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経済産業省は、工場を建設する目的で平成27年（1月～12月期）に1,000平方メートル以上の用地を取得した製造業、ガス業、熱供給業、電気業の事業者を対象に「工場立地動向調査」を実施し、結果を取りまとめました。

4月5日から9日まで、韓国のソウルにおいて、日中韓自由貿易協定(FTA)交渉の第10回交渉会合(局長/局次長会合)が開催されます。

経済産業省は、昨年10月に「活力あふれる『ビンテージ・ソサエティ』の実現に向けた取組に係る研究会」を立ち上げ、検討を重ね、今般報告書をとりまとめました。

交通事故の削減や渋滞緩和、環境負荷低減等の課題解決が期待される協調ITS（※）の実現普及に必要とされる地図データベースの整備や維持・更新のコスト削減に役立つ日本発の国際規格が制定されました。

平成28年3月4日に、東京電力からありました改正電気事業法に基づく会社分割に係る申請について、このたび申請のとおり認可しました。

情報通信審議会　電気通信事業政策部会　接続政策委員会（第85回）の開催について

Apple’s new operating system is here, and along with it comes a new version of Siri, dubbed with two very familiar letters: AI. As the name suggests, this Siri power-up resembles an AI chatbot more than the often derided voice assistant you might be used to. It has even evolved from a blob you invoke with a verbal command or a button press to a whole app. This update comes with a slew of privacy complications, but you can take some control over what this new Siri can access and use. 

There’s no denying that the new version of Siri is far more powerful than it used to be, and arguably more useful at surfacing details on your phone. But that comes at the cost of deeper access. Once enabled, Siri and Spotlight are combined, unifying the interface. Where you may have once just pulled down on the screen to search for an app or contact, you’re now also invoking Siri. 

Spotlight and Siri are now visually one and the same.

By default, ask Siri a question and it’ll search through your Apple apps, like Notes, Messages, emails, and more. As time goes on, if the developer chooses to let it, Siri will gain access to more and more third-party apps. If an app developer doesn’t add that support, then Siri AI won’t be able to access the contents of that app (unless it’s shared screenshot-style via a new feature called “on-screen awareness,” which we’ll talk about more in a moment). 

For example, if Signal doesn’t choose to implement Siri AI support and you only talk to Bill on Signal, you won’t get an answer when you ask Siri AI, “What was the last photo Bill sent me?” But if you talk to Bill on Apple Messages and ask that same question, Siri AI will summarize what it thinks the photo is.

Sometimes Siri processes this data on your device. Sometimes it uses Apple’s Private Cloud Compute (PCC), which means the data is sent off your device to a cloud server. While you can try digging through the Apple Intelligence Report to figure out what’s sent to PCC, there’s no immediate visual indication from the user’s point of view when data leaves the device or when AI can handle it on the phone, iPad, or Mac itself. In practice, ask Siri AI a question and you’ll never really know if it’s being computed on device or off.

Apple claims what’s sent to PCC is not stored by the company after it is processed, but there are certain types of data or certain apps you might have on your phone that are not worth the risk. That’s especially true if you’re using a feature like Advanced Data Protection, which turns on end-to-end encryption for much of what’s stored in iCloud. Sending data that’s stored with end-to-end encryption off your device and into the cloud—no matter the privacy promises—is a fundamental change to the risk assessment you should make. “Private” means the system is engineered so that Apple shouldn’t be able to see or store the data, but it doesn’t mean it’s encrypted or doesn’t leave the device.

This leaves the privacy of certain apps up to a strange combination of an app developer’s choices and your own. You can, of course, disable Siri entirely (Settings > Siri > "Turn Off Siri"), or choose not to invoke Siri to ask questions, but perhaps you don’t want to fully disable or disengage with the system. Thankfully, you can put some guardrails on Siri AI’s access. Once you’ve updated to iOS 27, here are the steps to take. 

Note: only iPhone 15 Pro/Pro Max, as well as all models of the iPhone 16 and newer support Apple’s AI features. Siri AI is currently only available in English, and not available worldwide.

