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AIがWastedLocker攻撃を阻止した方法

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21
2020年12月
21
2020年12月
Darktrace recently detected and investigated a WastedLocker attack. This blog explores how this high-speed, high-stakes ransomware uses ‘Living off the Land’ techniques to bypass traditional security tools, and how Darktrace Antigena can autonomously stop this threat in its earliest stages, before encryption has begun.

2020年5月に最初に発見されて以来、WastedLockerはかなりの知名度を獲得し、短期間に世界中のビジネスやサイバーセキュリティ企業の間で大問題となりました。WastedLockerは高度な難読化能力と高額な身代金要求で知られています。

‘Living off the Land’ (環境に寄生する)テクニックの利用により、WastedLocker攻撃は従来のセキュリティツールでは極めて検知が難しいものとなっています。最初の侵入から最後の実行までの滞留時間がますます短くなっているということは、人間の対応者だけでは損害が起こる前にこのランサムウェアを封じ込めることは難しいことを意味します。

このブログでは、12月に発生した米国の農業団体を標的としたWastedLocker侵入事例を詳しく見ていきます。DarktraceのAIはインシデントをリアルタイムに検知し調査しました。またDarktrace RESPONDであれば暗号化が開始される前にどのように自律的に対応しこの攻撃を阻止できたかを紹介します。

ランサムウェアの滞留時間が数日から数時間に短縮されるなかで、ますます多くのセキュリティチームはAIを使って侵入の最も早い兆候があった段階で脅威がエスカレートするのを阻止し、攻撃が夜間または週末に発生しても封じ込められるように対策しています。

攻撃はどのように進んだか

図1: 攻撃のタイムライン

初期侵入

最初の感染は従業員が騙されて偽のブラウザアップデートをダウンロードしてしまったときに起こったものと見られます。Darktrace AIはこの組織内の約5,000台のデバイスの挙動を監視し、変化する「生活パターン」の理解を継続的に適応させていました。脅威の最初の兆候を検知したのは、仮想デスクトップ端末がこの組織にとって普通ではないと見なされた外部の接続先にHTTPおよびHTTPS接続を始めたときでした。以下のグラフは12月4日前後にゼロ号患者デバイスにおいて内部接続のスパイクがあったことを示しています。

図2:ゼロ号患者デバイスの示した内部接続のスパイク。オレンジ色の点はさまざまな深刻度のモデル違反を示している。

偵察

最初の侵入からわずか11分後には偵察の試みが始まりました。ここでも、Darktraceはこのアクティビティに即座に気づき、135、139および445番ポートに対する不信なICMP pingスキャンおよび特定のアドレススキャンが検知されました。これは攻撃者がさらなるWindowsデバイスを標的として探したものと思われます。以下の図は、異常な数の失敗した接続によってスキャニングが検知された様子を示しています。

図3:Darktraceが異常な数の失敗した接続を検知

ラテラルムーブメント

攻撃者は実際の管理者認証情報を使ってドメインコントローラで認証を行い、SMBを使って新しいサービスコントロールを開始しました。Darktraceはこれを即座に検知し、異常な挙動として識別しました。

図4:DarktraceがDCE-RPCリクエストを特定
図5:DarktraceがSMB書き込みを指摘

数時間後(未明のことでした)、攻撃者は一時的な管理者アカウント ‘tempadmin’ を使ってSMBを介して別のDomain Controllerに移動しました。Darktraceはこれを即座に検知しました。一時的な管理者アカウントを使って仮想デスクトップからDomain Controllerに接続することはきわめて異例だからです。

Figure 6: Further anomalous connections detected the following day

ロックとロード:WastedLockerによる攻撃の準備

ビーコニング活動中、攻撃者は内部偵察も行い、既にそこにあったツールを使って他の内部デバイスに対する管理者リモート接続を確立することに成功しました。その後ほどなく、疑わしい .csprojファイルがDarktraceによって検知され、さらに少なくとも4台のデバイスが同様のCommand and Control(C2)通信を始めました。

しかし、Darktraceのリアルタイム検知、およびCyber AI Analystによるインシデントの調査と報告が数分で行われたために、セキュリティチームはこの攻撃を封じ込め、感染したデバイスをオフラインにすることができました。

