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PREVENTのユースケース:インパクトが大きい攻撃経路の特定

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22
Feb 2023
22
Feb 2023
This blog explains the benefits of thinking like an attacker and modeling attack paths in order to understand where you need to invest your defenses.

攻撃者によって侵害された場合、最も被害を受けるであろう人、プロセス、テクノロジー資産は何か?

攻撃経路モデリングは、最も重要な機密情報につながるすべての道の詳細なマップを提供し、可能性と潜在的な影響の順に優先順位を付けます。CISOは、セキュリティスタックを補完するために、この種のソリューションにますます注目しています。このソリューションは、組織の構造に特有のリスクや、悪用されると致命的となるデバイスやユーザー間の予期せぬ関係の可能性を明らかにするためです。  

Darktraceの攻撃経路モデリングソリューションの特徴は何ですか?

  • データソースは多様で、デジタルエステート全体からの情報が考慮される
  • モデリングはリアルタイムで、継続的に再評価される
  • 専門的な技術的知識がなくても活用できるアウトプット
  • 脆弱性の優先順位付けのためのスタンドアロン製品として活用
  • サイバーAIループの構成要素として、このソリューションは、DETECTおよびRESPOND (例:重要資産へのタグ付けによる検知)にフィードバックすることで即時価値を提供しますが、結果をフォローアップすることで長期的なシステム改善も実現します。

攻撃者のように考える

2023年、CISOは単なる保険やチェックボックスのコンプライアンスにとどまらず、アンダーライターが特定の種類のサイバー攻撃に対する除外規定を設けるようになります。運用保証を強化するのではなく、保護ボックスをチェックするコンプライアンスの限界がより明確になるにつれ、その立場を変えていくでしょう。彼らは、予算が削減される中でROIを最大化するために、よりプロアクティブなサイバーセキュリティ対策を選択し、サイバーレジリエンスを継続的に改善し、サイバーリスク低減を実証するツールや機能への投資にシフトするよう、チームに働きかけるでしょう。

レッドチームは、労力とリソースを最も即座に適用すべき場所についての洞察を提供することができますが、演習自体はコストがかかり、網羅的でなく、実行頻度も低いことが多いのです。

ハッカーは、システムの脆弱性を突いて攻撃するための経路、できれば最も抵抗の少ない経路を常に探し求めています。攻撃経路のモデル化により、セキュリティチームは攻撃者の視点から自分たちの環境を見ることができます。これにより、攻撃経路を段階的に排除し、攻撃者が壁を突破する際の選択肢を減らすことができます。

攻撃経路モデリングに関する詳細

攻撃経路とは、攻撃者がシステムの弱点を突くために取る経路を視覚的に表現したものです。攻撃経路は、脅威者が組織への入り口(攻撃対象領域)から貴重な資産にアクセスするまでの一連のステップ(攻撃ベクトル)を強調するものです。

通常、攻撃者が最も欲しいデータまでまっすぐに大通りを行くことは稀です。攻撃者は、いくつかの抜け道や予期せぬ関係、セキュリティスタックの死角を利用して、機密資産への道を切り開く可能性が高いのです。攻撃経路のモデリングは、このような侵入経路を形成するために必要な攻撃ベクトルを明らかにするのに役立ちます。  

図1:Darktrace PREVENT /End-To-Endのユーザーインターフェイスのスクリーンショット

攻撃経路をモデル化する方法

Darktraceは、独自の自己学習型AIで関係性をモデル化し、グラフ理論を取り入れることで、ユーザー、ドキュメント、これらの関係性の重要性を理解しています。

Darktrace PREVENTの攻撃経路モデリングコンポーネントは、ターゲットノード(ユーザー、アカウント、デバイス)を特定し、これらのターゲットノードへの最短経路を計算し、この攻撃経路の可能性とターゲット資産が侵害された場合の被害に応じて結果に重みを付けます。これは、攻撃者が攻撃を計画する際に行うことと全く同じですが、攻撃者よりも多くの情報にアクセスできるDarktrace PREVENTのAIエンジンに大きなアドバンテージがあると言えます。このとき初めて、防御側が攻撃側に対して優位に立つことができるのです。