How to Restrict Siri’s Access to the Content Inside Apps

By default, how (and if) Siri AI can access data inside apps is up to the app developer. If an app developer chooses to index the contents of their app, then it may appear in search, and thus be made available to Siri AI. This means the content may pop up during general or direct searches, like “What are my plans for November" might cull information from your calendar, Messages, Notes, and, as they update, third-party apps.

If you do not want Siri to look through certain apps to consider the contents in results, you can tell it not to:

• Open Settings > Apps > [the app you don’t want Siri to look through] > Search
• Disable the option to “Show Content in Search.” 

With this setting disabled, when you ask Siri general questions, it will not surface details from the app you selected. For example, if you disable “Show Content in Search” for Messages, it will not be able to read your Messages conversations. 

Left: Asking Siri to summarize a message thread with Show Content in Search enabled. Right: With the setting disabled.

There is also an “App Access” setting where you can configure some of the ways Siri interacts with apps. You’d think this is where we’d have gone to revoke access to the content of an app, but alas, this settings page is more about some basic functionality with device personalization, not Siri’s access to the contents of the app.

• Open Settings > Siri > App Access
• Tap an app where you’d like to change Siri’s settings.

On this screen, you’ll find a variety of options, depending on what an app supports. “Learn from this App” sounds nefarious, but is mostly about tracking usage, like how often you open an app, and if a developer supports it, what you interact with.

The rest of the options are mostly about the personalization tweaks that Siri makes, where it suggests apps it thinks you want at the moment in various places, like when searching or sharing. “Show on Home Screen,” “Suggest App,” and “Suggest Notifications” are just about whether you see apps in those places. 

For example, if you have a widget of Siri-suggested apps on the home screen, that’s the “Show on Home Screen” toggle. If you see an app recommended in another app, like adding a date to your calendar from an email, that’s “Suggest App.” Apple claims these features all use on-device processing and the data is not stored on servers.

For anything not covered here, refer to this documentation for steps to disable certain features.

The On-Screen Awareness Capability May Be Concerning for Some People

There is one Siri AI feature you (and app developers) can’t do as much about: on-screen awareness, a feature you can invoke at any point to prompt Siri and ask it to explain what you’re looking at and perform certain actions. For example, you can ask it to summarize a web page, cut a recipe you’re reading in half, add an event to your calendar, or try to figure out where a photo was taken. All potentially useful features.

But you can also ask it to summarize or explain a Signal group chat that you're looking at, or a meme in a WhatsApp chat, and the data from that on-screen interaction may be sent to PCC. There is currently no way for you or app developers to block this feature, so it’s up to you, and those you chat with, to simply not use it if you’re concerned about the content of conversations potentially leaving your device. It would be a large improvement to privacy, especially secure chat apps, if Apple provided developers a means to block access to Siri AI’s on-screen awareness tool. Even better if they gave you a single control to block all Siri AI features from an app entirely.

Revoke Access to Training Data

By default, Siri AI won’t collect and use data from your interactions with it for training AI features. But during the setup process, Apple provides a way to opt in, which you might have tapped without thinking about it. If you’d rather your data not get used for training, you can opt out:

• Open Settings > Privacy & Security > Analytics & Improvements
• Disable the option for “Improve Siri & Dictation.” 

According to Apple’s privacy documentation, disabling this option should revoke training access to the audio and text from the Siri app.

Go Back to the Old Version of Siri (and Disable Other AI Features)

Want nothing to do with any of this but still find Siri useful enough to keep around (or you just have to keep it turned on in order to use CarPlay)? For the time being, you can get the old Siri back, though the process is a bit odd.

• Open up Settings > Screen Time > Content & Privacy Restrictions
• If you have never done so, enable the toggle for “Content & Privacy Restrictions.”
• Tap the Siri option, then “Allowed Siri Version.” 
• Select “Siri Classic.”

You can no longer easily disable Apple Intelligence entirely with one tap in the Settings, but on this screen you can also configure other AI features, like disabling the writing and math assistance prompts, turning off image creation, and disallowing the use of extensions. Follow this guide on Apple's site for everything else.

For the most part, Apple’s handling of AI features is far less in-your-face than others, and because of that the privacy implications are easier to untangle. But even still, it’s difficult to know what’s processed on device and what’s sent off, and so the privacy trade-offs are never spelled out as clearly as they should be. 