Cyber AI Analystによる自動調査

DarktraceのCyber AI Analystはすべての異常検知に対する自動調査を開始し、仮説を立て、見つかった結果を問い直し、正確な答えをマシンスピードで作成していきました。そのうえで、ハイレベルでわかりやすいインシデントサマリーをセキュリティチーム向けに生成しました。48時間でAI Analystが洗い出したセキュリティインシデントは6件だけでしたが、そのうちの3件は直接WastedLocker侵入に関連したものでした。

図7:Cyber AI Analyst脅威トレイ

以下のスクリーンショットはVMWareデバイス(ゼロ号患者)が未知の通信先に繰り返し外部接続を行い、ネットワークをスキャンし新しい管理者認証情報を使っている状況が示されています。

図8:Cyber AI Analystによる調査

Darktrace RESPOND: セキュリティチームに代わって対応するAI

世界初、そして唯一の自動遮断テクノロジーであるDarktrace RESPONDはこのケースではパッシブモードに設定されており、攻撃に対して積極的に介入しませんでした。しかしThreat Visualizerを確認すると、RESPONDが完全な自律モードに設定されていれば攻撃の初期段階で対処しセキュリティチームにとっての貴重な時間を稼ぐことができたであろうことがわかります。

このケースでは、最初の異常なSSL C2を検知(通信先の珍しさ、JA3の異変および頻度の分析に基づき)した後、RESPONDは443番ポートのC2トラフィックおよび135番ポートの並列内部スキャニングを即座にブロックすることを推奨していました。

図9:Threat VisualizerにはAntigenaが取ることのできたアクションが示されている

ビーコニングがその後、bywce.payment.refinedwebs[.]com]に対して、HTTPで /updateSoftwareVersionにアクセスしようとすると、RESPONDは対応をエスカレートさせ、以降C2チャネルをブロックしました。

図10:RESPONDが対応をエスカレート

対応のためのツールのほとんどは、「もしXならばYを実行する」といった、ハードコードされた定義済みのルールに依存しています。これは偽陽性の発生につながり、不必要にデバイスをオフラインにして生産性を阻害する可能性があります。Darktrace RESPONDのアクションは程度に応じた、その組織に合ったものであり、前もって作成するものではありません。Darktrace RESPONDは何をブロックし、どの程度ブロックするかを侵入のコンテキストに基づいて自律的に選択し、人間がコマンドや応答のセットをうまくハードコードしておく必要はありません。

48時間内のあらゆる対応はこのインシデントに関係していました。RESPONDは侵入の期間中、他のことに対するアクションを取ろうとはしませんでした。脅威を封じ込めるための正確で的を絞ったアクションだけを実行し、ビジネスの他の部分は通常通り続けることができたはずです。インシデント発生中のアクションは全部で59件ありました。ただし以下に示された ‘Watched Domain Block’ は除いた数です。これはインシデント対処として事前対応的にC2通信をシャットダウンするのに使われるアクションです。

図11:組織全体で侵入期間中にRESPONDが取ろうとしたすべてのアクション

RESPONDはこれらのブロックを、ファイアウォール、NACLあるいはその他のネイティブインテグレーションを含めその組織にとって最適なインテグレーションにより実行できたはずです。RESPONDは関連するポートおよびプロトコルに対してこれらの悪意あるアクティビティを数時間にわたりブロックし、脅威アクターの侵入アクティビティに正確に的を絞って介入することによりさらなるエスカレーションを防ぎ、セキュリティチームに上空からの援護射撃を提供していたことでしょう。

WastedLockerランサムウェアを暗号化される前に阻止

この攻撃はシグネチャベースのツールをすり抜けるために多くの注意すべきTTP(Tools, Techniques and Procedures)を使っていました。Windows Management Instrumentation (WMI)、Powershell、ならびにデフォルトの管理者認証情報の使用など、‘Living off the Land’ (環境に寄生する)テクニックを利用していました。関与したC2ドメインのうち1つだけがOpen Source Intelligence Lists(OSINT)にふくまれており、他のドメインはその時点では未知のものでした。また、C2は正当なThawte SSL証明書で暗号化されていました。

こうした理由から、Darktraceが導入されていなかった場合、ランサムウェアはファイルの暗号化に成功し、この厳しい状況下でビジネスオペレーションを妨げ、場合によってはこの組織に莫大な金銭的損失と評判の毀損を招いていたかもしれません。

