of Snowflake's Korea operations, as the first country head of its Korean unit ahead of the formal opening of a Seoul office. Senior executives will visit Seoul within weeks to establish the office and meet major corporate customers. Anthropic registered its Korean entity, Anthropic Korea, in July 2025. Choi brings more than 30 years of experience in the techn
- OpenAI brings GPT-5.5-Cyber to Korea in security push
OpenAI said Wednesday it is launching a cyber action plan for South Korea to help government agencies, public institutions and companies strengthen defenses against AI-driven cyber threats, with the country set to gain access to its security-focused model GPT-5.5-Cyber. “We want OpenAI to become a trusted partner to Korea,” OpenAI Chief Strategy Officer Jason Kwon said during a press briefing in Seoul. “Our goal is not to keep advanced cyber capabilities in the hands of a small number of organiz
- Anthropic taps 30-year industry vet Choi Ki-young as chief of operations in Korea
Choi Ki-young, the newly appointed Representative Director of Korea for AI company Anthropic [ANTHROPIC] U.S. artificial intelligence company Anthropic appointed Choi Ki-young as the head of its Korea operations ahead of the opening of its Seoul office, the tech company said Wednesday. Established in 2021, Anthropic is the developer of AI models, namely Claude, and recently drew attention for Mythos, an model designed to identify security vulnerabilities. Although Anthropic established its Korean entity last year, it had not appointed a Korean country head and local staff had been overseeing operations on behalf of the corporation. Related Article Bifrost pitches 3-D AI training platform to Korean manufacturers Samsung will allow employees to use outside AI models starting from June NHN Cloud sells out GPU resources, unveils AI brand 'FactoryX' “According to our latest Economic Index , Koreans use Claude at more than 3.5 times the rate expected for the population size, with usage skewing heavily toward technical and creative work,” the company said. “In the coming weeks, senior leadership from Anthropic will travel to Seoul to officially open the office and meet with customers.” Before joining Anthropic, Choi served as the general manager for Korea at global cloud company Snowflake and has spent more than 30 years in the technology industry across Korea and the Asia-Pacific region. Prior to Snowflake, Choi led Korean operations for companies including Google Cloud, Adobe, Autodesk and Microsoft. The AI company Anthropic's logo is seen in this illustration [REUTERS/YONHAP] “Korea is one of the most sophisticated AI markets in the world, leading in hardware innovation, developer activity and enterprise adoption,” Choi said. “Korean organizations combine technical depth with a commitment to responsible deployment, which is exactly where Anthropic operates.” As the company's representative director of Korea, Choi will lead the AI developer in establishing customized strategies that reflect the “unique uses of Claude in Korea,” while focusing on business expansion, according to Anthropic. “Korea is one of the markets where we’ve seen the most enthusiasm for Claude, and few people understand its technology landscape the way Choi does,” said Chris Ciauri, Managing Director of International at Anthropic. “He’ll build the team and the local partnerships to support how Korean organizations are putting Claude to work.” Anthropic has been expanding its business-to-business operations across the Asia-Pacific region. Last year, the company also appointed a former Snowflake executive as head of its Japan office, continuing its recruitment of B2B specialists. This article was originally written in Korean and translated by a bilingual reporter with the help of generative AI tools. It was then edited by a native English-speaking editor. All AI-assisted translations are reviewed and refined by our newsroom. BY KIM MIN-JEONG [kim.jiye@joongang.co.kr]
- OpenAI to expand Daybreak cybersecurity initiative to Korea
OpenAI Chief Strategy Officer Jason Kwon discusses the company's Daybreak cybersecurity initiative at a press conference at the JW Marriott in Gangnam District, southern Seoul, on May 27. [OPENAI] OpenAI is expanding its Daybreak cybersecurity initiative to Korea's public and private sectors, a move that stands in contrast to Anthropic's stringent standards for access to its Glasswing program to foreign countries. OpenAI will open its Government Trusted Access for Cyber program — designed for government agencies and institutions — to Korea, signing on along with Japan to become one of four user countries, along with the United States and Canada. Participants gain access to OpenAI's latest high-performance AI models. "We are pretty