サイロ化した取り組みの回避

ガートナーによると、75%の組織がセキュリティツールの統合を検討しています。これは、主にコスト的な理由ではなく、サイバーリスクの低減を促進するためです。これらの投資から最大限の利益を得るためには、セキュリティへの取り組みが、サイロ化した取り組みではなく、より広範なセキュリティエコシステムの一部であることを確認することが重要です。Darktraceの攻撃経路モデリングソリューションは、Darktrace PREVENT のエンドツーエンド(E2E)サービスのコンポーネントとして提供されています。

Darktrace PREVENT は、Darktrace のDETECT およびRESPOND と統合し、攻撃経路を排除する時間がない場合でも、組織のセキュリティ体制が強化されるようにします。

防御の優位性は重要であり、攻撃経路モデリングは、セキュリティチームが優位性を取り戻すための1つの方法です。攻撃経路モデリングは、セキュリティチームが優位性を取り戻すための1つの方法です。

しかし、攻撃経路モデリングは客観的なものであり、これらのモデルを作成するさまざまな方法を評価する際に考慮すべきいくつかの重要な疑問があります。

私の攻撃経路マップを構築する際、すべての関連データを考慮しているか?

マーケティング担当の重役の一人が、開発チームの誰かと親しい友人関係にある場合を考えてみましょう。このような場合、どのように攻撃経路をモデル化すればよいのでしょうか。攻撃経路はデジタル資産全体を包含するため、攻撃経路のモデリングソリューションは、内部および外部のさまざまな部分からの情報を考慮する必要があります。これには、Eメール環境、ネットワーク、エンドポイント、SaaSとクラウド、Active Directory、脆弱性スキャナなどからのデータが含まれることがあります。  

全体的な攻撃経路を把握するためには、データのクロス分析が唯一の方法です。

攻撃経路の最新のルートを理解しているのだろうか?

ユーザー、デバイス、その他の機密資産の関係は日々変化しており、これは攻撃経路が日々変化していることを意味します。セキュリティ担当者が組織のリスク状況を最新の状態で把握したいのであれば、使用する手法やソリューションが継続的かつリアルタイムに理解を更新していることを確認することが重要です。

セキュリティ体制を向上させるために、どのような攻撃経路から手をつければよいのか、どうすればよいのか?

一つは攻撃経路の総和をマップ化すること、もう一つは攻撃経路に優先順位をつけることです。攻撃経路のモデリングはマップを提供しますが、その上にリスク評価(以下で詳しく説明します)のレイヤーを追加することで、優先順位付けを行うことができます。そこで、グラフ理論が非常に有効であり、強化すべきチョークポイントを特定することができます。  

この出力から実用的な洞察が得られるか?

このソリューションの主な目的は、単にサイバーリスクの状況を評価することではなく、セキュリティ対策を正しい方向に導くことにあります。そのためには、サイバー技術の専門家ではないチームメンバーも利用できるような出力が必要です。利用可能な洞察と緩和のためのアドバイスによって参入障壁を低くすることが、組織のセキュリティ体制をうまく改善する鍵になります。

攻撃経路の優先順位付けのためのリスクアセスメント

Darktraceの攻撃経路モデリング(APM)は、サイバー攻撃の経路を評価するリスクベースのアプローチで、攻撃者の立場で考え、最も抵抗の少ない経路を探り当てるものです。この場合の「リスク」は、2つの要因の積として定義されます。「確率」と「影響力」です。この情報を使って、考えられる攻撃経路を以下のリスクマトリックスに分類することで、DarktraceのAPMは攻撃経路に優先順位を付け、セキュリティチームの労力を組織にとって最も関連性の高いリスクの制御に費やすことができるようにすることができます。

図2:攻撃経路の優先順位付けのためのリスクマトリックス

A: 確率の定義

確率には2つのタイプがあります。

攻撃者が組織に侵入するために、ある特定のドアを選択する可能性(攻撃対象地域の資産のうち、インターネットに面したサーバー、受信トレイ、SaaSおよびクラウドアカウントなどが考えられる)

ある特定のノード(デバイスまたはユーザーアカウントと定義される)が、ラテラルムーブメントによって次に侵害される可能性

図3: 侵入したエージェントが2つのサーバのいずれかに横移動する確率を計算する簡略化した例

B: インパクトの定義

インパクトとは、ある資産が侵害され使用できなくなった場合の総合的な影響度を指します。資産(例:キーサーバー)の場合、この資産が停止した場合の混乱が大きければ大きいほど、インパクトのスコアが高くなります。特定の文書を考える場合、アクセス制限やアクセスするユーザーの機密性スコアなどが影響度の推定に使われる変数の一部です。