DarktraceのAIは脅威インテリジェンスに頼ることなく進行中のランサムウェアを検知し阻止します。今年はランサムウェアが勢いを強め、攻撃者達は絶え間なく新しい攻撃TTPを打ち出してきました。しかし、上記の脅威検知事例は、標的型の巧妙なランサムウェアであってもAIテクノロジーによって阻止できることを実証しています。

この脅威検知についての考察はDarktraceアナリストSigne Zaharka が協力しました。

自動遮断技術についてもっと知る

Darktraceによるモデル検知:

  • Compliance / High Priority Compliance Model Breach
  • Compliance / Weak Active Directory Ticket Encryption
  • Anomalous Connection / Cisco Umbrella Block Page
  • Anomalous Server Activity / Anomalous External Activity from Critical Network Device
  • Compliance / Default Credential Usage
  • Compromise / Suspicious TLS Beaconing To Rare External
  • Anomalous Server Activity / Rare External from Server
  • Device / Lateral Movement and C2 Activity
  • Compromise / SSL Beaconing to Rare Destination
  • Device / New or Uncommon WMI Activity
  • Compromise / Watched Domain
  • Antigena / Network / External Threat / Antigena Watched Domain Block
  • Compromise / HTTP Beaconing to Rare Destination
  • Compromise / Slow Beaconing Activity To External Rare
  • Device / Multiple Lateral Movement Model Breaches
  • Compromise / High Volume of Connections with Beacon Score
  • Device / Large Number of Model Breaches
  • Compromise / Beaconing Activity To External Rare
  • Antigena / Network / Significant Anomaly / Antigena Controlled and Model Breach
  • Anomalous Connection / New or Uncommon Service Control
  • Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block
  • Compromise / SSL or HTTP Beacon
  • Antigena / Network / External Threat / Antigena Suspicious Activity Block
  • Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block
  • Compromise / Sustained SSL or HTTP Increase
  • Unusual Activity / Unusual Internal Connections
  • Device / ICMP Address Scan


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
Max Heinemeyer
Chief Product Officer

Max is a cyber security expert with over a decade of experience in the field, specializing in a wide range of areas such as Penetration Testing, Red-Teaming, SIEM and SOC consulting and hunting Advanced Persistent Threat (APT) groups. At Darktrace, Max is closely involved with Darktrace’s strategic customers & prospects. He works with the R&D team at Darktrace, shaping research into new AI innovations and their various defensive and offensive applications. Max’s insights are regularly featured in international media outlets such as the BBC, Forbes and WIRED. Max holds an MSc from the University of Duisburg-Essen and a BSc from the Cooperative State University Stuttgart in International Business Information Systems.

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A Thorn in Attackers’ Sides: How Darktrace Uncovered a CACTUS Ransomware Infection

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24
Apr 2024

What is CACTUS Ransomware?

In May 2023, Kroll Cyber Threat Intelligence Analysts identified CACTUS as a new ransomware strain that had been actively targeting large commercial organizations since March 2023 [1]. CACTUS ransomware gets its name from the filename of the ransom note, “cAcTuS.readme.txt”. Encrypted files are appended with the extension “.cts”, followed by a number which varies between attacks, e.g. “.cts1” and “.cts2”.

As the cyber threat landscape adapts to ever-present fast-paced technological change, ransomware affiliates are employing progressively sophisticated techniques to enter networks, evade detection and achieve their nefarious goals.

How does CACTUS Ransomware work?

In the case of CACTUS, threat actors have been seen gaining initial network access by exploiting Virtual Private Network (VPN) services. Once inside the network, they may conduct internal scanning using tools like SoftPerfect Network Scanner, and PowerShell commands to enumerate endpoints, identify user accounts, and ping remote endpoints. Persistence is maintained by the deployment of various remote access methods, including legitimate remote access tools like Splashtop, AnyDesk, and SuperOps RMM in order to evade detection, along with malicious tools like Cobalt Strike and Chisel. Such tools, as well as custom scripts like TotalExec, have been used to disable security software to distribute the ransomware binary. CACTUS ransomware is unique in that it adopts a double-extortion tactic, stealing data from target networks and then encrypting it on compromised systems [2].

