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高度なEメール脅威に対するマクラーレン・レーシングの対策

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05
2021年1月
05
2021年1月
Faced with sophisticated phishing attacks targeting their C-suite, McLaren turned to AI to stop advanced email threats that outsmarted their legacy security tools. This blog uncovers an attack that slipped through their gateway but was neutralized by Darktrace/Email.

チームのEメール受信箱をセキュアにすることはマクラーレン・レーシングにおいて長い間大きな課題でした。COVID-19以前から、私達のワークフォースは非常に分散していました。過去30年間、世界中のレーシングコースにおいて、1週間おきにリモートオフィスをセットアップしていたも同然です。そのため、人々がきわめて高い割合でEメールを使い、また必ずしも同じ場所にいない状態に慣れていました。

パートナーや主要なサプライヤーも含め、コラボレーションがチームにとっての鍵です。データの共有は毎日行われ、さまざまな方法が使われています。これらのデータには扱いに注意が必要な車の設計データや非公開のコースデータも含まれています。

チームを標的としたEメール攻撃はこの1〜2年でかなり高度化し、攻撃者は偽の支払い要求をしたり、知的財産にアクセスしようとしたりしていました。ソーシャルエンジニアリング攻撃がますます巧妙化するということは、こうした事態を防ぐためにさまざまなツールや手順を配備していても、依然として社内のユーザーはこれらのフィッシングメールやなりすましメールに引っかかるということを意味します。

昨年、当社ではDarktraceのImmune SystemをEメールシステムにも適用拡大し、現在はインテリジェントなAIセキュリティソリューションによりあらゆるMicrosoft 365ユーザーの「生活パターン」を理解させ、攻撃を特定できるようになりました。Darktraceにより、セキュリティチームは従来型防御をすり抜けた攻撃に対して遡及的に対応するのではなく、最も高度なEメール脅威にも先行して対処できるようになりました。

攻撃を見つけるための従業員トレーニング

以前は脅威インテリジェンスフィードや悪意あるアドレス、ドメインおよびURLをブロックする従来型のセキュリティツールを使用していましたが、少数のフィッシングメールはどうしてもユーザーの受信箱まで到達することが避けられませんでした。通常、これらのEメールは周到に準備し高度にコンテキスト化されたもので、受信者に合わせて作成され、場合によっては本物のメールと区別がつかないようなものでした。従業員に対して認識向上プログラムを実施しても、これらの悪意あるメールに対して一定の割合でユーザーが反応してしまい、アカウント乗っ取りや詐欺行為が発生していました。当時はセキュリティリソースがこれらのインシデントへの対応で手いっぱいとなり、マクラーレン・レーシングのセキュリティを積極的に強化する取り組みができていませんでした。

そこで多くのパートナー企業と協力して「サイバー攻撃認識向上週間」を実施し、自分達で作成した偽のフィッシング攻撃を行うことで従業員に対して攻撃をどうやって発見するかを教育しました。しかしこれらの教育プログラムは、リモートワークの割合が増えるにつれ、伝えることが難しくなっていきました。従業員の積極的な関与が常に重要であったため、セキュリティチームへのリソース負荷は大きくなりました。関係部署の責任者と協力してスプーフィングメールの特定を助けたり、ビジネスプロセスを構築するのに非常に長い時間をとられていたのです。

これは長く根気のいるプロセスであり、また、ますます巧妙化するEメール攻撃のかすかな兆候の発見を従業員に期待することは困難です。現代のEメール攻撃の高度化、それに費やされる研究、関連するソーシャルエンジニアリングのレベル、これらのことからフィッシング攻撃が人間と基本的な防御システムのどちらも突破してしまうことは避けられませんでした。

Cyber AIの活用

パートナーであるDarktraceと協力し、私達は彼らのEメールセキュリティテクノロジーであるDarktrace/Email を導入し、一緒にインストールと設定を行いました。そしてその効果は数日で確認できました。自動対処により、ユーザーから報告されるフィッシングメールの数は次第にそして大幅に減り、Darktrace Emailのアクションを定期的にレビューすることによってそれまで気づいていなかった数多くのフィッシング攻撃を発見するに至りました。

Darktraceのアクションはビジネスのコンテキストを考慮して実行され、最後の手段としてのみメールの保留を行い(当社の環境では1%未満)、膨大な偽陽性を発生させるのではなく本当に悪意あるメールのみを捕捉しました。また、これらのアクションは的を絞り程度に見合ったもので、メールをゴミ箱に移動する、あるいは添付ファイルを変換したりリンクをロックするなどのさまざまな種類があり、私達が必要とする柔軟性がありました。

Darktrace Email が継続的に学習し高度なEメール攻撃を阻止してくれたため、セキュリティチームは圧力から解放され、ビジネスの新しい取り組みをサポートしたり、新たなイノベーションへの協力などに時間を使うことができるようにありました。

