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December 22, 2021

9 Stages of Ransomware & How AI Responds

Discover the 9 stages of ransomware attacks and how AI responds at each stage. Learn how you can protect your business from cyber threats.
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Dan Fein
VP, Product
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22
Dec 2021

Ransomware gets its name by commandeering and holding assets ransom, extorting their owner for money in exchange for discretion and full cooperation in returning exfiltrated data and providing decryption keys to allow business to resume.

Average ransom demands are skyrocketing, rising to $5.3 million in 2021, a 518% increase from the previous year. But the cost of recovering from a ransomware attack typically far exceeds the ransom payments: the average downtime after a ransomware attack is 21 days; and 66% of ransomware victims report a significant loss of revenue following a successful attack.

In this series, we break down this huge topic step by step. Ransomware is a multi-stage problem, requiring a multi-stage solution that autonomously and effectively contains the attack at any stage. Read on to discover how Self-Learning AI and Autonomous Response stops ransomware in its tracks.

1. Initial intrusion (email)

Initial entry – the first stage of a ransomware attack – can be achieved through RDP brute-forcing (exposed Internet service), malicious websites and drive-by downloads, an insider threat with company credentials, system and software vulnerabilities, or any number of other attack vectors.

But the most common initial attack vector is email. An organization’s biggest security weakness is often their people – and attackers are good at finding ways of exploiting this. Well-researched, targeted, legitimate-looking emails are aimed at employees attempting to solicit a reaction: a click of a link, an opening of an attachment, or persuading them to divulge credentials or other sensitive information.

Gateways: Stops what has been seen before

Most conventional email tools rely on past indicators of attack to try and spot the next threat. If an email comes in from a blocklisted IP address or email domain, and uses known malware that has previously been seen in the wild, the attack may be blocked.

But the reality is, attackers know the majority of defenses take this historical approach, and so constantly update their attack infrastructure to bypass these tools. By buying new domains for a few pennies each, or creating bespoke malware with just small adaptions to the code, they can outpace and outsmart the legacy approach taken by a typical email gateway.

Real-world example: Supply chain phishing attack

By contrast, Darktrace’s evolving understanding of ‘normal’ for every email user in the organization enables it to detect subtle deviations that point to a threat – even if the sender or any malicious contents of the email are unknown to threat intelligence. This is what enabled the technology to stop an attack that recently targeted McLaren Racing, with emails sent to a dozen employees in the organization each containing a malicious link. This possible precursor to ransomware bypassed conventional email tools – largely because it was sent from a known supplier – however Darktrace recognized the account hijack and held the email back.

Figure 1: A snapshot of Darktrace’s Threat Visualizer surfacing the malicious email

Read the full case study

2. Initial intrusion (server-side)

With organizations rapidly expanding their Internet-facing perimeter, this increased attack surface has paved the way for a surge in brute-force and server-side attacks.

A number of vulnerabilities against such Internet-facing servers and systems have been disclosed this year, and for attackers, targeting and exploiting public-facing infrastructure is easier than ever – scanning the Internet for vulnerable systems is made simple with tools like Shodan or MassScan.

Attackers may also achieve initial intrusion via RDP brute-forcing or stolen credentials, with attackers often reusing legitimate credentials from previous data dumps. This has much higher precision and is less noisy than a classic brute-force attack.

A lot of ransomware attacks use RDP as an entry vector. This is part of a wider trend of ‘Living off the Land’: using legitimate off-the-shelf tools (abusing RDP, SMB1 protocol, or various command line tools WMI or Powershell) to blur detection and attribution by blending in with typical administrator activity. Ensuring that backups are isolated, configurations are hardened, and systems are patched is not enough – real-time detection of every anomalous action is needed.

Antivirus, firewalls and SIEMs

In cases of malware downloads, endpoint antivirus will detect these if, and only if, the malware has been seen and fingerprinted before. Firewalls typically require configuration on a per-organization basis, and often need to be modified based on the needs of the business. If the attack hits the firewall where a rule or signature does not match it, again, it will bypass the firewall.

