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Ameaças internas, cadeias de suprimentos e IoT: Quebrando um cyber-ataque dos tempos modernos

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03
Maio 2021
03
Maio 2021
A paisagem de ameaça não é o que era. A expansão dos ecossistemas e cadeias de abastecimento globalizadas oferecem muitas oportunidades para os atores da ameaça. Darktrace detecta esses vetores diariamente, às vezes no mesmo ataque.

São dez a cinco em uma sexta-feira à tarde. Um técnico entrou para realizar uma verificação de rotina em uma porta eletrônica. Ela entra no escritório sem problemas - ela trabalha para um fornecedor terceirizado de confiança, os funcionários a vêem todas as semanas. Ela abre seu laptop e se conecta à Unidade de Controle de Acesso às Portas, um pequeno dispositivo de Internet das Coisas (IoT) usado para operar a fechadura inteligente. Minutos depois, trojans foram baixados na rede da empresa, uma operação de criptografia de minas foi iniciada e há evidência de dados confidenciais sendo exfiltrados. Onde as coisas deram errado?

Ameaças em um negócio: um novo amanhecer

Como as organizações acompanham as exigências da transformação digital, a superfície de ataque se tornou mais ampla do que nunca. Há inúmeros pontos de entrada para um cyber-criminoso - desde vulnerabilidades nos ecossistemas da Internet sem fio, a pontos cegos nas cadeias de abastecimento, até o uso indevido do acesso ao negócio por parte de pessoas de dentro. Darktrace vê estas ameaças todos os dias. Às vezes, como no exemplo do mundo real acima, que será examinado neste blog, elas podem ocorrer exatamente no mesmo ataque.

As ameaças internas podem usar sua familiaridade e nível de acesso a um sistema como uma vantagem crítica ao evitar a detecção e lançar um ataque. Mas os infiltrados não têm necessariamente que ser maliciosos. Cada funcionário ou contratante é uma ameaça potencial: clicar em um link de phishing ou liberar dados acidentalmente muitas vezes leva a violações em larga escala.

Ao mesmo tempo, a conectividade no espaço de trabalho - com cada dispositivo IoT comunicando-se com a rede corporativa e a Internet em seu próprio endereço IP - é uma questão urgente de segurança. Sistemas de controle de acesso, por exemplo, adicionam uma camada de segurança física rastreando quem entra no escritório e quando. Entretanto, estes mesmos sistemas de controle põem em risco a segurança digital ao introduzir um cluster de sensores, fechaduras, sistemas de alarme e teclados, que retêm informações sensíveis do usuário e se conectam à infra-estrutura da empresa.

Além disso, uma proporção significativa dos dispositivos de IOT é construída sem ter em mente a segurança. Os fornecedores priorizam o time-to-market e muitas vezes não têm os recursos para investir em medidas de segurança cozidas. Considere o número de empresas iniciantes que fabricam o IoT - mais de 60% das empresas de automação doméstica têm menos de dez funcionários.

Ameaça interna detectada pela Cyber AI

Em janeiro de 2021, uma empresa norte-americana de médio porte sofreu um ataque na cadeia de abastecimento quando um fornecedor terceirizado se conectou à unidade de controle para uma porta inteligente.

Figura 1: O ataque durou 3,5 horas no total, a partir das 16:50 horas locais.

O técnico da empresa do fornecedor tinha vindo para realizar a manutenção programada. Eles tinham sido autorizados a se conectar diretamente à Unidade de Controle de Acesso às Portas, mas não sabiam que o laptop que estavam usando, trazido de fora da organização, tinha sido infectado por malware.

Assim que o laptop conectou-se à unidade de controle, o malware detectou uma porta aberta, identificou a vulnerabilidade e começou a se mover lateralmente. Em poucos minutos, o dispositivo IoT foi visto fazendo conexões altamente incomuns com endereços IP externos raros. As conexões foram feitas usando HTTP e continham agentes de usuários suspeitos e URIs.