close to finalizing access for Korean government personnel," said Jason Kwon, the chief strategy officer at OpenAI, at a press event held in southern Seoul on Wednesday. "I think they will need to gain access and use the tools, and we expect them to produce results fairly quickly." Related Article OpenAI to shut down viral AI-generated video app Sora that sparked deepfake concerns OpenAI officials discuss safety protocols with Canada following school shooting The program will be overseen by the Korea Internet & Security Agency, while the Ministry of Science and ICT is leveraging the partnership to establish ties between the U.S. company and Korea's newly established AI Safety Institute. OpenAI has flagged Korea as one of the fastest-transforming countries in terms of AI adoption, citing surging user numbers on Codex, OpenAI's coding agent. Kwon said Codex's weekly active users in Korea have grown 10-fold since the beginning of this year, and since the app's launch in February, daily interactions in Korea have increased by more than 30-fold. "Korea is now among the top five countries globally in terms of Codex's adoption and engagement," Kwon said. "In Korea, 50 percent of Codex requests are noncoding tasks. Codex is being used not just by professional developers but also by people looking to automate repetitive tasks, organize workflows and turn ideas into useful tools for Korean users." OpenAI's cybersecurity push in Korea comes amid growing fears triggered by Anthropic's Claude Mythos Preview — an unreleased AI model so powerful at finding software vulnerabilities that Anthropic chose not to release it to the public. The model had already uncovered thousands of previously unknown security flaws across every major operating system and browser, including bugs that had gone undetected for decades. Second Vice Minister of Science and ICT Ryu Je-myung, right, poses for a photo with OpenAI Chief Strategy Officer Jason Kwon ahead of their meeting at Three IFC in western Seoul on May 26, where the two discussed cooperation on AI security threats and related initiatives. [MINISTRY OF SCIENCE AND ICT] The Glasswing project was formed as a defense initiative to channel the model’s capabilities to patch the world’s digital infrastructure. Anthropic launched the initiative with Google, Microsoft, Apple, Nvidia, AWS, Broadcom, Cisco, CrowdStrike, JPMorgan Chase, Palo Alto Networks and the Linux Foundation, with more than 40 additional organizations also granted access to scan their own critical software. Despite discussions held between Seoul and Anthropic earlier this month, Korea's access remains stalled, with domestic reports suggesting that a path in is unlikely. Related Article Korean broadcasters sue OpenAI alleging unauthorized use of content OpenAI claims China's DeepSeek trained its AI by distilling U.S. models, memo shows Reports indicate that the U.S. government holds considerable sway over Glasswing membership. Kwon also noted that data will remain within Korea — a significant assurance given that Korean law strictly limits the transfer of personal data outside its borders. "We have made data residency available in Korea for a while now, which means that when data is processed, it stays within the country," Kwon said. "The other thing that we can do is also not store the data at all, which we do for some of our customers, which tends to satisfy the requirements around data security." OpenAI Chief Strategy Officer Jason Kwon [OPENAI] The Daybreak program is divided into separate frameworks for the public and private sectors, with the latter being the Trusted Access for Cyber initiative. OpenAI is currently in talks with several Korean conglomerates to sign them up for the program as well. Beyond cybersecurity, OpenAI is broadening its partnerships with Korean institutions. On Tuesday, OpenAI signed a memorandum of understanding with Korea Water Resources Corporation to develop AI-powered water disaster management systems capable of addressing climate-driven water crises. Separately, OpenAI also signed a cooperation agreement with the Korea Technology Finance Corporation to support the growth of domestic AI startups, including the development of an AI-based technology assessment system. BY LEE JAE-LIM [lee.jaelim@joongang.co.kr]
- Cybersecurity Evolution: How We Went From Perimeter Defense to AI-Native Security
The cybersecurity industry of 2006 barely resembled today's billion-dollar behemoth. As part of Dark Reading's 20th anniversary celebration, we trace the industry's evolution through a technology lens.
- AI-Assisted Exploit Development Outpaces Scanner Detection
Attackers are using AI to dramatically reduce the time they need to develop a working exploit for a CVE, according to new research.
