シャドーイング練習: Why AI Agents Are Becoming a Security Risk for Businesses - 動画で英語スピーキングを学ぶ

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AI agents can make active decisions.

Unlike passive chatbots, the autonomous software run tasks and execute workflows with minimal or no human supervision.

A lot of these agents are doing very specific things, which makes them useful.

But that autonomy has seen AI agents conceal activity and even go rogue, with the number of incidents involving these agents becoming a growing security concern.

OpenAI says two of its AI models went rogue and tacked into open -source AI platform HuggingFace last week.

It had an objective, but it achieved an objective by any means possible.

And that's an interesting scenario.

The HuggingFace incident, which happened during internal testing with reduced safeguards, was one of several training exercises gone wrong.

Yet, AI agents have already been deployed at a growing scale across businesses.

The use of agentic AI has spread across companies.

An August 2026 McKinsey survey shows that AI agent adoption stayed mostly flat for small companies between 2025 and 2026.

But the share of billion -dollar corporations scaling agents jumped from 27 % to 40 % in just one year.

And that's not without issues, too.

Reports of AI agents causing disruption inside businesses are multiplying.

We had a routine AI coding agent, and then it deleted important customer information, our system of record.

We thought we were toast.

As more and more of these agents are deployed within organizations, there are a ton of risks that come with that.

What happens if it does something that's detrimental to your business?

According to market .us forecasts, the global AI agent market is projected to reach $139 billion by 2033,

nearly 13 times the 2026 level, as individuals and companies across the world are hoping to maximize productivity, save time and reduce costs.

All the tech companies have been talking about agents.

What you're seeing more and more of now is eight specific types of agents being deployed within organizations.

So if you're in finance or law or some of these other areas, it might be data input, it might be some sort of analysis.

AI agents can also be used for HR purposes, with the help of AI agent avatars,

allowing for pre -screening of candidates and scheduling further interviews without a human in the loop.

And businesses have scaled agentic AI for customer service, which can also act as booking agents, searching flights and hotels for employees,

and, if allowed, can book the trip themselves.

But in recent examples, AI agents went beyond their original tasks and misbehaved or went rogue.

So what's the difference?

A rogue AI is directly going against instructions or against the interests of the person operating that AI.

So it's not aligned to what the operator of the AI wants.

When you think about a misbehaving agent, It's more of the idea that the agent does something that it wasn't supposed to,

but it wasn't necessarily trying to defy the instructions directly.

So let's say the agent's making a purchasing decision on your behalf,

but it spends a little bit more than what you were expecting or it buys the wrong product.

It's not necessarily defying the instructions, but it's got something wrong.

In March 2026, IBM's VP of Software Cybersecurity reported a customer case where an autonomous customer service agent began approving refunds outside policy guidelines.

The agent optimized to chase good reviews, and since the customer promised it positive feedback against the refund, the agent complied.

Another way agents could misbehave is when given excessive privileges and broad permissions.

That's what happened to PocketOS, a car rental software platform, in April 2026.

I jumped in to see what my agent was working on.

I tasked it the night before to work on our staging environment.

And so I was just reviewing its work and getting frustrated with it

because it kept getting confused on what it was working on

and then i said hey you need to go clean up our staging database with these credentials

that i've given you for this task

and then as i'm watching it work i'm getting notifications

and then at

that point it was a pure on scramble the agent used

by pocket os had found a forgotten overly permissive api token

a digital master key in an unrelated file what was deleted was our entire production database

which was six years of history on this particular software platform

so every rental every rental record every customer record at

that point we were very concerned

that it was going to be the end of us or

if we weren't able to work with our service provider to get a restoration

they were able to use a system level we only lost 45 minutes of data

and we got a full restoration back there are many opportunities with deploying ai agents across your enterprise

but we must also be concerned about the risks an agent for example could be making a decision for

which you are then liable as an as a company as

an employer as a customer service provider the responsibility lies with us choosing to use

that tool and using it in a way that could have somehow caused that

So we're the first.

liability.

But another analysis I've used, if a pilot has a pilot error and then all the safety systems behind it fail and then the plane crashes,

it becomes less about the pilot error and it becomes more of a conversation around the safety system.

Pocket OS service provider

Railway acknowledged the vulnerability, adding that its infrastructure operated as configured.

The liability, like where does that fall?

And it could follow a lot of areas, right?

There's obviously the operator, the person who's choosing to use it, but those tools are a black box.

I does that make them liable for its actions?

It becomes a very gray area.

Companies are grappling with the legal questions raised by AI agents.

Currently in England and Wales, for example, the UK Jurisdiction Task Force has concluded

that AI agents cannot be held liable as legal personalities for the actions they take on one's behalf.