Apple could improve on this by offering an on-device only option for Siri AI and providing a clear, single setting toggle to prevent all AI features in a specific app (it looks like Apple is planning a single privacy toggle in a future update. We'll update if and when it does). In general, Siri’s power-up has also made it blurry and difficult to really figure out what sorts of privacy options exist. “Siri” means many things, both on device and off, ranging from “searching the entire internet for an answer” to “setting a timer,” and users have no straightforward ways to wrangle that data to suit their needs. As it stands, it’s a confusing collection of different toggles that never feel exactly right and which many users might struggle to grasp.

Secure messaging platforms, like Signal, WhatsApp, and recently, encrypted RCS, operate on a straightforward assumption: the content at each end of a conversation is private to the participants in the conversation. End-to-end encryption helps provide the mathematical guarantees that the companies who operate these messaging platforms cannot access the contents of messages. But there’s no way to guarantee what happens once the message arrives on a phone. As more devices and services introduce more artificial intelligence (AI) features into messaging apps, that line begins to blur. 

When AI features are computed entirely on device, it’s less concerning. Yet sometimes the computing requirements are heavy enough that the computation has to be done on a company server. Tech companies tell us they have a solution for this: trusted execution environments (TEEs). But do server-side TEEs really solve the problem?

TEEs exist to serve many different functions, ranging from digital rights management (DRM) content protections to securely storing information in your phone's mobile wallet, but for our purposes, we’ll be focusing on how tech companies use them for their AI tools. 

The basic idea is straightforward: most consumer devices aren’t powerful enough to handle the sorts of AI features companies want to offer, so sometimes they send data off your device to more powerful cloud servers to do the computing, then display the results on your device. Since your data is leaving your device, there’s a privacy compromise. For example, if you ask for a messaging app to summarize a conversation, it may offload that computing power to a cloud server, sending the entire contents of your messages to the cloud, then back to your phone.

TEEs supposedly offer a way to keep those requests private. There are several implementations out there, like Apple’s Private Cloud Compute, Google’s Private AI Compute, and WhatsApp’s Private Processing. It’s not just the big tech players, we’ve seen chatbots built with TEEs as well.

TEEs can provide more security and privacy than simply running in the clear, but they are fundamentally different from actual encryption or running locally. Despite the promises of some tech companies, they will never be able to match that level of security and privacy. Because of that, a user’s device should never automatically send data to a TEE. Let’s dig through the reasons why.

What Exactly Is a TEE, Anyway?

A TEE is a hardened section of the computer that runs software in a way that’s supposed to be secret even from other processes running on the machine. TEEs also let users check that the code being run is the code that they think is running, and not backdoored code instead, using a process called “attestation.” You may have also heard this referred to as a “secure enclave,” or heard the brand names SGX or TrustZone.

The intention of a cloud-based TEE is simple: a company can run a server in their data center, but still process data that you provide on your behalf without being able to see that information themselves.

Is a TEE Secure?

In practice, we've seen multiple cracks and hacks every year that show that it is possible to get at that data. That’s because while encryption relies on math, TEEs rely on engineering to provide their security. Standard encryption algorithms are created by years-long processes collaboratively produced by mathematicians around the world and are based on problems that have been studied for decades. The math is reliable, and there is no shortcut to breaking it that would not also upend fundamental understandings of mathematics as a field. 

The collective understanding of every mathematician in the world is that standard encryption algorithms are not breakable to the best of the world’s collective knowledge. No responsible engineer builds a system based on a new encryption method until after it’s been offered up for prodding.

Engineering, on the other hand, doesn’t work like that. Every individual system is the product of a group of engineers who put it out into the world, and each product will have its own quirks and bugs that have to be individually discovered and patched. These bugs are found after the system is built, not before. No one has yet built a system that is unbreakable. On the contrary, there is new research all the time that finds new ways to break into TEE systems. They’re patched as they come up, but they’re unlikely to ever become perfect, and certainly not any time soon. 

TEEs in particular are a hard engineering problem because the encryption key is physically right there on the device. Building a TEE means keeping a key fully separate and inaccessible while it’s on the same physical device as parts of the system that shouldn’t have access to the key.