At the end of November 2023, cybersecurity firm Arctic Wolf reported instances of CACTUS attacks exploiting vulnerabilities on the Windows version of the business analytics platform Qlik, specifically CVE-2023-41266, CVE-2023-41265, and CVE-2023-48365, to gain initial access to target networks [3]. The vulnerability tracked as CVE-2023-41266 can be exploited to generate anonymous sessions and perform HTTP requests to unauthorized endpoints, whilst CVE-2023-41265 does not require authentication and can be leveraged to elevate privileges and execute HTTP requests on the backend server that hosts the application [2].

Darktrace’s Coverage of CACTUS Ransomware

In November 2023, Darktrace observed malicious actors leveraging the aforementioned method of exploiting Qlik to gain access to the network of a customer in the US, more than a week before the vulnerability was reported by external researchers.

Here, Qlik vulnerabilities were successfully exploited, and a malicious executable (.exe) was detonated on the network, which was followed by network scanning and failed Kerberos login attempts. The attack culminated in the encryption of numerous files with extensions such as “.cts1”, and SMB writes of the ransom note “cAcTuS.readme.txt” to multiple internal devices, all of which was promptly identified by Darktrace DETECT™.

While traditional rules and signature-based detection tools may struggle to identify the malicious use of a legitimate business platform like Qlik, Darktrace’s Self-Learning AI was able to confidently identify anomalous use of the tool in a CACTUS ransomware attack by examining the rarity of the offending device’s surrounding activity and comparing it to the learned behavior of the device and its peers.

Unfortunately for the customer in this case, Darktrace RESPOND™ was not enabled in autonomous response mode during their encounter with CACTUS ransomware meaning that attackers were able to successfully escalate their attack to the point of ransomware detonation and file encryption. Had RESPOND been configured to autonomously act on any unusual activity, Darktrace could have prevented the attack from progressing, stopping the download of any harmful files, or the encryption of legitimate ones.

Cactus Ransomware Attack Overview

Holiday periods have increasingly become one of the favoured times for malicious actors to launch their attacks, as they can take advantage of the festive downtime of organizations and their security teams, and the typically more relaxed mindset of employees during this period [4].

Following this trend, in late November 2023, Darktrace began detecting anomalous connections on the network of a customer in the US, which presented multiple indicators of compromise (IoCs) and tactics, techniques and procedures (TTPs) associated with CACTUS ransomware. The threat actors in this case set their attack in motion by exploiting the Qlik vulnerabilities on one of the customer’s critical servers.

Darktrace observed the server device making beaconing connections to the endpoint “zohoservice[.]net” (IP address: 45.61.147.176) over the course of three days. This endpoint is known to host a malicious payload, namely a .zip file containing the command line connection tool PuttyLink [5].

Darktrace’s Cyber AI Analyst was able to autonomously identify over 1,000 beaconing connections taking place on the customer’s network and group them together, in this case joining the dots in an ongoing ransomware attack. AI Analyst recognized that these repeated connections to highly suspicious locations were indicative of malicious command-and-control (C2) activity.

Cyber AI Analyst Incident Log showing the offending device making over 1,000 connections to the suspicious hostname “zohoservice[.]net” over port 8383, within a specific period.
Figure 1: Cyber AI Analyst Incident Log showing the offending device making over 1,000 connections to the suspicious hostname “zohoservice[.]net” over port 8383, within a specific period.

The infected device was then observed downloading the file “putty.zip” over a HTTP connection using a PowerShell user agent. Despite being labelled as a .zip file, Darktrace’s detection capabilities were able to identify this as a masqueraded PuttyLink executable file. This activity resulted in multiple Darktrace DETECT models being triggered. These models are designed to look for suspicious file downloads from endpoints not usually visited by devices on the network, and files whose types are masqueraded, as well as the anomalous use of PowerShell. This behavior resembled previously observed activity with regards to the exploitation of Qlik Sense as an intrusion technique prior to the deployment of CACTUS ransomware [5].

The downloaded file’s URI highlighting that the file type (.exe) does not match the file's extension (.zip). Information about the observed PowerShell user agent is also featured.
Figure 2: The downloaded file’s URI highlighting that the file type (.exe) does not match the file's extension (.zip). Information about the observed PowerShell user agent is also featured.