図4:アクセス量と感度を対応させて文書価値を推定する簡略化した例を示す図

どちらの変数も、人間の入力を必要とせず、AIによって自律的に計算されます。もちろん、セキュリティチームはAIによる組織への理解をビジネス上の専門知識で補強することができます(たとえば、機密性の高いデバイスに追加でタグを付けるなど)。

Darktrace Attack Path Modeling モジュールを構成する他のコンポーネントと同様に、キーサーバーや機密文書を特定するためにどのように影響が伝播されるかについてのより詳細な説明は、このホワイトペーパーに記載されています。

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.
AUTHOR
ABOUT ThE AUTHOR
Elliot Stocker
Product SME

After 2 years in a commercial role helping to deploy Darktrace across a broad range of digital environments, Elliot currently occupies the role of Product Subject Matter Expert, where he helps to articulate the value of Darktrace’s technology to customers around the world. Elliot holds a Masters degree in Data Science and Machine Learning, using this knowledge to communicate concepts around machine learning and AI in an accessible way to different audiences.

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Gootloader Malware: Detecting and Containing Multi-Functional Threats with Darktrace

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15
Feb 2024

What is multi-functional malware?

While traditional malware variants were designed with one specific objective in mind, the emergence of multi-functional malware, such as loader malware, means that organizations are likely to be confronted with multiple malicious tools and strains of malware at once. These threats often have non-linear attack patterns and kill chains that can quickly adapt and progress quicker than human security teams are able to react. Therefore, it is more important than ever for organizations to adopt an anomaly approach to combat increasingly versatile and fast-moving threats.

Example of Multi-functional malware

One example of a multi-functional malware recently observed by Darktrace can be seen in Gootloader, a multi-payload loader variant that has been observed in the wild since 2020. It is known to primarily target Windows-based systems across multiple industries in the US, Canada, France, Germany, and South Korea [1].  

How does Gootloader malware work?

Once installed on a target network, Gootloader can download additional malicious payloads that allow threat actors to carry out a range of harmful activities, such as stealing sensitive information or encrypting files for ransom.

The Gootloader malware is known to infect networks via search engine optimization (SEO) poisoning, directing users searching for legitimate documents to compromised websites hosting a malicious payload masquerading as the desired file.

If the malware remains undetected, it paves the way for a second stage payload known as Gootkit, which functions as a banking trojan and information-stealer, or other malware tools including Cobalt Strike and Osiris [2].

Darktrace detection of Gootloader malware

In late 2023, Darktrace observed one instance of Gootloader affecting a customer in the US. Thanks to its anomaly-focused approach, Darktrace DETECT™ quickly identified the anomalous activity surrounding this emerging attack and brought it to the immediate attention of the customer’s security team. All the while, Darktrace RESPOND™ was in place and able to autonomously intervene, containing the suspicious activity and ensuring the Gootloader compromise could not progress any further.

In September 2023, Darktrace identified an instance of the Gootloader malware attempting to propagate within the network of a customer in the US. Darktrace identified the first indications of the compromise when it detected a device beaconing to an unusual external location and performing network scanning. Following this, the device was observed making additional command-and-control (C2) connections, before finally downloading an executable (.exe) file which likely represented the download of a further malicious payload.

As this customer had subscribed to the Proactive Notification Service (PTN), the suspicious activity was escalated to the Darktrace Security Operations Center (SOC) for further investigation by Darktrace’s expert analysts. The SOC team were able to promptly triage the incident and advise urgent follow-up actions.

Gootloader Attack Overview

Figure 1: Timeline of Anomalous Activities seen on the breach device.

Initial Beaconing and Scanning Activity

On September 21, 2023, Darktrace observed the first indications of compromise on the network when a device began to make regular connections to an external endpoint that was considered extremely rare for the network, namely ‘analyzetest[.]ir’.

Although the endpoint did not overtly seem malicious in nature (it appeared to be related to laboratory testing), Darktrace recognized that it had never previously been seen on the customer’s network and therefore should be treated with caution.  This initial beaconing activity was just the beginning of the malicious C2 communications, with several additional instances of beaconing detected to numerous suspicious endpoints, including funadhoo.gov[.]mv, tdgroup[.]ru’ and ‘army.mil[.]ng.