Many attacks on TEEs involve “side channels.” In a side channel attack, the attacker measures the electrical impulses or other effects to figure out the timing of operations inside the TEE, then uses that to figure out the key being used. Once they have the key, they can read all the data. Compare that to end-to-end encryption, where the key is never on that machine in the first place, so an attacker would have to also run a similar attack on the user’s device.

Companies who turn to TEEs to protect data want to both have the key on the server and have it protected while still performing complex operations like running an LLM, which makes it much more difficult to protect those keys.

That being said, a TEE versus plaintext on a server is the difference between being able to easily read the data and having to do a bunch of specialized work to get at the data. That work often involves accessing the physical machine. This is most relevant for protecting against mass surveillance, and for many people, that might just be enough security.

But that's the core of the problem. “Secure enough for most cases” and “encrypted as in math” are not the same thing, and it’s important not to conflate the two. And services that currently offer “encryption as in math” have a real downgrade in security when they switch to security based on TEEs. 

If you want to dive into the myriad security issues and limitations of TEEs we’ve seen so far, they’re well documented here, here, here, and here.

What Does This Have To Do With LLMs and AI?

Sometimes organizations want to offer an LLM that can respond to queries in a private manner. On-device LLMs exist, but they’re limited in size. So, when organizations want to offer the ability to answer queries without being able to see the conversation, they turn to TEEs. That’s a useful way to run a chatbot that’s reasonably private. This is what Apple, Google, WhatsApp, and others are doing.

Why not turn to encryption? After all, LLM inference is just a bunch of math like any other things a computer does. It takes input to a (really big) function and gives an output. We have the math to do that computation in a way that hides the inputs and outputs from the one running the computation, it's just super expensive. It’s called homomorphic encryption, and no one’s figured out how to do it fast enough that it makes sense for this sort of computation.

Instead, the allure of a TEE is that it will run that computation for you inside of a special opaque section of a server. TEE manufacturers try to make it as hard as possible for the person running the TEE to peek inside. But you still have to trust the operator to not put a stethoscope to the box to try to figure out what's happening inside. 

In this case, it’s reasonable to consider these systems “privacy-preserving,” but not “encrypted.” That distinction is important, especially when we talk about how the TEEs interact with secure messaging. When someone using an end-to-end encrypted chat app asks an LLM to summarize, review, or store those messages, the content of those messages is leaving the device and going to an unencrypted third-party server somewhere. That’s a major threat to the privacy of secure chat apps, and one that’s increasingly hard for users to take control of.

How Does This Translate to Practical Advice?

The answer to this is going to vary based on an individual’s threat model, but a good rule of thumb is that a user’s device should never automatically send data to a TEE. When the person holding a phone can choose what information is sent, even if it’s a chunk of data like “unread messages,” they have the opportunity to pause and consider if that data might be too sensitive to risk sending.

In contrast, when data is sent automatically, the automatic sending becomes a feature of the system as a whole. If the system was previously end-to-end encrypted, adding automatic exfiltration makes the whole system no longer end-to-end encrypted.

Developers: don’t build systems that automatically send data off a device to a TEE, especially when it’s coming from an app that is otherwise end-to-end encrypted.

Users: if developers ignore us and build that system, turn off any automatic data sending features. Take a second to think about how much you’re willing to risk sending data when you choose to send it off the device.

So, What Should I Be Concerned About?

TEEs are useful for security in a number of circumstances. Your phone likely has a TEE where it keeps the key that encrypts your biometric unlock data and the base of the keychain where passwords are stored. It also enables certain backup systems, like how you can restore a phone with your passcode or restore WhatsApp or Signal backups.

But when we’re talking about cloud processing, it’s important to be clear this isn’t the same as end-to-end encryption and doesn’t offer the same level of privacy. 

Most of our private lives are on our phones and in our messages. We’ve worked for years to secure those messages, with major wins like encrypted RCS, and the continued user experience improvements of Signal and WhatsApp. We’ve even seen real improvements to backup security with features like Advanced Data Protection that bring end-to-end encryption for a variety of data outside of messaging, like notes and photos. 

But as companies roll out AI features that interact with these encrypted services, pulling data off devices and into a cloud-based TEE, they’re eroding the privacy protections of end-to-end encryption and risk causing serious confusion around what data is protected and what isn’t.