Following the download of the masqueraded file, Darktrace observed the initial infected device engaging in unusual network scanning activity over the SMB, RDP and LDAP protocols. During this activity, the credential, “service_qlik” was observed, further indicating that Qlik was exploited by threat actors attempting to evade detection. Connections to other internal devices were made as part of this scanning activity as the attackers attempted to move laterally across the network.

Numerous failed connections from the affected server to multiple other internal devices over port 445, indicating SMB scanning activity.
Figure 3: Numerous failed connections from the affected server to multiple other internal devices over port 445, indicating SMB scanning activity.

The compromised server was then seen initiating multiple sessions over the RDP protocol to another device on the customer’s network, namely an internal DNS server. External researchers had previously observed this technique in CACTUS ransomware attacks where an RDP tunnel was established via Plink [5].

A few days later, on November 24, Darktrace identified over 20,000 failed Kerberos authentication attempts for the username “service_qlik” being made to the internal DNS server, clearly representing a brute-force login attack. There is currently a lack of open-source intelligence (OSINT) material definitively listing Kerberos login failures as part of a CACTUS ransomware attack that exploits the Qlik vulnerabilities. This highlights Darktrace’s ability to identify ongoing threats amongst unusual network activity without relying on existing threat intelligence, emphasizing its advantage over traditional security detection tools.

Kerberos login failures being carried out by the initial infected device. The destination device detected was an internal DNS server.
Figure 4: Kerberos login failures being carried out by the initial infected device. The destination device detected was an internal DNS server.

In the month following these failed Kerberos login attempts, between November 26 and December 22, Darktrace observed multiple internal devices encrypting files within the customer’s environment with the extensions “.cts1” and “.cts7”. Devices were also seen writing ransom notes with the file name “cAcTuS.readme.txt” to two additional internal devices, as well as files likely associated with Qlik, such as “QlikSense.pdf”. This activity detected by Darktrace confirmed the presence of a CACTUS ransomware infection that was spreading across the customer’s network.

The model, 'Ransom or Offensive Words Written to SMB', triggered in response to SMB file writes of the ransom note, ‘cAcTuS.readme.txt’, that was observed on the customer’s network.
Figure 5: The model, 'Ransom or Offensive Words Written to SMB', triggered in response to SMB file writes of the ransom note, ‘cAcTuS.readme.txt’, that was observed on the customer’s network.
CACTUS ransomware extensions, “.cts1” and “.cts7”, being appended to files on the customer’s network.
Figure 6: CACTUS ransomware extensions, “.cts1” and “.cts7”, being appended to files on the customer’s network.

Following this initial encryption activity, two affected devices were observed attempting to remove evidence of this activity by deleting the encrypted files.

Attackers attempting to remove evidence of their activity by deleting files with appendage “.cts1”.
Figure 7: Attackers attempting to remove evidence of their activity by deleting files with appendage “.cts1”.

結論

In the face of this CACTUS ransomware attack, Darktrace’s anomaly-based approach to threat detection enabled it to quickly identify multiple stages of the cyber kill chain occurring in the customer’s environment. These stages ranged from ‘initial access’ by exploiting Qlik vulnerabilities, which Darktrace was able to detect before the method had been reported by external researchers, to ‘actions on objectives’ by encrypting files. Darktrace’s Self-Learning AI was also able to detect a previously unreported stage of the attack: multiple Kerberos brute force login attempts.

If Darktrace’s autonomous response capability, RESPOND, had been active and enabled in autonomous response mode at the time of this attack, it would have been able to take swift mitigative action to shut down such suspicious activity as soon as it was identified by DETECT, effectively containing the ransomware attack at the earliest possible stage.

Learning a network’s ‘normal’ to identify deviations from established patterns of behaviour enables Darktrace’s identify a potential compromise, even one that uses common and often legitimately used administrative tools. This allows Darktrace to stay one step ahead of the increasingly sophisticated TTPs used by ransomware actors.