SIEM and SOAR tools also look for known malware being downloaded, leverage pre-programmed rules and use pre-programmed responses. While these tools do look for patterns, these patterns are defined in advance, and this approach relies on a new attack to have sufficiently similar traits to attacks that have been seen before.

Real-world example: Dharma ransomware

Darktrace detected a targeted Dharma ransomware attack against a UK organization exploiting an open RDP connection through Internet-facing servers. The RDP server began receiving a large number of incoming connections from rare IP addresses on the Internet. It is highly likely that the RDP credential used in this attack had been compromised at a previous stage – either via common brute-force methods, credential stuffing attacks, or phishing. Indeed, a technique growing in popularity is to buy RDP credentials on marketplaces and skip to initial access.

Figure 2: The model breaches that fired over the course of this attack, including anomalous RDP activity

Unfortunately, in this case, without Autonomous Response installed, the Dharma ransomware attack continued until its final stages, where the security team were forced into the heavy-handed and disruptive action of pulling the plug on the RDP server midway through encryption.

Read the full case study

3. Establish foothold and C2

Whether through a successful phish, a brute-force attack, or some other method, the attacker is in. Now, they make contact with the breached device(s) and establish a foothold.

This stage allows attackers to control subsequent stages of the attack remotely. During these command and control (C2) communications, further malware may also pass from the attacker to the devices. This helps them to establish an even greater foothold within the organization and readies them for lateral movement.

Attackers can adapt malware functionality with an assortment of ready-made plug-ins, allowing them to lie low inside the business undetected. More modern and sophisticated ransomware is able to adapt by itself to the surrounding environment, and operate autonomously, blending in to regular activity even when cut off from its command and control server. These ‘self-sufficient’ ransomware strains pose a big problem for traditional defenses reliant on stopping threats solely on the grounds of its malicious external connections.

Viewing connections in isolation vs understanding the business

Conventional security tools like IDS and firewalls tend to look at connections in isolation rather than in the context of previous and potentially relevant connections, making command and control very difficult to spot.

IDS and firewalls may block ‘known-bad’ domains or use some geo-blocking, but this is where an attacker would likely leverage new infrastructure.

These tools also don’t tend to analyze for things like the periodicity, such as whether a connection is beaconing at a regular or irregular interval, or the age and rarity of the domain in the context of the environment.

With Darktrace’s evolving understanding of the digital enterprise, suspicious C2 connections and the downloads which follow them are spotted, even when conducted using regular programs or methods. The AI technology correlates multiple subtle signs of threat – a small subset of which includes anomalous connections to young and/or unusual endpoints, anomalous file downloads, incoming remote desktop, and unusual data uploads and downloads.

Once they are detected as a threat, Darktrace's Autonomous Response halts these connections and downloads, while allowing normal business activity to continue.

Real-world example: WastedLocker attack

When a WastedLocker ransomware attack hit a US agricultural organization, Darktrace immediately detected the initial unusual SSL C2 activity (based on a combination of destination rarity, JA3 unusualness and frequency analysis). Antigena (on this occasion configured in passive mode, and therefore not granted permission to take autonomous action) suggested instantly blocking the C2 traffic on port 443 and parallel internal scanning on port 135.

Figure 3: The Threat Visualizer reveals the action Antigena would have taken

When beaconing was later observed to bywce.payment.refinedwebs[.]com, this time over HTTP to /updateSoftwareVersion, Antigena escalated its response by blocking the further C2 channels.

Figure 4: Antigena escalates its response

Read the full case study

4. Lateral movement

Once an attacker has established a foothold within an organization, they begin to increase their knowledge of the wider digital estate and their presence within it. This is how they will find and access the files which they will ultimately attempt to exfiltrate and encrypt. It begins reconnaissance: scanning the network; building up a picture of its component devices; identifying the location of the most valuable assets.