Figure 2: Initial beaconing activity detected on the breach device.

Soon thereafter, Darktrace detected the device performing internal reconnaissance, with an unusually large number of connections to other internal locations observed. This scanning activity appeared to primarily be targeting the SMB protocol by scanning port 445.

Within seconds of DETECT’s detection of this suspicious SMB scanning activity, Darktrace RESPOND moved to contain the compromise by blocking the device from connecting to port 445 and enforcing its ‘pattern of life’. Darktrace’s Self-Learning AI enables it to learn a device’s normal behavior and recognize if it deviates from this; by enforcing a pattern of life on an affected device, malicious activity is inhibited but the device is allowed to continue its expected activity, minimizing disruption to business operations.

Figure 3: The breach device Model Breach Event Log showing Darktrace DETECT identifying suspicious SMB scanning activity and the corresponding RESPOND actions.

Following the initial detection of this anomalous activity, Darktrace’s Cyber AI Analyst launched an autonomous investigation into the beaconing and scanning activity and was able to connect these seemingly separate events into one incident. AI Analyst analyzes thousands of connections to hundreds of different endpoints at machine speed and then summarizes its findings in a single pane of glass, giving customers the necessary information to assess the threat and begin remediation if necessary. This significantly lessens the burden for human security teams, saving them previous time and resources, while ensuring they maintain full visibility over any suspicious activity on their network.

Figure 4: Cyber AI Analyst incident log summarizing the technical details of the device’s beaconing and scanning behavior.

Beaconing Continues

Darktrace continued to observe the device carrying out beaconing activity over the next few days, likely representing threat actors attempting to establish communication with their malicious infrastructure and setting up a foothold within the customer’s environment. In one such example, the device was seen connecting to the suspicious endpoint ‘fysiotherapie-panken[.]nl’. Multiple open-source intelligence (OSINT) vendors reported this endpoint to be a known malware delivery host [3].

Once again, Darktrace RESPOND was in place to quickly intervene in response to these suspicious external connection attempts. Over the course of several days, RESPOND blocked the offending device from connecting to suspicious endpoints via port 443 and enforced its pattern of life. These autonomous actions by RESPOND effectively mitigated and contained the attack, preventing it from escalating further along the kill chain and providing the customer’s security team crucial time to take act and employ their own remediation.

Figure 5: A sample of the autonomous RESPOND actions that was applied on the affected device.

Possible Payload Retrieval

A few days later, on September 26, 2023, Darktrace observed the affected device attempting to download a Windows Portable Executable via file transfer protocol (FTP) from the external location ‘ftp2[.]sim-networks[.]com’, which had never previously been seen on the network. This download likely represented the next step in the Gootloader infection, wherein additional malicious tooling is downloaded to further cement the malicious actors’ control over the device. In response, Darktrace RESPOND immediately blocked the device from making any external connections, ensuring it could not download any suspicious files that may have rapidly escalated the attackers’ efforts.

Figure 6: DETECT’s identification of the offending device downloading a suspicious executable file via FTP.

The observed combination of beaconing activity and a suspicious file download triggered an Enhanced Monitoring breach, a high-fidelity DETECT model designed to detect activities that are more likely to be indicative of compromise. These models are monitored by the Darktrace SOC round the clock and investigated by Darktrace’s expert team of analysts as soon as suspicious activity emerges.

In this case, Darktrace’s SOC triaged the emerging activity and sent an additional notice directly to the customer’s security team, informing them of the compromise and advising on next steps. As this customer had subscribed to Darktrace’s Ask the Expert (ATE) service, they also had a team of expert analysts available to them at any time to aid their investigations.

Figure 7: Enhanced Monitoring Model investigated by the Darktrace SOC.

結論

Loader malware variants such as Gootloader often lay the groundwork for further, potentially more severe threats to be deployed within compromised networks. As such, it is crucial for organizations and their security teams to identify these threats as soon as they emerge and ensure they are effectively contained before additional payloads, like information-stealing malware or ransomware, can be downloaded.