Cレベルの役員を狙った標的型の認証情報奪取攻撃を阻止

多くの組織同様、最も悪意あるメールの標的となるのは執行レベルの役員が多く、最近も当社の役員に対して送金のための書類に署名させようとするEメールをAntigena Email が検知しました。メールはDocuSignから送信されたように見え、‘Review Document’ というテキストの背後に悪意あるリンクが隠されていました。

図1:Eメールを特定したDarktrace/EmailのUI画面
図2:問題のEメールのスクリーンショット

この種のEメール攻撃では、リンクをクリックすると通常2つのシナリオが続きます。ユーザーが偽の(多くの場合、非常にもっともらしい)ログインページに誘導されて認証情報を入力させられる、あるいは文書自体は本物に見える請求書であるが、一つの重要な要素、すなわち口座情報が変更されている、というものです。会計チームやCFOは日常的にこうした攻撃にさらされていますが、このケースでは、攻撃者達は役員の認証情報を狙っていました。

この役員がクリックしてログインを試みていれば、それとは知らずに攻撃者に対して認証情報を送ってしまい、攻撃者はこの情報を使ってEメール受信箱あるいは他のSaaSアカウントから機微な情報を収集する、あるいはこのアカウントから悪意あるメールを送信して組織内にさらに侵入していたかもしれません。

Eメールはイモラサーキットでグランプリレースが行われていた週末に送られました。これはチーム全体が高いプレッシャーに晒されていた48時間でした。というのも金曜日の練習走行を行わずに新しいフォーマットでの走行を行ったため、緊張が一段と高まっていたのです。しかし、Darktrace Emailが警備を行っていたため、この送信者が新しい連絡先であることを認識しリンクを疑わしいと判断しました。Eメールに対して適切な問題認識を持ったDarktraceのAIはリンクをダブルロックし、このメールを役員のゴミ箱フォルダに移動しました。これらのことはすべて週末にオンコールで待機していたサイバーセキュリティチームを煩わせることなく行われました。

このような攻撃が日々やってくるなかで、本物と偽物の区別をマクラーレンの従業員に頼っていたのでは、あらゆる脅威から我々を現実的に保護することは不可能です。認証情報の奪取やアカウント乗っ取りが増える中で、たった1通のフィッシングメールの成功により洪水ゲートが開いてしまうのは時間の問題だと感じていました。しかしDarktrace Email導入により、コース上でもコース外でも強力なAIソリューションにより守られているという安心感を得ることができました。

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
Ed Green
Head of Commercial Technology, McLaren Racing

Ed Green works in the Architecture practice within the Information Technology function at McLaren Technology Group, as well as being responsible for the successful integration of their Technology Partners into the McLaren ecosystem. Ed joined McLaren in March 2018 after spending 5 years working for Block Solutions, a specialist network consultancy. In previous roles, he led the Consultancy division at a UK Solution Integrator operating across the public, enterprise, and commercial sectors. Ed has driven innovative engagements with organisations such as Harrods, intu, The Francis Crick Institute, and Barts Health NHS Trust. He has also spent seven years on the council at Great Ormond Street Hospital representing the views of patients at a Board level, and he continues his work at the Hospital School as a Governor and supports the school with STEM initiatives.

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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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Sliver C2: How Darktrace Provided a Sliver of Hope in the Face of an Emerging C2 Framework

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17
Apr 2024

Offensive Security Tools

As organizations globally seek to for ways to bolster their digital defenses and safeguard their networks against ever-changing cyber threats, security teams are increasingly adopting offensive security tools to simulate cyber-attacks and assess the security posture of their networks. These legitimate tools, however, can sometimes be exploited by real threat actors and used as genuine actor vectors.

What is Sliver C2?

Sliver C2 is a legitimate open-source command-and-control (C2) framework that was released in 2020 by the security organization Bishop Fox. Silver C2 was originally intended for security teams and penetration testers to perform security tests on their digital environments [1] [2] [5]. In recent years, however, the Sliver C2 framework has become a popular alternative to Cobalt Strike and Metasploit for many attackers and Advanced Persistence Threat (APT) groups who adopt this C2 framework for unsolicited and ill-intentioned activities.

The use of Sliver C2 has been observed in conjunction with various strains of Rust-based malware, such as KrustyLoader, to provide backdoors enabling lines of communication between attackers and their malicious C2 severs [6]. It is unsurprising, then, that it has also been leveraged to exploit zero-day vulnerabilities, including critical vulnerabilities in the Ivanti Connect Secure and Policy Secure services.

In early 2024, Darktrace observed the malicious use of Sliver C2 during an investigation into post-exploitation activity on customer networks affected by the Ivanti vulnerabilities. Fortunately for affected customers, Darktrace DETECT™ was able to recognize the suspicious network-based connectivity that emerged alongside Sliver C2 usage and promptly brought it to the attention of customer security teams for remediation.