Credit to Tiana Kelly, Cyber Analyst & Analyst Team Lead, Anna Gilbertson, Cyber Analyst

付録

参考文献

[1] https://www.kroll.com/en/insights/publications/cyber/cactus-ransomware-prickly-new-variant-evades-detection

[2] https://www.bleepingcomputer.com/news/security/cactus-ransomware-exploiting-qlik-sense-flaws-to-breach-networks/

[3] https://explore.avertium.com/resource/new-ransomware-strains-cactus-and-3am

[4] https://www.soitron.com/cyber-attackers-abuse-holidays/

[5] https://arcticwolf.com/resources/blog/qlik-sense-exploited-in-cactus-ransomware-campaign/

Darktrace DETECT Models

Compromise / Agent Beacon (Long Period)

Anomalous Connection / PowerShell to Rare External

Device / New PowerShell User Agent

Device / Suspicious SMB Scanning Activity

Anomalous File / EXE from Rare External Location

Anomalous Connection / Unusual Internal Remote Desktop

User / Kerberos Password Brute Force

Compromise / Ransomware / Ransom or Offensive Words Written to SMB

Unusual Activity / Anomalous SMB Delete Volume

Anomalous Connection / Multiple Connections to New External TCP Port

Compromise / Slow Beaconing Activity To External Rare  

Compromise / SSL Beaconing to Rare Destination  

Anomalous Server Activity / Rare External from Server  

Compliance / Remote Management Tool On Server

Compromise / Agent Beacon (Long Period)  

Compromise / Suspicious File and C2  

Device / Internet Facing Device with High Priority Alert  

Device / Large Number of Model Breaches  

Anomalous File / Masqueraded File Transfer

Anomalous File / Internet facing System File Download  

Anomalous Server Activity / Outgoing from Server

Device / Initial Breach Chain Compromise  

Compromise / Agent Beacon (Medium Period)  

Compromise / Agent Beacon (Long Period)  

IoC一覧

IoC - Type - Description

zohoservice[.]net: 45.61.147[.]176 - Domain name: IP Address - Hosting payload over HTTP

Mozilla/5.0 (Windows NT; Windows NT 10.0; en-US) WindowsPowerShell/5.1.17763.2183 - User agent -PowerShell user agent

.cts1 - File extension - Malicious appendage

.cts7- File extension - Malicious appendage

cAcTuS.readme.txt - Filename -Ransom note

putty.zip – Filename - Initial payload: ZIP containing PuTTY Link

MITRE ATT&CK マッピング

Tactic - Technique  - SubTechnique

Web Protocols: COMMAND AND CONTROL - T1071 -T1071.001

Powershell: EXECUTION - T1059 - T1059.001

Exploitation of Remote Services: LATERAL MOVEMENT - T1210 – N/A

Vulnerability Scanning: RECONAISSANCE     - T1595 - T1595.002

Network Service Scanning: DISCOVERY - T1046 - N/A

Malware: RESOURCE DEVELOPMENT - T1588 - T1588.001

Drive-by Compromise: INITIAL ACCESS - T1189 - N/A

Remote Desktop Protocol: LATERAL MOVEMENT – 1021 -T1021.001

Brute Force: CREDENTIAL ACCESS        T – 1110 - N/A

Data Encrypted for Impact: IMPACT - T1486 - N/A

Data Destruction: IMPACT - T1485 - N/A

File Deletion: DEFENSE EVASION - T1070 - T1070.004

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著者について
Tiana Kelly
Deputy Team Lead, London & Cyber Analyst

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The State of AI in Cybersecurity: How AI will impact the cyber threat landscape in 2024

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22
Apr 2024

About the AI Cybersecurity Report

We surveyed 1,800 CISOs, security leaders, administrators, and practitioners from industries around the globe. Our research was conducted to understand how the adoption of new AI-powered offensive and defensive cybersecurity technologies are being managed by organizations.

This blog is continuing the conversation from our last blog post “The State of AI in Cybersecurity: Unveiling Global Insights from 1,800 Security Practitioners” which was an overview of the entire report. This blog will focus on one aspect of the overarching report, the impact of AI on the cyber threat landscape.

To access the full report click here.

Are organizations feeling the impact of AI-powered cyber threats?

Nearly three-quarters (74%) state AI-powered threats are now a significant issue. Almost nine in ten (89%) agree that AI-powered threats will remain a major challenge into the foreseeable future, not just for the next one to two years.

However, only a slight majority (56%) thought AI-powered threats were a separate issue from traditional/non AI-powered threats. This could be the case because there are few, if any, reliable methods to determine whether an attack is AI-powered.