Darktrace então detectou que a unidade de controle estava tentando baixar trojans e outras cargas úteis, incluindo upsupx2.exe e 36BBB9658.moe. Outras conexões foram usadas para enviar cordas codificadas base64 contendo o nome do dispositivo e o endereço IP externo da organização.

A atividade de mineração de moedas criptográficas com um minerador de CPU Monero (XMR) foi detectada logo em seguida. O dispositivo também utilizou uma exploração SMB para fazer conexões externas na porta 445 enquanto procurava por dispositivos internos vulneráveis usando o protocolo SMBv1 desatualizado.

Uma hora depois, o dispositivo conectado a um ponto final relacionado à ferramenta de acesso remoto de terceiros TeamViewer. Após alguns minutos, o dispositivo foi visto carregando mais de 15 MB para um IP externo 100% raro.

Figura 2: Linha do tempo das conexões feitas por um dispositivo de exemplo nos dias em torno de um incidente (azul). As conexões associadas ao compromisso são um desvio significativo do padrão de vida normal do dispositivo, e resultam em múltiplos eventos de atividade incomuns e violações repetidas do modelo (laranja).

Ameaças à segurança na cadeia de fornecimento

A Cyber AI sinalizou a ameaça interna ao cliente assim que a unidade de controle foi comprometida. O ataque tinha conseguido contornar o resto da pilha de segurança da organização, pela simples razão de que foi introduzido diretamente de um laptop externo confiável, e o próprio dispositivo IoT foi gerenciado pelo fornecedor terceirizado, de modo que o cliente tinha pouca visibilidade sobre ele.

As ferramentas tradicionais de segurança são ineficazes contra ataques da cadeia de abastecimento como este. Do hack do SolarWinds ao Vendor Email Compromise, 2021 colocou o prego no caixão para segurança baseada em assinatura - provando que não podemos contar com os ataques de ontem para prever as ameaças de amanhã.

As cadeias de fornecimento internacionais e o grande número de diferentes parceiros e fornecedores com os quais as organizações modernas trabalham representam assim um sério dilema de segurança: como podemos permitir a entrada de fornecedores externos em nossa rede e manter um sistema hermético?

A primeira resposta é o zero-trust. Isto envolve tratar cada dispositivo como malicioso, dentro e fora da rede corporativa, e exigir verificação em todas as etapas. A segunda resposta é visibilidade e resposta. Os produtos de segurança devem lançar uma luz clara na infraestrutura da nuvem e da Internet sem fio e reagir de forma autônoma assim que surgirem anomalias sutis em toda a empresa.

IoT investigado

DarktraceO Cyber AI Analyst informou sobre cada etapa do ataque, incluindo o download do primeiro arquivo executável malicioso.

Figura 3: Exemplo de Cyber AI Analyst detectando comportamento anômalo em um dispositivo, incluindo conectividade C2 e downloads suspeitos de arquivos.

O Cyber AI Analyst investigou a conectividade C2, fornecendo um resumo de alto nível da atividade. O dispositivo IoT tinha acessado arquivos MOE suspeitos com nomes alfanuméricos gerados aleatoriamente.

Figura 4: Um resumo do Cyber AI Analyst da conectividade C2 para um dispositivo.

A IA não apenas detectou cada etapa da atividade, mas o cliente também foi alertado através de uma Notificação de Ameaça Proativa após uma quebra do modelo de pontuação alta às 16:59, poucos minutos após o ataque ter começado.

Perigo estranho

Terceiros que entram para ajustar as configurações do dispositivo e ajustar a rede podem ter conseqüências não intencionais. O mundo hiper conectado em que vivemos, com o advento da 5G e da Indústria 4.0, tornou-se um campo de jogos digital para ciber-criminosos.

No estudo de caso do mundo real acima, o dispositivo IoT não estava seguro e estava mal configurado. Com criações apressadas de ecossistemas de IOT, cadeias de fornecimento entrelaçadas e uma gama de indivíduos e dispositivos conectados à infra-estrutura corporativa, as organizações modernas não podem esperar simples ferramentas de segurança que dependem de regras pré-definidas para deter ameaças internas e outros ataques cibernéticos avançados.