How does Silver C2 work?

Given its open-source nature, the Sliver C2 framework is extremely easy to access and download and is designed to support multiple operating systems (OS), including MacOS, Windows, and Linux [4].

Sliver C2 generates implants (aptly referred to as ‘slivers’) that operate on a client-server architecture [1]. An implant contains malicious code used to remotely control a targeted device [5]. Once a ‘sliver’ is deployed on a compromised device, a line of communication is established between the target device and the central C2 server. These connections can then be managed over Mutual TLS (mTLS), WireGuard, HTTP(S), or DNS [1] [4]. Sliver C2 has a wide-range of features, which include dynamic code generation, compile-time obfuscation, multiplayer-mode, staged and stageless payloads, procedurally generated C2 over HTTP(S) and DNS canary blue team detection [4].

Why Do Attackers Use Sliver C2?

Amidst the multitude of reasons why malicious actors opt for Sliver C2 over its counterparts, one stands out: its relative obscurity. This lack of widespread recognition means that security teams may overlook the threat, failing to actively search for it within their networks [3] [5].

Although the presence of Sliver C2 activity could be representative of authorized and expected penetration testing behavior, it could also be indicative of a threat actor attempting to communicate with its malicious infrastructure, so it is crucial for organizations and their security teams to identify such activity at the earliest possible stage.

Darktrace’s Coverage of Sliver C2 Activity

Darktrace’s anomaly-based approach to threat detection means that it does not explicitly attempt to attribute or distinguish between specific C2 infrastructures. Despite this, Darktrace was able to connect Sliver C2 usage to phases of an ongoing attack chain related to the exploitation of zero-day vulnerabilities in Ivanti Connect Secure VPN appliances in January 2024.

Around the time that the zero-day Ivanti vulnerabilities were disclosed, Darktrace detected an internal server on one customer network deviating from its expected pattern of activity. The device was observed making regular connections to endpoints associated with Pulse Secure Cloud Licensing, indicating it was an Ivanti server. It was observed connecting to a string of anomalous hostnames, including ‘cmjk3d071amc01fu9e10ae5rt9jaatj6b.oast[.]live’ and ‘cmjft14b13vpn5vf9i90xdu6akt5k3pnx.oast[.]pro’, via HTTP using the user agent ‘curl/7.19.7 (i686-redhat-linux-gnu) libcurl/7.63.0 OpenSSL/1.0.2n zlib/1.2.7’.

Darktrace further identified that the URI requested during these connections was ‘/’ and the top-level domains (TLDs) of the endpoints in question were known Out-of-band Application Security Testing (OAST) server provider domains, namely ‘oast[.]live’ and ‘oast[.]pro’. OAST is a testing method that is used to verify the security posture of an application by testing it for vulnerabilities from outside of the network [7]. This activity triggered the DETECT model ‘Compromise / Possible Tunnelling to Bin Services’, which breaches when a device is observed sending DNS requests for, or connecting to, ‘request bin’ services. Malicious actors often abuse such services to tunnel data via DNS or HTTP requests. In this specific incident, only two connections were observed, and the total volume of data transferred was relatively low (2,302 bytes transferred externally). It is likely that the connections to OAST servers represented malicious actors testing whether target devices were vulnerable to the Ivanti exploits.

The device proceeded to make several SSL connections to the IP address 103.13.28[.]40, using the destination port 53, which is typically reserved for DNS requests. Darktrace recognized that this activity was unusual as the offending device had never previously been observed using port 53 for SSL connections.

Model Breach Event Log displaying the ‘Application Protocol on Uncommon Port’ DETECT model breaching in response to the unusual use of port 53.
Figure 1: Model Breach Event Log displaying the ‘Application Protocol on Uncommon Port’ DETECT model breaching in response to the unusual use of port 53.

Figure 2: Model Breach Event Log displaying details pertaining to the ‘Application Protocol on Uncommon Port’ DETECT model breach, including the 100% rarity of the port usage.
Figure 2: Model Breach Event Log displaying details pertaining to the ‘Application Protocol on Uncommon Port’ DETECT model breach, including the 100% rarity of the port usage.

Further investigation into the suspicious IP address revealed that it had been flagged as malicious by multiple open-source intelligence (OSINT) vendors [8]. In addition, OSINT sources also identified that the JARM fingerprint of the service running on this IP and port (00000000000000000043d43d00043de2a97eabb398317329f027c66e4c1b01) was linked to the Sliver C2 framework and the mTLS protocol it is known to use [4] [5].