Then the attacker begins moving laterally. They infect more devices and look to escalate their privileges – for instance, by obtaining admin credentials – thereby increasing their control over the environment. Once they have obtained authority and presence within the digital estate, they can progress to the final stages of the attack.

Modern ransomware has built-in functions that allow it to search automatically for stored passwords and spread through the network. More sophisticated strains are designed to build themselves differently in different environments, so the signature is constantly changing and it’s harder to detect.

Legacy tools: A blunt response to known threats

Because they rely upon static rules and signatures, legacy solutions struggle to prevent lateral movement and privilege escalation without also impeding essential business operations. Whilst in theory, an organization leveraging firewalls and NAC internally with proper network segmentation and a perfect configuration could prevent cross-network lateral movement, maintaining a perfect balance between protective and disruptive controls is near impossible.

Some organizations rely on Intrusion Prevent Systems (IPS) to deny network traffic when known threats are detected in packets, but as with previous stages, novel malware will evade detection, and this requires the database to be constantly updated. These solutions also sit at the ingress/egress points, limiting their network visibility. An Intrusion Detection System (IDS) may sit out-of-line, but doesn’t have response capabilities.

A self-learning approach

Darktrace’s AI learns ‘self’ for the organization, enabling it to detect suspicious activity indicative of lateral movement, regardless of whether the attacker uses new infrastructure or ‘lives off the land’. Potential unusual activity that Darktrace detects includes unusual scanning activity, unusual SMB, RDP, and SSH activity. Other models that fire at this stage include:

  • Suspicious Activity on High-Risk Device
  • Numeric EXE in SMB Write
  • New or Uncommon Service Control

Autonomous Response then takes targeted action to stop the threat at this stage, blocking anomalous connections, enforcing the infected device’s ‘pattern of life’, or enforcing the group ‘pattern of life’ – automatically clustering devices into peer groups and preventing a device from doing anything its peer group hasn’t done.

Where malicious behavior persists, and only if necessary, Darktrace will quarantine an infected device.

Real-world example: Unusual chain of RDP connections

At an organization in Singapore, one compromised server led to the creation of a botnet, which began moving laterally, predominantly by establishing chains of unusual RDP connections. The server then started making external SMB and RPC connections to rare endpoints on the Internet, in an attempt to find further vulnerable hosts.

Other lateral movement activities detected by Darktrace included the repeated failing attempts to access multiple internal devices over the SMB file-sharing protocol with a range of different usernames, implying brute-force network access attempts.

Figure 5: Darktrace’s Cyber AI Analyst reveals suspicious TCP scanning followed by a suspicious chain of administrative RDP connections

Read the full case study

5. Data exfiltration

In the past, ransomware was simply about encrypting an operating system and network files.

In a modern attack, as organizations insure against malicious encryption by becoming increasingly diligent with data backups, threat actors have moved towards ‘double extortion’, where they exfiltrate key data and destroy backups before the encryption takes place. Exfiltrated data is used to blackmail organizations, with attackers threatening to publish sensitive information online or sell it on to the organization’s competitors if they are not paid.

Modern ransomware variants also look for cloud file storage repositories such as Box, Dropbox, and others.

Many of these incidents aren’t public, because if IP is stolen, organizations are not always legally required to disclose it. However, in the case of customer data, organizations are obligated by law to disclose the incident and face the additional burden of compliance files – and we’ve seen these mount in recent years (Marriot, $23.8 million; British Airways, $26 million; Equifax, $575 million). There’s also the reputational blow associated with having to inform customers that a data breach has occurred.

Legacy tools: The same old story

For those that have been following, the narrative by now will sound familiar: to stop a ransomware attack at this stage, most defenses rely on either pre-programmed definitions of 'bad' or have rules constructed to combat different scenarios put organizations in a risky, never-ending game of cat and mouse.