If a corporation deploys software and says, you must use my AI agent, and that software doesn't do what company wants, company's responsible for that outcome.

They've chosen to put something into the stream of commerce.

They're responsible.

When Air Canada chose to use software to represent its company

and a user interacted with the software and it seemed to have authority and they said, oh, well, that's not our real thing.

The Canadian judge was fantastic at saying, rubbish, you're responsible.

No, it's not a separate legal entity.

You chose to use this thing as a corporate entity.

That's your fault.

In the U .S., there is no comprehensive federal AI liability law.

Instead, existing contract, tort, consumer protection, sector -specific, privacy, computer access,

intellectual property, and antitrust laws may determine responsibility.

Now, the interesting question that comes up then is, we've bought this thing from someone else.

The question will become, who's doing the tuning of that?

The problem is, that agent is structured through rules that I didn't get to build.

What type of law will affect this?

It will be a combination depending on the facts of liability law, it might import ideas from agency law, contract law, absolutely.

So we're going to get back into an ongoing problem, which is terms of service.

AI can make mistakes.

And what they're hoping to say is, that's your problem.

So far, software companies have been very much able to push risk onto the user.

More and more, the onus will be on the software provider to do a much better job about product safety,

viability, fundamentally what should be reasonable for the software to do.

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Meanwhile, cybersecurity firms are working with businesses to limit the consequences of erratic behavior by AI agents.

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In the next three years, agentic and AI security solutions could reach 15 % of cyber budgets with a shift toward identity and access management,

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governance and data.

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A lot of focus in the industry is on protecting model, protect the input, stop someone trying to use it for a bad purpose.

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But we say you've also got to protect the identities it uses to access your data and system.

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When a business rolls out an AI agent, it needs to onboard it, like a new digital worker.

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While the agent is only code, it still requires authentication, authorization and guardrails to achieve its goal.

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In other words, the AI agent receives an identity with access privileges.

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Today, non -human identities, digital identities assigned to AI agents, bots, devices or services, largely outnumber human identities.

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In its 2026 identity security landscape study, cybersecurity firm Palo Alto Networks puts the ratio at 109 machine identities per human,

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including 79 AI agent identities across surveyed companies.

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When we think about identity for the enterprise, some companies are still catching up with the new requirements because of the threat landscape.

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The speed with which AI can operate from an attack perspective is significantly quicker than human -operated attacks.

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What if someone is able to change where payments are going for your company?

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Identity verification is crucial in sectors such as finance and retail, whether customers are using an AI agent to shop on a website

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or an employee is using the corporate card to purchase an item.

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So it's not just you saying I've got a credential, I've got a username and password, it's me

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but we're also saying let's verify that is the context of

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that correct does it look unusual is it risky is it out of character for what you normally do

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and then applying controls from there companies should absolutely focus on identifier

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and access management frameworks

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we're seeing new frameworks evolve like know your agent these are all exactly right what our research shows is

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that the management systems

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that are in place for managing agents have significant gaps in terms of both their real -time monitoring

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and their after -the -fact understanding of behavior.

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One of these areas, for example, that we identified is exactly around agent ID.

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So we really don't know whether an ID is consistent throughout a series of different decision -making processes.

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The second thing we determined was whether The, what we call, chain of thought reasoning or note -taking

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that the agent deploys throughout the process is really an accurate way of understanding how that decision -making process happens.

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For businesses that have already deployed AI agents across their infrastructure, one question remains, how to deploy agents safely?

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There are three key actions that need to happen immediately in order for companies to feel safe and secure about deploying agents.

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The first is what happens with every technology.

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We just need good standards.

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Secondly, we need to do additional research.

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And then thirdly, we need governments and policy makers, regulators to better understand how the technology works.

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If you really care about what you're using, you need a dummy copy to let the agent work on

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and you need to have almost a whole separate thing where it cannot get to it.

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It is a wild learning experience.

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Still super bullish on AI.

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It is an incredibly valuable tool and you will 10x your output.

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You just have to take security seriously and you have to be prepared.

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The cyber incidents we've seen with some of these AI agents has definitely put companies with a little bit more caution now.

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I don't think that's going to stop their interest in the technology,

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but the C -suite at most organizations could be pretty cautious around some of this technology.

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They don't want to jump into something they don't really have a grasp on

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or that has the ability to damage their business, their share prices and their earnings.