Identifying exactly when and where AI is being applied may not ever be possible. However, it is possible for AI to affect every stage of the attack lifecycle. As such, defenders will likely need to focus on preparing for a world where threats are unique and are coming faster than ever before.

a hypothetical cyber attack augmented by AI at every stage

Are security stakeholders concerned about AI’s impact on cyber threats and risks?

The results from our survey showed that security practitioners are concerned that AI will impact organizations in a variety of ways. There was equal concern associated across the board – from volume and sophistication of malware to internal risks like leakage of proprietary information from employees using generative AI tools.

What this tells us is that defenders need to prepare for a greater volume of sophisticated attacks and balance this with a focus on cyber hygiene to manage internal risks.

One example of a growing internal risks is shadow AI. It takes little effort for employees to adopt publicly-available text-based generative AI systems to increase their productivity. This opens the door to “shadow AI”, which is the use of popular AI tools without organizational approval or oversight. Resulting security risks such as inadvertent exposure of sensitive information or intellectual property are an ever-growing concern.

Are organizations taking strides to reduce risks associated with adoption of AI in their application and computing environment?

71.2% of survey participants say their organization has taken steps specifically to reduce the risk of using AI within its application and computing environment.

16.3% of survey participants claim their organization has not taken these steps.

These findings are good news. Even as enterprises compete to get as much value from AI as they can, as quickly as possible, they’re tempering their eager embrace of new tools with sensible caution.

Still, responses varied across roles. Security analysts, operators, administrators, and incident responders are less likely to have said their organizations had taken AI risk mitigation steps than respondents in other roles. In fact, 79% of executives said steps had been taken, and only 54% of respondents in hands-on roles agreed. It seems that leaders believe their organizations are taking the needed steps, but practitioners are seeing a gap.

Do security professionals feel confident in their preparedness for the next generation of threats?

A majority of respondents (six out of every ten) believe their organizations are inadequately prepared to face the next generation of AI-powered threats.

The survey findings reveal contrasting perceptions of organizational preparedness for cybersecurity threats across different regions and job roles. Security administrators, due to their hands-on experience, express the highest level of skepticism, with 72% feeling their organizations are inadequately prepared. Notably, respondents in mid-sized organizations feel the least prepared, while those in the largest companies feel the most prepared.

Regionally, participants in Asia-Pacific are most likely to believe their organizations are unprepared, while those in Latin America feel the most prepared. This aligns with the observation that Asia-Pacific has been the most impacted region by cybersecurity threats in recent years, according to the IBM X-Force Threat Intelligence Index.

The optimism among Latin American respondents could be attributed to lower threat volumes experienced in the region, but it's cautioned that this could change suddenly (1).

What are biggest barriers to defending against AI-powered threats?

The top-ranked inhibitors center on knowledge and personnel. However, issues are alluded to almost equally across the board including concerns around budget, tool integration, lack of attention to AI-powered threats, and poor cyber hygiene.

The cybersecurity industry is facing a significant shortage of skilled professionals, with a global deficit of approximately 4 million experts (2). As organizations struggle to manage their security tools and alerts, the challenge intensifies with the increasing adoption of AI by attackers. This shift has altered the demands on security teams, requiring practitioners to possess broad and deep knowledge across rapidly evolving solution stacks.

Educating end users about AI-driven defenses becomes paramount as organizations grapple with the shortage of professionals proficient in managing AI-powered security tools. Operationalizing machine learning models for effectiveness and accuracy emerges as a crucial skill set in high demand. However, our survey highlights a concerning lack of understanding among cybersecurity professionals regarding AI-driven threats and the use of AI-driven countermeasures indicating a gap in keeping pace with evolving attacker tactics.

The integration of security solutions remains a notable problem, hindering effective defense strategies. While budget constraints are not a primary inhibitor, organizations must prioritize addressing these challenges to bolster their cybersecurity posture. It's imperative for stakeholders to recognize the importance of investing in skilled professionals and integrated security solutions to mitigate emerging threats effectively.

To access the full report click here.

参考文献

1. IBM, X-Force Threat Intelligence Index 2024, Available at: https://www.ibm.com/downloads/cas/L0GKXDWJ

2. ISC2, Cybersecurity Workforce Study 2023, Available at: https://media.isc2.org/-/media/Project/ISC2/Main/Media/ documents/research/ISC2_Cybersecurity_Workforce_Study_2023.pdf?rev=28b46de71ce24e6ab7705f6e3da8637e

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