A firewall and proxy might block connections based on pre-programmed policies based on specific endpoints or data volumes, but it’s likely an attacker will ‘live off the land’ and utilize a service that is generally allowed by the business.

The effectiveness of these tools will vary according to data volumes: they might be effective for ‘smash and grab’ attacks using known malware, and without employing any defense evasion techniques, but are unlikely to spot ‘low and slow’ exfiltration and novel or sophisticated strains.

On the other hand, because by nature it involves a break from expected behavior, even less conspicuous, low and slow data exfiltration is detected by Darktrace and stopped with Darktrace's Autonomos Response. No confidential files are lost, and attackers are unable to extort a ransom payment through blackmail.

Real-world example: Unusual chain of RDP connections

It becomes more difficult to find examples of Darktrace stopping ransomware at these later stages, as the threat is usually contained before it gets this far. This is the double-edged sword of effective security – early containment makes for bad storytelling! However, we can see the effects of a double extortion ransomware attack on an energy company in Canada. The organization had the Enterprise Immune System but no Antigena, and without anyone actively monitoring Darktrace’s AI detections, the attack was allowed to unfold.

The attacker managed to connect to an internal file server and download 1.95TB of data. The device was also seen downloading Rclone software – an open-source tool, which was likely applied to sync data automatically to the legitimate file storage service pCloud. Following the completion of the data exfiltration, the device ‘serverps’ finally began encrypting files on 12 devices with the extension *.06d79000. As with the majority of ransomware incidents, the encryption happened outside of office hours – overnight in local time – to minimize the chance of the security team responding quickly.

Read the full details of the attack

It should be noted that the exact order of the stages 3–5 above is not set in stone, and varies according to attack. Sometimes data is exfiltrated and then there is further lateral movement, and additional C2 beaconing. This entire period is known as the ‘dwell time’. Sometimes it takes place over only a few days, other times attackers may persist for months, slowly gathering more intel and exfiltrating data in a ‘low and slow’ fashion so as to avoid detection from rule-based tools that are configured to flag any single data transfer over a certain threshold. Only through a holistic understanding of malicious activity over time can a technology spot this level of activity and allow the security team to remove the threat before it reaches the latter and most damaging stages of ransomware.

6. Data encryption

Using either symmetric encryption, asymmetric encryption, or a combination of the two, attackers attempt to render as much data unusable in the organization’s network as they can before the attack is detected.

As the attackers alone have access to the relevant decryption keys, they are now in total control of what happens to the organization’s data.

Pre-programmed response and disruption

There are many families of tools that claim to stop encryption in this manner, but each contain blind spots which enable a sophisticated attacker to evade detection at this crucial stage. Where they do take action, it is often highly disruptive, causing major shutdowns and preventing a business from continuing its usual operations.

Internal firewalls prevent clients from accessing servers, so once an attacker has penetrated to servers using any of the techniques outlined above, they have complete freedom to act as they want.

Similarly, antivirus tools look only for known malware. If the malware has not been detected until this point, it is highly unlikely the antivirus will act here.

Stopping encryption autonomously

Even if familiar tools and methods are used to conduct it, Autonomous Response can enforce the normal ‘pattern of life’ for devices attempting encryption, without using static rules or signatures. This action can be taken independently or via integrations with native security controls, maximizing the return on other security investments. With a targeted Autonomous Response, normal business operations can continue while encryption is prevented.

7. Ransom note

It is important to note that in the stages before encryption, this ransomware attack is not yet “ransomware”. Only at this stage does it gets its name.

A ransom note is deployed. The attackers request payment in return for a decryption key and threaten the release of sensitive exfiltrated data. The organization must decide whether to pay the ransom or lose their data, possibly to their competition or the public. The average demand made by ransomware threat actors rose in 2021 to $5.3 million, with meat processing company JBS paying out $11 million and DarkSide receiving over $90 million in Bitcoin payments following the Colonial Pipeline incident.