Score: 00🌐 MovesMay 27, 2026https://www.darkreading.com/threat-intelligence/ai-assisted-exploit-development-scanner-detection - 오픈AI, 한국에 ‘데이브레이크’ 도입…정부·공공기관 AI 보안 지원 본격화
오픈AI 최고전략책임자(CSO) 제이슨 권은 27일 서울에서 열린 기자간담회에서 “AI는 이제 단순한 기술 실험 단계를 넘어 경제와 사회의 핵심 인프라로 진화하고 있다”며 “한국은 AI 확산의 다음 단계로 나아가기에 매우 적합한 국가”라고 말했다. 권 CSO는 현재 AI 산업이 세 번째 단계에 진입했다고 설명했다. 그는 “첫 번째 단계는 모델 성능과 기술적 돌파구 중심이었다면, 두 번째 단계는 수십억 명의 사용자에게 AI 접근성을 제공하는 과정이었다”며 “이제는 AI가 경제와 사회의 핵심 인프라 일부로 자리잡는 세 번째 단계에 들어섰다”고 말했다. 그는 한국이 이러한 변화에 유리한 조건을 갖췄다고 평가하며, 디지털 기술 수용도가 높고 공공 부문의 AI 관심이 크다는 점, 반도체·인프라·개발자 생태계 등을 모두 갖춘 ‘풀스택 경제(full-stack economy)’라는 점 등을 이유로 들었다. 이날 기자간담회의 핵심은 사이버보안 분야 협력안이다. 오픈AI는 최근 출범한 사이버보안 프로그램 ‘ 데이브레이크 (Daybreak)’를 바탕으로, 한국 맞춤형 액션 플랜을 세 축으로 추진한다. 여기에는 ▲한국 주요 이해관계자 대상 최신 사이버 AI 역량 브리핑 및 시연 ▲‘신뢰 접근 사이버 프로그램(TAC, Trusted Access for Cyber)’을 통한 한국 정부·공공기관 및 사이버보안 당국의 접근 권한 확대 ▲주요 기업과 핵심 산업으로의 TAC 접근 확대 등이 포함된다. 권 CSO는 “지난 5월 18일 오픈AI 국가보안정책 총괄 사샤 베이커가 방한해 과학기술정보통신부, 외교부, 행정안전부, 금융위원회, 국가인공지능전략위원회, 한국인터넷진흥원(KISA) 등 정부 부처와 공공기관 관계자들을 대상으로 사이버 특화 모델 역량을 시연했다”며 “어제는 류제명 과학기술정보통신부 2차관과 사이버보안 및 국가 회복력 협력 방안을 논의했다”고 밝혔다. 보안 외 공공 분야 협력도 구체화됐다. 한국수자원공사와의 MOU를 통해 오픈AI 기술은 수재해 대응, 수자원 관리, 기후변화 적응 등 공공서비스에 적용된다. 기술보증기금과는 스타트업 비즈니스 평가와 금융 지원 의사결정 프로세스 고도화에 협력한다. 오픈AI는 이날 한국 내 AI 활용 확대 사례도 공개했다. 권 CSO에 따르면 챗GPT(ChatGPT)는 현재 전 세계 주간 활성 사용자 9억 명 이상을 확보했으며, 한국은 활성 사용자·유료 가입자·기업 고객 수 기준 글로벌 상위 10개 시장에 포함된다. 특히 코딩 에이전트 ‘코덱스(Codex)’의 한국 내 사용량 증가세가 두드러졌다. 그는 “올해 초 대비 한국 내 코덱스 주간 활성 사용자는 10배, 2월 앱 출시 이후 일간 상호작용은 30배 이상 증가했다”고 설명했다. 특이한 대목은 한국 이용자의 코덱스 요청 가운데 50%가 비(非)코딩 업무에 해당한다는 점이다. 반복 업무 자동화, 워크플로우 정리, 아이디어 구현 등 실무 전반의 도구로 자리잡고 있다는 의미다. 오픈AI는 이달 초 FDE 인력을 전면에 내세운 오픈AI 디플로이먼트 컴퍼니(OpenAI Deployment Company)를 설립 하는 등 FDE(Forward Deployed Engineer) 모델 확장에 속도를 내고 있다. FDE란 고객사 현장에 깊숙이 들어가 AI·소프트웨어를 실제 업무 환경에 맞게 구축·적용하는 엔지니어를 뜻한다. 