A organização não tinha visibilidade sobre a gestão da Unidade de Controle de Acesso às Portas. Apesar disso, e apesar de não ter conhecimento prévio do tipo de ataque ou das vulnerabilidades presentes no dispositivo IoT, Darktrace detectou imediatamente as anomalias comportamentais. Sem a Cyber AI, a infecção poderia ter permanecido no ambiente do cliente por semanas ou meses, aumentando os privilégios, minerando silenciosamente em criptografia e exfiltrando dados sensíveis da empresa.

Agradecimentos à analista Grace Carballo de Darktrace por suas idéias sobre a descoberta da ameaça acima.

Saiba mais sobre as ameaças internas

Darktrace detecções de modelos:

  • Arquivo anômalo/Crente de octetos anômalos
  • Conexão anômala /Novo agente de usuário para IP sem nome de host
  • Atividade incomum/Conectividade externa incomum
  • Conectividade externa do dispositivo/Incredible
  • Atividade do servidor anômalo/Outgoing from server
  • Dispositivo/Novo agente de usuário e novo IP
  • Atividade de mineração de conformidade/cryptocurrency
  • Conformidade/Conectividade com Windows externo
  • Arquivo anômalo/ EXE múltiplo de locais externos raros
  • Arquivo anomalous/EXE de localização externa rara
  • Número grande de quebras de modelo de dispositivo
  • Arquivo Anomalous / Sistema de faceamento da internet download do arquivo
  • Compromisso de Dispositivo/Cadeia Inicial de breach
  • Dispositivo/SMB sessão bruteforce
  • Escaneamento de dispositivos/Rede - Baixa pontuação de anomalias
  • Dispositivo/Grande número de conexões para o novo ponto final
  • Atividade do servidor anômalo/Outgoing from server
  • Compromisso/Beacon para young endpoint
  • Atividade do servidor anomalous/Raros externos do servidor
  • Quebra do modelo C2 do dispositivo/Múltiplo
  • Ferramenta de gerenciamento de conformidade/Remota no servidor
  • Conexão anômala/Dados enviados para novo dispositivo externo


DENTRO DO SOC
Os analistas cibernéticos da Darktrace são especialistas de classe mundial em inteligência de ameaças, caça de ameaças e resposta a incidentes, e fornecem suporte 24/7 SOC a milhares de Darktrace clientes em todo o mundo. Dentro do SOC é de autoria exclusiva desses especialistas, fornecendo análises de incidentes cibernéticos e tendências de ameaças, com base na experiência do mundo real na área.
AUTOR
SOBRE O AUTOR
Brianna Leddy
Director of Analysis

Com sede em São Francisco, Brianna é Diretora de Análise em Darktrace. Ela se juntou à equipe de analistas em 2016 e desde então tem aconselhado uma ampla gama de clientes empresariais sobre caça avançada de ameaças e alavancagem da IA de auto-aprendizagem para detecção e resposta. Brianna trabalha de perto com a equipe do SOC Darktrace para alertar proativamente os clientes sobre ameaças emergentes e investigar comportamentos incomuns em ambientes empresariais. Brianna é graduada em Engenharia Química pela Carnegie Mellon University.

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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”.

Conclusão

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

Appendices

References

[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

Dispositivo / Atividade de escaneamento de SMBs suspeitas

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)  

Compromisso / Arquivo Suspeito e C2  

Dispositivo / Dispositivo de Faceamento de Internet com Alerta de Alta Prioridade  

Dispositivo / Grande número de quebras de modelo  

Anomalous File / Masqueraded File Transfer

Anomalous File / Internet facing System File Download  

Atividade Anomalosa do Servidor / Saída do Servidor

Device / Initial Breach Chain Compromise  

Compromise / Agent Beacon (Medium Period)  

Compromise / Agent Beacon (Long Period)  

List of IoCs

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 Mapping

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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About the author
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.

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References

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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