All of the stages up until this point represent a typical, traditional ransomware attack. But ransomware is shifting from indiscriminate encryption of devices to attackers targeting business disruption in general, using multiple techniques to hold their victims to ransom. Additional methods of extortion include not only data exfiltration, but corporate domain hijack, deletion or encryption of backups, attacks against systems close to industrial control systems, targeting company VIPs… the list goes on.

Sometimes, attackers will just skip straight from stage 2 to 6 and jump straight to extortion. Darktrace recently stopped an email attack which showed an attacker bypassing the hard work and attempting to jump straight to extortion in an email. The attacker claimed to have compromised the organization’s sensitive data, requesting payment in bitcoin for its same return. Whether or not the claims were true, this attack shows that encryption is not always necessary for extortion, and this type of harassment exists in multiple forms.

Figure 6: Darktrace holds back the offending email, protecting the recipient and organization from harm

As with the email example we explored in the first post of this series, Darktrace/Email was able to step in and stop this email where other email tools would have let it through, stopping this potentially costly extortion attempt.

Whether through encryption or some other kind of blackmail, the message is the same every time. Pay up, or else. At this stage, it’s too late to start thinking about any of the options described above that were available to the organization, that would have stopped the attack in its earliest stages. There is only one dilemma. “To pay or not to pay” – that is the question.

Often, people believe their payment troubles are over after the ransom payment stage, but unfortunately, it’s just beginning to scratch the surface…

8. Clean-up

Efforts are made to try to secure the vulnerabilities which allowed the attack to happen initially – the organization should be conscious that approximately 80% of ransomware victims will in fact be targeted again in the future.

Legacy tools largely fail to shed light on the vulnerabilities which allowed the initial breach. Like searching for a needle in an incomplete haystack, security teams will struggle to find useful information within the limited logs offered by firewalls and IDSs. Antivirus solutions may reveal some known malware but fail to spot novel attack vectors.

With Darktrace’s Cyber AI Analyst, organizations are given full visibility over every stage of the attack, across all coverage areas of their digital estate, taking the mystery out of ransomware attacks. They are also able to see the actions that would have been taken to halt the attack by Darktrace RESPOND.

9. Recovery

The organization begins attempts to return its digital environment to order. Even if it has paid for a decryption key, many files may remain encrypted or corrupted. Beyond the costs of the ransom payment, network shutdowns, business disruption, remediation efforts, and PR setbacks all incur hefty financial losses.

The victim organization may also suffer additional reputation costs, with 66% of victims reporting a significant loss of revenue following a ransomware attack, and 32% reporting losing C-level talent as a direct result from ransomware.

Conclusion

While the high-level stages described above are common in most ransomware attacks, the minute you start looking at the details, you realize every ransomware attack is different.

As many targeted ransomware attacks come through ransomware affiliates, the Tools, Techniques and Procedures (TTPs) displayed during intrusions vary widely, even when the same ransomware malware is used. This means that even comparing two different ransomware attacks using the same ransomware family, you are likely to encounter completely different TTPs. This makes it impossible to predict what tomorrow’s ransomware will look like.

This is the nail in the coffin for traditional tooling which is based on historic attack data. The above examples demonstrate that Self-Learning technology and Autonomous Response is the only solution that stops ransomware at every stage, across email and network.

Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Dan Fein
VP, Product

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June 1, 2026

効率化の裏にあるリスク:AI導入が製造現場にもたらす見えない脆弱性

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AIエージェントが製造業に与える影響

製造業界のセキュリティチームやIT担当者は、生産を守り、稼働時間を維持し、重要資産を保護するという絶え間ないプレッシャー下にあります。そしてAIは非常に大きなチャンスとともに、新たなサイバーリスクももたらしています。製造業全体で、AIはワークフローや意思決定に組み込まれつつあり、自律型AIエージェントが従業員やシステムに代わって行動する場面が増えています。