　エピロス王ピリュウスが外征の計画で「まずギリシアを征服しよう」という。側近のシネアスが「次は？」と聞くと「アフリカを手に入れる」。「次は？」とシネアス。「中央アジア、その次はアラビア…」。インドまで行ったところで「その後は？」と聞かれたピュリウスは「休息しよう」。シネアスは聞く。「それならなぜ今すぐ休息されないのですか」―。ボーヴォワールのエッセイで知られる「ピュリウスとシネアス」の寓話▼「働いて働いて働いて」、疲れ果ててなお進もうとする高市首相を見ると、やっぱり「なぜ..

Representatives from the APC membership, team and partners were among groups from around the world attending this year's Team CommUNITY Global Gathering to meaningfully explore the intersection…

ISC（Internet Systems Consortium）から、ISC BINDの複数の脆弱性が公開されました。

　ペルー独立記念日の７月28日、日系３世のケイコ・フジモリ大統領（51歳）が誕生する。 　ケイコの父は熊本県系移民２世の故アルベルト・フジモリ元大統領（任期１９９０～２０００年、24年９月死去）。父娘２代の日系大統領出現 […]

With doomsday AI scenarios dominating the news, lawmakers are rightly concerned about reports concerning security breaches at major US AI labs, such as the OpenAI–Hugging Face incident and the many others reported in its aftermath. As they consider potentially regulating frontier AI, they should focus any new legislation on the immediate, demonstrated risks from those incidents. 

Post-incident reports show that the Hugging Face incident could have been mitigated or prevented by following longstanding cybersecurity best practices, like stronger sandboxing and monitoring. Any new legislation should focus on closing gaps in existing law to prevent AI companies from taking unreasonable risks with the public's security.

When an AI developer or deployer runs a test or a task that has a high likelihood of causing harm to third parties—for instance, by breaking into someone else's computers—there should be clear minimum safety requirements. Such tests should run in a properly sandboxed test environment, disconnected from other systems, and be monitored and logged. Following these fundamental best practices would have prevented or substantially mitigated all of the incidents at AI labs that we currently know about.

That said, any proposal must be flexible enough to evolve with changing technology. Minimum safety requirements specific only to current AI technologies are likely to become obsolete; legal standards tied to well-established cybersecurity best practices are far more likely to stand the test of time. Tying any new mandates to evidence-backed security protocols also protects the public without impeding future AI development.

Strong legislation should also mandate and fund independent third-party investigations into any serious security incidents that may occur during AI labs’ tests of new tools, and make reports of these investigations available to the public. This important transparency measure would go a long way toward providing public oversight of the industry.

As with any technology regulation, those targeting cybersecurity practices at AI labs must be careful, precise, and practical.

米国CISAがCISA ICS Advisory / ICS Medical Advisoryを公表しました。

CERT/CCから本件に関するアドバイザリが公表されました。

「やんばる　いのちの森」写真展　１９周年記念企画 写真展 ヘリパッド完成後も抗議、監視活動を続けています。その中で出会った生き物、自然、今までの高江の風景などの写真を展示します。 日時：2026年10月16日㈮～18日㈰ 　　　10時～18時（18日は16時まで） 場所：名護博物館　ギャラリー 講演会 講演：やんばるの森を真の世界遺産に〜これまでの取り組みとこれからの取り組み〜 講師：吉川秀樹　Okinawa Environmental Justice Project 代表／ジュゴン保護キャンペーンセンター　国際担当IUCN種の保存委員会メンバー　琉球大学、名桜大学非常勤講師（文化人類学） 報告：「米国サンディエゴの軍事化と人々の抵抗」 講師 ：シミオン・マン　カリフォルニア大学サンディエゴ校　准教授（歴史学） 司会：KEN子 日時：2026年10月18日㈰ 　　　13時～15時 場所：名護博物館　体験学習室 開催場所；名護博物館 住所:名護市大中4丁目20ー50 ＊お願い　駐車場に限りがありますので、できる限り公共機関や乗り合わせでお越しくださいますようにおねがいいたします。 入場無料 主催：「ヘリパッドいらない」住民の会 ブログ：http://takae.ti-da.net tel：090-9789-6396 mail：info@nohelipadtakae.org

周波数再編アクションプラン（令和8年度版）（案）に対する意見募集