In this instance, Darktrace demonstrated its value when faced with a multi-payload threat by detecting Gootloader at the earliest stage and responding to it with swift targeted actions, halting any suspicious connections and preventing the download of any additional malicious tooling.

Darktrace DETECT recognized that the beaconing and scanning activity performed by the affected device represented a deviation from its expected behavior and was indicative of a potential network compromise. Meanwhile, Darktrace RESPOND ensured that any suspicious activity was promptly shut down, buying crucial time for the customer’s security team to work with Darktrace’s SOC to investigate the threat and quarantine the compromised device.

Credit to: Ashiq Shafee, Cyber Security Analyst, Qing Hong Kwa, Senior Cyber Analyst and Deputy Analyst Team Lead, Singapore

付録

Darktrace DETECT によるモデル検知

Anomalous Connection / Rare External SSL Self-Signed

Device / Suspicious SMB Scanning Activity

Anomalous Connection / Young or Invalid Certificate SSL Connections to Rare

Compromise / High Volume of Connections with Beacon Score

Compromise / Beacon to Young Endpoint

Compromise / Beaconing Activity To External Rare

Compromise / Slow Beaconing Activity To External Rare

Compromise / Beacon for 4 Days

Anomalous Connection / Suspicious Expired SSL

Anomalous Connection / Multiple Failed Connections to Rare Endpoint

Compromise / Sustained SSL or HTTP Increase

Compromise / Large Number of Suspicious Successful Connections

Compromise / Large Number of Suspicious Failed Connections

Device / Large Number of Model Breaches

Anomalous File / FTP Executable from Rare External Location

Device / Initial Breach Chain Compromise

RESPOND Models

Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block

Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block

Antigena / Network/Insider Threat/Antigena Network Scan Block

Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Client Block

Antigena / Network / External Threat / Antigena Suspicious File Block

Antigena / Network / External Threat / Antigena File then New Outbound Block

Antigena / Network / External Threat / Antigena Suspicious Activity Block

侵害指標(IoC)一覧

Type

Hostname

IoCs + Description

explorer[.]ee - C2 Endpoint

fysiotherapie-panken[.]nl- C2 Endpoint

devcxp2019.theclearingexperience[.]com- C2 Endpoint

campsite.bplaced[.]net- C2 Endpoint

coup2pompes[.]fr- C2 Endpoint

analyzetest[.]ir- Possible C2 Endpoint

tdgroup[.]ru- C2 Endpoint

ciedespuys[.]com- C2 Endpoint

fi.sexydate[.]world- C2 Endpoint

funadhoo.gov[.]mv- C2 Endpoint

geying.qiwufeng[.]com- C2 Endpoint

goodcomix[.]fun- C2 Endpoint

ftp2[.]sim-networks[.]com- Possible Payload Download Host

MITRE ATT&CK マッピング

Tactic – Technique

Reconnaissance - Scanning IP blocks (T1595.001, T1595)

Command and Control - Web Protocols , Application Layer Protocol, One-Way Communication, External Proxy, Non-Application Layer Protocol, Non-Standard Port (T1071.001/T1071, T1071, T1102.003/T1102, T1090.002/T1090, T1095, T1571)

Collection – Man in the Browser (T1185)

Resource Development - Web Services, Malware (T1583.006/T1583, T1588.001/T1588)

Persistence - Browser Extensions (T1176)

参考文献

1.     https://www.blackberry.com/us/en/solutions/endpoint-security/ransomware-protection/gootloader

2.     https://redcanary.com/threat-detection-report/threats/gootloader/

3.     https://www.virustotal.com/gui/domain/fysiotherapie-panken.nl

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Ashiq Shafee
Cyber Security Analyst

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Seven Cyber Security Predictions for 2024

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13
Feb 2024

2024 Cyber Threat Predictions

After analyzing the observed threats and trends that have affected customers across the Darktrace fleet in the second half of 2023, the Darktrace Threat Research team have made a series of predictions. These assessments highlight the threats that are expected to impact Darktrace customers and the wider threat landscape in 2024.  

1. Initial access broker malware, especially loader malware, is likely to be a prominent threat.  

Initial access malware such as loaders, information stealers, remote access trojans (RATs), and downloaders, will probably remain some of the most relevant threats to most organizations, especially when noted in the context that many are interoperable, tailorable Malware-as-a-Service (MaaS) tools.  