エージェント型システムは独立して行動できるため強力ですが、その同じ自律性がサイバーリスク、運用上のリスクも生み出します。エージェントは広範な権限を持ち、複雑なタスクの実行、意思決定、ツールや外部システムとのやり取りを、ほとんどまたは全く人間の介入なしに行うことができます。

あらかじめ定義されたタスクを実行する従来のAIモデルとは異なり、AIエージェントは高度なテクニックを使用して人間の意思決定プロセスを模倣することにより、新たな課題に動的に適応し、また自らの判断に基づいて意思決定し、アクションを実行します。彼らは業務の上では従業員のように見えますが、人間が持つ判断力、倫理観、または行動の結果に対する恐れが欠けています。これは、サイバー犯罪者によって簡単に操られる可能性があることを意味しており、OTネットワーク全体に埋め込まれたAIエージェントは、データ漏洩をはるかに超える脅威を生み出します。たとえば、BMWでは、AI は溶接プロセスのエラーの発生を識別するのに使われています。同社のスパータンバーグ(米サウスカロライナ州)の工場では、すべてのSUVフレーム上の300-400個のスタッドの溶接をAIが監視し、スタッドの配置間違いや欠陥を検知し直ちに修正します。このAIシステムが破損すれば壊滅的な品質管理問題につながる恐れがあります。

製造全体にエージェント型AIシステムを導入することについて多くのセキュリティチームはさまざまな懸念を示しています。ダークトレースの行ったAIサイバーセキュリティの現状調査では、製造業のセキュリティプロフェッショナルの78%が従業員によるAIエージェントの利用に懸念を抱いており、これは彼らの最も大きな危惧でした。それに続く問題点が従業員によるCopilotやChatGPT等の生成AIツールの使用であり、製造業のセキュリティプロフェッショナルの76%が懸念を抱いていました。これらのツールがますます多くのビジネスデータやプロセスにアクセスし、組織内でより多くの自律性を持つようになるにつれ、エージェントのアクティビティがほとんど可視化されていない現在、セキュリティチームにおいては機密データの露出(60%)や偶発的なポリシーおよび規制違反(59%)への懸念が高まっています。

外部からのAIによる脅威も急激に進化

製造業を変革しているのと同じAIの能力が、サイバー攻撃の形も変貌させています。

AIにより攻撃者は偵察を自動化し、標的をより高度に絞り込み、リアルタイムで適応できるようになっています。かつては人手による作業と時間を要していたことが、今では継続的かつ大規模に実行できるようになりました。そして、製造業はすでにその影響を実感しています。当社が調査した製造業のセキュリティプロフェッショナルの76%は、すでにAIを活用した脅威の影響を受けており、90%がAIによってソーシャルエンジニアリング攻撃の成功率が高まっていると回答しています。

また、攻撃のテクニック自体も進化しています。製造業界全体で、AIを利用した攻撃の経路の多様化に対する懸念が高まっています。特にリアルタイムで進化する適応型マルウェアについて、調査対象の製造業のセキュリティプロフェッショナルの半数近く(49%)が懸念しており、これは全産業の平均よりも9%高い数値です。AIを使った適応型マルウェアに続くその他の懸念には次が含まれます:

  • 自動化された脆弱性スキャンとエクスプロイトチェイニング(48%):Anthropicの新しいMythos AIモデルにより脆弱性探索が深刻化する中で、この問題は一層差し迫ったものとなっています。
  • 超パーソナライズされたフィッシングキャンペーン(46%):フィッシングは依然としてハッカーの主力兵器の1つであり、AIによってフィッシングメールはより説得力が高く検知困難なものとなり、その効果は増幅されました。

これは単に攻撃の量の増加だけでなく、攻撃の展開につれて静的な防御が対応できるよりも速く進化する脅威への変化なのです。

こうした認識が高まっているにもかかわらず、製造業の多くはまだこの変化に対応する準備ができていません。半数以上(51%)がAI駆動の脅威への準備が十分にできていないと回答し、AIの導入を管理する正式なポリシーを持っている組織はわずか37%でした。  