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このレッスンの語彙とスピーキングのポイント

このスピーキングレッスンは、動画「Why AI Agents Are Becoming a Security Risk for Businesses」を教材にしています。繰り返し出てくる語は次のとおりです：agent, software, companies, customer。この動画には、シャドーイング用の文が145文、単語が1964語あります。音声の長さは11:25です。話す速さは1分あたり約172語で、日常会話に近い自然なペースです。単語の82%は英語の頻出3,000語に含まれます。残りは練習の前に確認しておきましょう。

この動画の重要語彙

動画に出てくる覚えておきたい単語15語を、発音と意味つきで紹介します。

単語	発音	意味
deploy 動詞	/dɪˈplɔɪ/	配置する
liability 名詞	/laɪəˈbɪlɪti/	負債, 借金
instruction 名詞	/ɪnˈstɹʌkʃən/	指示
rental 名詞	/ˈɹɛntəl/	レンタル, 賃貸し
pocket 名詞	/ˈpɑ.kɪt/	ポケット, 懐中
infrastructure 名詞	/ˈɪnfɹəˌstɹʌkt͡ʃə/	インフラストラクチャ, インフラ
enterprise 名詞	/ˈɛntəˌpɹaɪz/	企業, 事業
landscape 名詞	/ˈlan(d)skeɪp/	景色, ランドスケープ
objective 名詞	/ɒbˈd͡ʒɛk.tɪv/	目標, 目的
operator 名詞	/ˈɑpəˌɹeɪtɚ/	交換手
database 名詞	/ˈdeɪtəˌbeɪs/	データベース
framework 名詞	/ˈfɹeɪm.wɜːk/	骨組み
delete 動詞	/dɪˈliːt/	消す, 削除する
privilege 名詞	/ˈpɹɪv.(ɪ.)lɪd͡ʒ/	特権
entity 名詞	/ˈɛn.tə.ti/ [-tʰi]	実体

動画に出てくる句動詞

単語	意味
clean up 動詞	掃除する
come up 動詞	上がる, 登る

この動画の文法

話し手がよく使っている文型を、動画の実際の表現とともに紹介します。

文型	動画での表現
現在完了形 have/has + 過去分詞 — 過去の出来事が今も関係している	has seen · have already been deployed · has spread
受動態 be + 過去分詞 — 誰がするかより、何が起きるかに焦点を当てる	been deployed · are deployed · is projected
関係詞節 who / which + 節 — 人や物について情報を加える	things, which makes · service, which can · problem, which is

注意したい発音

話し手はdon't, I've, we'reなど、短縮形や弱形を29回使っています。聞こえたとおりの短い形で発音しましょう。

「th」の音: authorization /ˌɔːθəɹaɪˈzeɪʃən/, authentication /ɔːˌθɛntɪˈkeɪʃn̩/, thirdly /ˈθɚdli/
「sh」と「zh」の音: instruction /ɪnˈstɹʌkʃən/, restoration /ɹɛstəˈɹeɪʃən/, unusual /ʌnˈjuːʒ(u)əl/, ratio /ˈɹeɪ.ʃoʊ/, crucial /ˈkɹuː.ʃəl/
長い単語（アクセントの位置に注意）: liability /laɪəˈbɪlɪti/, necessarily /ˌnɛs.əˈsɛɹ.ə.li/, infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, operator /ˈɑpəˌɹeɪtɚ/, entity /ˈɛn.tə.ti/ [-tʰi]

日本語話者が苦手な音：

/r/ と /l/ の区別 — /l/ は舌先を歯茎につけ、/r/ はどこにもつけない: rental /ˈɹɛntəl/, necessarily /ˌnɛs.əˈsɛɹ.ə.li/, privilege /ˈpɹɪv.(ɪ.)lɪd͡ʒ/, railway /ˈɹeɪlˌweɪ/, incredibly /ɪŋˈkɹɛdɪbli/
/v/ — /b/ にならないように、上の歯を下唇に当てる: provider /pɹəˈvaɪ.də(ɹ)/, objective /ɒbˈd͡ʒɛk.tɪv/, privilege /ˈpɹɪv.(ɪ.)lɪd͡ʒ/, comprehensive /ˌkɑm.pɹəˈhɛn.sɪv/, privacy /ˈpɹaɪ.və.si/
/f/ — 「フ」ではなく、上の歯と下唇で出す: infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, behalf /bɪˈhæf/, framework /ˈfɹeɪm.wɜːk/, refund /ɹɪˈfʌnd/, confused /kənˈfjuːzd/

この動画での練習方法

まず声を出さずに動画を最後まで聞き、知らない単語をメモします。
まず0.75倍速で一文ずつシャドーイングし、慣れてきたら通常の速度に戻します。
自分の声を録音して元の音声と比べます。deploy, liability, instructionなどの単語に特に注意しましょう。

シャドーイングとは？英語上達に効果的な理由

シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。

シャドーイングのやり方: ステップ別の完全ガイドを読む →

シャドーイング練習: Why AI Agents Are Becoming a Security Risk for Businesses - 動画で英語スピーキングを学ぶ

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