These types of malware often serve as a gateway for threat actors to compromise a target network before launching subsequent, and often more severe, attacks. Would-be cyber criminals are now able to purchase and deploy these malware without the need for technical expertise.  

2. Infrastructure complexity will increase SaaS attacks and leave cloud environments vulnerable.

The increasing reliance on SaaS solutions and platforms for business operations, coupled with larger attack surfaces than ever before, make it likely that attackers will continue targeting organizations’ cloud environments with account takeovers granting unauthorized access to privileged accounts. These account hijacks can be further exploited to perform a variety of nefarious activities, such as data exfiltration or launching phishing campaigns.  

It is paramount for organizations to not only fortify their SaaS environments with security strategies including multifactor authentication (MFA), regular monitoring of credential usage, and strict access control, but moreover augment SaaS security using anomaly detection.  

3. The prevalence and evolution of ransomware will surge.

The Darktrace Threat Research team anticipates a surge in Ransomware-as-a-Service (RaaS) attacks, marking a shift away from conventional ransomware. The uptick in RaaS observed in 2023 evidences that ransomware itself is becoming increasingly accessible, lowering the barrier to entry for threat actors. This surge also demonstrates how lucrative RaaS is for ransomware operators in the current threat landscape, further reinforcing a rise in RaaS.  

This development is likely to coincide with a pivot away from traditional encryption-centric ransomware tactics towards more sophisticated and advanced extortion methods. Rather than relying solely on encrypting a target’s data for ransom, malicious actors are expected to employ double or even triple extortion strategies, encrypting sensitive data but also threatening to leak or sell stolen data unless their ransom demands are met.  

4. Threat actors will continue to rely on living-off-the-land techniques.

With evolving sophistication of security tools and greater industry adoption of AI techniques, threat actors have focused more and more on living-off-the-land. The extremely high volume of vulnerabilities discovered in 2023 highlights threat actors’ persistent need to compromise trusted organizational mechanisms and infrastructure to gain a foothold in networks. Although inbox intrusions remain prevalent, the exploitation of edge infrastructure has demonstrably expanded compared to previously endpoint-focused attacks.

Given the prevalence of endpoint evasion techniques and the high proportion of tactics utilizing native programs, threat actors will likely progressively live off the land, even utilizing new techniques or vulnerabilities to do so, rather than relying on unidentified malicious programs which evade traditional detection.

5. The “as-a-Service” marketplace will contribute to an increase in multi-phase compromises.

With the increasing “as-a-Service” marketplaces, it is likely that organizations will face more multi-phase compromises, where one strain of malware is observed stealing information and that data is sold to additional threat actors or utilized for second and/or third-stage malware or ransomware.  

This trend builds on the concept of initial access brokers but utilizes basic browser scraping and data harvesting to make as much profit throughout the compromise process as possible. This will likely result in security teams observing multiple malicious tools and strains of malware during incident response and/or multi-functional malware, with attack cycles and kill chains morphing into less linear and more abstract chains of activity. This makes it more essential than ever for security teams to apply an anomaly approach to stay ahead of asymmetric threats.  

6. Generative AI will let attackers phish across language barriers.

Classic phishing scams play a numbers game, targeting as many inboxes as possible and hoping that some users take the bait, even if there are spelling and grammar errors in the email. Now, Generative AI has reduced the barrier for entry, so malicious actors do not have to speak English to produce a convincing phishing email.  

In 2024, we anticipate this to extend to other languages and regions. For example, many countries in Asia have not yet been greatly impacted by phishing. Yet Generative AI continues to develop, with improved data input yielding improved output. More phishing emails will start to be generated in various languages with increasing sophistication.    

7. AI regulation and data privacy rules will stifle AI adoption.

AI regulation, like the European Union’s AI Act and NIS2, is starting to be implemented around the world. As policies continue to come out about AI and data privacy, practical and pragmatic AI adoption becomes more complex.  

Businesses will likely have to take a second look at AI they are adopting into their tech stacks to consider what may happen if a tool is suddenly deprecated because it is no longer fit for purpose or loses the approvals in place. Many will also have to use completely different supply chain evaluations from their usual ones based on developing compliance registrars. This increased complication may make businesses reticent to adopt innovative AI solutions as legislation scrambles to keep up.  

Learn more about observed threat trends and future predictions in the 2023 End of Year Threat Report

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