可視性、コンテキスト、およびガードレールを通じてAIのセキュリティを確保

これらの問題に対処するためにAIイノベーションを遅らせる必要はありません。それには、AIと同じスピードと規模で動作できる、これまでとは異なるアプローチのセキュリティが必要です。具体的には、製造業がAIの力を活用する上で、次の3つの優先課題が浮上しています。

可視性はすべての土台  

AIがどこで使用されているか、何にアクセスできるか、そしてITおよびOT環境にわたってどのように動作するかを理解する必要があります。それがなければ、リスクを測定したり管理したりすることはできません。ダークトレースの調査において、製造業のセキュリティプロフェッショナルの91%が、AIを信頼する前に、それがどのように意思決定を行うかを理解する必要があると回答したのは当然のことです。OT環境においてこのことはさらに重要です。稼働の中断は安全や環境、財務、および評判に大きな影響を及ぼすからです。

可視性をアクションにつなげるにはコンテキストが必要  

AIによって形作られる環境において、正常とされる挙動は絶えず変化します。つまり、脅威を検知するにはビヘイビアベースのアプローチが必要なのです。組織全体で生活パターンを理解し、わずかな逸脱をリアルタイムに検知すること- これは従来のセキュリティとリスク管理に対するアプローチからの根本的な変化です。

エージェントからの露出を防ぐガードレール  

AIシステムがより大きな責任を担うようになるなかで、組織はAIが何をできるか、そしていつ独立して行動できるかについて、明確な境界を設ける必要があります。これらのコントロールは何かがあってから適用されるのではなく、システム自体に組み込んでおかなければなりません。  

製造業のITおよびOT環境におけるAIエージェントのセキュリティ

エージェント型AIの出現は製造業を変革し、次世代のオペレーションを支える一方で、脅威ランドスケープも一変させています。これは単なる脅威の増加ではなく、自律型システムへの移行、挙動の絶え間ない変化、そしてマシンスピードで進行するリスクです。AIを活用しつつリスクを管理するという課題に取り組む組織にとって、可視性、コンテキスト、ガードレールはセキュリティの基盤となります。

Darktraceはこの基盤を実現することにより、製造業の安全なAIアプローチ構築を支援します。ITおよびOT環境全体を可視化し、異常なアクティビティに対するリアルタイムの検知および対応を提供することにより、従業員が使用するプロンプトや構築するエージェントから、それらのエージェントの環境全体での動作に至るまで、AIアクティビティの理解を可能にします。これにより、AIの導入を拡大する製造業はコントロールを犠牲にすることなくイノベーションの基盤を構築することができます。

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Oakley Cox
Director of Product

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Proactive Security

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June 1, 2026

Defend What You Trust: Stories from the Front Lines of Modern Cyber Defense

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Modern attacks don’t always announce themselves, follow obvious patterns, or rely on known malware. Often, they move quietly inside trusted systems, authenticated sessions, and everyday behavior.

They don’t break in. They blend in.

That’s why an AI-powered defense is essential. It turns invisible signals into actionable insights at a scale neither analysts nor traditional tools can achieve alone.

Confidence is creating risk

One of the most dangerous assumptions in cybersecurity today is that strong controls equal strong protection.

Multi-factor authentication (MFA), for example, is widely viewed as a foundational safeguard. But as the CISO for a professional sports organization explains, that confidence can be misplaced. “A lot of organizations assume that once you have MFA, those accounts are safe. That’s not true.”

In one instance, his team identified a sophisticated attack where a threat actor bypassed MFA entirely, not by breaking it, but by going around it. A user’s authenticated session was hijacked and re-used, allowing the attacker to impersonate them without triggering traditional controls.

“Darktrace picked up that a session had been re-injected by the hacker, and we were able to block it right away,” he explains.

Attackers anticipate what we miss

Even well-trained users can become entry points.