An Additional Example of Darktrace’s Detection of Sliver C2

However, it was not just during the January 2024 exploitation of Ivanti services that Darktrace observed cases of Sliver C2 usages across its customer base.  In March 2023, for example, Darktrace detected devices on multiple customer accounts making beaconing connections to malicious endpoints linked to Sliver C2 infrastructure, including 18.234.7[.]23 [10] [11] [12] [13].

Darktrace identified that the observed connections to this endpoint contained the unusual URI ‘/NIS-[REDACTED]’ which contained 125 characters, including numbers, lower and upper case letters, and special characters like “_”, “/”, and “-“, as well as various other URIs which suggested attempted data exfiltration:

‘/upload/api.html?c=[REDACTED] &fp=[REDACTED]’

  • ‘/samples.html?mx=[REDACTED] &s=[REDACTED]’
  • ‘/actions/samples.html?l=[REDACTED] &tc=[REDACTED]’
  • ‘/api.html?gf=[REDACTED] &x=[REDACTED]’
  • ‘/samples.html?c=[REDACTED] &zo=[REDACTED]’

This anomalous external connectivity was carried out through multiple destination ports, including the key ports 443 and 8888.

Darktrace additionally observed devices on affected customer networks performing TLS beaconing to the IP address 44.202.135[.]229 with the JA3 hash 19e29534fd49dd27d09234e639c4057e. According to OSINT sources, this JA3 hash is associated with the Golang TLS cipher suites in which the Sliver framework is developed [14].

結論

Despite its relative novelty in the threat landscape and its lesser-known status compared to other C2 frameworks, Darktrace has demonstrated its ability effectively detect malicious use of Sliver C2 across numerous customer environments. This included instances where attackers exploited vulnerabilities in the Ivanti Connect Secure and Policy Secure services.

While human security teams may lack awareness of this framework, and traditional rules and signatured-based security tools might not be fully equipped and updated to detect Sliver C2 activity, Darktrace’s Self Learning AI understands its customer networks, users, and devices. As such, Darktrace is adept at identifying subtle deviations in device behavior that could indicate network compromise, including connections to new or unusual external locations, regardless of whether attackers use established or novel C2 frameworks, providing organizations with a sliver of hope in an ever-evolving threat landscape.

Credit to Natalia Sánchez Rocafort, Cyber Security Analyst, Paul Jennings, Principal Analyst Consultant

付録

DETECT Model Coverage

  • Compromise / Repeating Connections Over 4 Days
  • Anomalous Connection / Application Protocol on Uncommon Port
  • Anomalous Server Activity / Server Activity on New Non-Standard Port
  • Compromise / Sustained TCP Beaconing Activity To Rare Endpoint
  • Compromise / Quick and Regular Windows HTTP Beaconing
  • Compromise / High Volume of Connections with Beacon Score
  • Anomalous Connection / Multiple Failed Connections to Rare Endpoint
  • Compromise / Slow Beaconing Activity To External Rare
  • Compromise / HTTP Beaconing to Rare Destination
  • Compromise / Sustained SSL or HTTP Increase
  • Compromise / Large Number of Suspicious Failed Connections
  • Compromise / SSL or HTTP Beacon
  • Compromise / Possible Malware HTTP Comms
  • Compromise / Possible Tunnelling to Bin Services
  • Anomalous Connection / Low and Slow Exfiltration to IP
  • Device / New User Agent
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Anomalous File / EXE from Rare External Location
  • Anomalous File / Numeric File Download
  • Anomalous Connection / Powershell to Rare External
  • Anomalous Server Activity / New Internet Facing System

侵害指標(IoC)一覧

18.234.7[.]23 - Destination IP - Likely C2 Server

103.13.28[.]40 - Destination IP - Likely C2 Server

44.202.135[.]229 - Destination IP - Likely C2 Server

参考文献

[1] https://bishopfox.com/tools/sliver

[2] https://vk9-sec.com/how-to-set-up-use-c2-sliver/

[3] https://www.scmagazine.com/brief/sliver-c2-framework-gaining-traction-among-threat-actors

[4] https://github[.]com/BishopFox/sliver

[5] https://www.cybereason.com/blog/sliver-c2-leveraged-by-many-threat-actors

[6] https://securityaffairs.com/158393/malware/ivanti-connect-secure-vpn-deliver-krustyloader.html

[7] https://www.xenonstack.com/insights/out-of-band-application-security-testing

[8] https://www.virustotal.com/gui/ip-address/103.13.28.40/detection

[9] https://threatfox.abuse.ch/browse.php?search=ioc%3A107.174.78.227

[10] https://threatfox.abuse.ch/ioc/1074576/

[11] https://threatfox.abuse.ch/ioc/1093887/

[12] https://threatfox.abuse.ch/ioc/846889/

[13] https://threatfox.abuse.ch/ioc/1093889/

[14] https://github.com/projectdiscovery/nuclei/issues/3330

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Natalia Sánchez Rocafort
Cyber Security Analyst
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