권 CSO는 이에 대해 “오픈AI는 FDE 비즈니스를 운영하려는 것이 아니라 AI 비즈니스를 운영하려는 회사”라며 “엔터프라이즈 고객이 우리 기술에서 최대한의 가치를 끌어낼 수 있도록 돕는 것이 목표이며, FDE는 그 과정을 가속화하기 위한 수단”이라고 설명했다. 그는 FDE 모델에 대해 “매우 강한 확신(bullish)을 갖고 있다”며, 그 중심 원칙으로 ‘의존성 배제’를 제시했다. 권 CSO는 “오픈AI가 파트너와 고객에게 제공하는 FDE 서비스는 특정 기업이 오픈AI 역량에 종속되도록 만드는 것이 아니라, 해당 기업이 스스로 동일한 역량을 구축하도록 돕기 위한 것”이라고 말했다. 이어 “FDE는 단순히 기업의 AI 전환 작업을 대신 수행하는 인력이 아니다”라며 “작업을 수행하는 동시에, 향후 고객사 내부에서 같은 역할을 맡게 될 인력을 교육하는 ‘트레이너를 훈련시키는(train the trainers)’ 역할도 수행한다”고 강조했다. 즉 오픈AI FDE는 프로젝트 기간 동안 고객사 내부 인력과 함께 일하며 관련 역량과 운영 방식을 전수하고, 프로젝트 종료 이후에도 기업이 자체적으로 AI 전환을 이어갈 수 있는 자립 구조를 만드는 데 초점을 둔다는 설명이다. 권 CSO는 “이러한 배포 작업 과정에서 고객과 협력하더라도, 고객 데이터는 어디까지나 고객의 자산”이라며 “오픈AI가 데이터를 가져가거나 자동으로 처리하지는 않는다”고 강조했다. jihyun.lee@foundryco.com
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앤트로픽은 한국을 클로드 활용도가 높은 시장 가운데 하나로 평가하고 있다. 지난 3월 공개된 앤트로픽의 자체 보고서에 따르면 , 한국의 클로드 사용량은 인구 규모 대비 예상치를 웃도는 수준으로 나타났으며, 기술·창작 분야를 중심으로 활용이 이뤄지고 있다고 설명했다. 앤트로픽은 이러한 흐름을 바탕으로 서울 오피스를 개소하고 국내 사업을 확대할 계획이다. 최기영 신임 대표는 지난 5월 중순까지 스노우플레이크 한국 총괄을 맡았으며, 앤트로픽 합류 이전에는 구글 클라우드, 어도비, 오토데스크, 마이크로소프트 등에서 한국 사업을 이끌었다. 약 30년간 글로벌 기술 기업에서 경력을 쌓아온 인물이다. 최기영 대표는 “한국은 하드웨어 혁신성, 개발자 생태계, 기업의 AI 도입 수준 면에서 매우 성숙한 시장”이라며 “국내 기업들은 기술적 역량과 책임 있는 AI에 대한 의지를 함께 갖추고 있어 앤트로픽이 추구하는 가치와 정확히 맞닿아 있다. 이러한 이유로 앤트로픽에 합류했으며, 장기적인 관점에서 한국 사업을 발전시켜 나가겠다”라고 말했다. 최기영 대표는 한국 시장에서 클로드가 활용되는 방식의 특성을 반영해 현지 맞춤형 전략을 세우고, 기업·개발자·연구자를 아우르는 파트너십 구축을 이끌 계획이다. 앤트로픽 인터내셔널 총괄 크리스 차우리는 “한국은 전 세계에서 클로드 관심도가 높은 시장 중 하나로, 최기영 대표만큼 한국 기술 생태계를 깊이 이해하는 인물은 드물다”라며 “최기영 대표는 서울 오피스 팀을 꾸려 한국 기업들이 클로드를 실무에 적극 활용할 수 있도록 현지 파트너십을 이끌어갈 것”이라고 밝혔다. 앤트로픽 한국 조직은 기업 및 스타트업과의 파트너십 구축, 정부 및 연구 기관과의 협력, 클로드를 활용하는 국내 개발자 커뮤니티 지원에 집중할 계획이다. jihyun.lee@foundryco.com
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