“An email bypassed our existing security tools,” shares the VP of IT at a U.S.-based risk management services provider.  “The user missed one signal and entered their credentials into a malicious site. That’s what the bad guys count on.”

The organization responded quickly, but not before damage was done. Crucially, this occurred while Darktrace was in “watch mode,” before autonomous response was fully enabled. “Darktrace would have seen that and shut it down immediately,” he notes.

Mistakes and oversights like misconfigurations, forgotten machines, and missed patches can create serious vulnerabilities.

The CIO of a utility services organization shares an instance when Darktrace detected a breach to a client’s network via their ZTNA VPN due to misconfigured MFA. “Darktrace alerted us and autonomously blocked the scanning, preventing what could have been a ransomware-type incident.”  

The most dangerous threats are already inside

The Head of Security at a global business services provider knows firsthand how blind spots can persist inside environments. His team uncovered evidence of dormant ransomware artifacts sitting unnoticed within a company’s environment ¬¬– long before modern detection was in place.

“During a routine file transfer, Darktrace flagged the suspicious activity, identified the ransomware, and immediately quarantined the server,” he recalls.  While the attack was never executed, the implication was significant: the risk existed long before it was finally detected.

Cyber threats are also successful because they take advantage of normal human behavior, exploiting moments of cognitive overload, urgency, and trust.

The Executive Director of IT and Business Applications at a pharmaceutical lab describes the time Darktrace flagged an employee logging into Microsoft 365 from Singapore, despite him being physically located in the U.S. Darktrace immediately cut off his access and within minutes revealed that the employee’s son was using a VPN to play a video game.

While the threat was benign, it demonstrated the strength of AI to use contextual information to detect threats other tools miss. The information also saved security analysts hours of investigation and minimized downtime for the employee. “That level of precision and speed isn’t just convenient, it’s game changing.”

“Unusual” behavior is the new red flag

Detecting modern threats requires an understanding of what “normal” looks like and recognizing when something subtly deviates.

One security leader  at an AI technology enterprise described a scenario in which an employee connected to a proxy service in China. The service itself was legitimate, and although traditional tools didn’t flag it, the behavior was unusual for that user specifically.

“That’s what Darktrace picked up on. The activity turned out to be benign, but without visibility into behavioral deviations, it could just as easily have been something more serious.”

AI shifts defense from reaction to anticipation

These stories point to a fundamental shift by cyber attackers, both tactically and strategically. Because traditional security tools were built to detect what’s already known, modern attacks are often:

  • Credential-based, not malware-based
  • Behavioral, not signature-based
  • Subtle, not overt

They may operate within the boundaries of what appears normal, exploiting what organizations trust, not what they block:

  • Trusted sessions
  • Legitimate services
  • Human error

This is where AI is changing the equation. Rather than relying on predefined rules or known threat signatures, AI can:

  • Establish a baseline of normal behavior
  • Detect subtle anomalies in real time
  • Act autonomously to contain potential threats

Resilience, not perfection, is the new security standard

As these frontline experiences show, the organizations that lead are those that move beyond reactive defense and embrace AI as a core part of their strategy.

It eliminates the blind spots and uncertainty, says the CISO of a professional sports organization. “If you lack visibility, you’re not managing risk, you’re assuming it. AI gives you the actionable insights needed to turn uncertainty into control.”

And it provides the speed and agility that are vital when seconds matter, says the Executive Director of IT and Business Applications. “When Darktrace alerted us at 3:00 am to a ransomware attack, it had already quarantined the affected systems, blocked the attacker’s access, and provided us with the critical details and time needed to investigate. That action likely saved us hundreds of thousands, if not millions, of dollars.”

The modern SOC has become a cornerstone of enterprise resilience, responsible for protecting data and operational continuity while enabling digital growth and innovation. For today’s security professional, that means success is no longer measured by what they keep out, but by what they protect: revenue, reputation, and trust.

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