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March 22, 2023

Amadey Info Stealer and N-Day Vulnerabilities

Understand the implications of the Amadey info stealer on cybersecurity and how it exploits N-day vulnerabilities for data theft.
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Zoe Tilsiter
Cyber Analyst
Written by
The Darktrace Threat Research Team
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22
Mar 2023

The continued prevalence of Malware as a Service (MaaS) across the cyber threat landscape means that even the most inexperienced of would-be malicious actors are able to carry out damaging and wide-spread cyber-attacks with relative ease. Among these commonly employed MaaS are information stealers, or info-stealers, a type of malware that infects a device and attempts to gather sensitive information before exfiltrating it to the attacker. Info-stealers typically target confidential information, such as login credentials and bank details, and attempt to lie low on a compromised device, allowing access to sensitive data for longer periods of time. 

It is essential for organizations to have efficient security measures in place to defend their networks from attackers in an increasing versatile and accessible threat landscape, however incident response alone is not enough. Having an autonomous decision maker able to not only detect suspicious activity, but also take action against it in real time, is of the upmost importance to defend against significant network compromise. 

Between August and December 2022, Darktrace detected the Amadey info-stealer on more than 30 customer environments, spanning various regions and industry verticals across the customer base. This shows a continual presence and overlap of info-stealer indicators of compromise (IOCs) across the cyber threat landscape, such as RacoonStealer, which we discussed last November (Part 1 and Part 2).

Background on Amadey

Amadey Bot, a malware that was first discovered in 2018, is capable of stealing sensitive information and installing additional malware by receiving commands from the attacker. Like other malware strains, it is being sold in illegal forums as MaaS starting from $500 USD [1]. 

Researchers at AhnLab found that Amadey is typically distributed via existing SmokeLoader loader malware campaigns. Downloading cracked versions of legitimate software causes SmokeLoader to inject malicious payload into Windows Explorer processes and proceeds to download Amadey.  

The botnet has also been used for distributed denial of service (DDoS) attacks, and as a vector to install malware spam campaigns, such as LockBit 3.0 [2]. Regardless of the delivery techniques, similar patterns of activity were observed across multiple customer environments. 

Amadey’s primary function is to steal information and further distribute malware. It aims to extract a variety of information from infected devices and attempts to evade the detection of security measures by reducing the volume of data exfiltration compared to that seen in other malicious instances.

Darktrace DETECT/Network™ and its built-in features, such as Wireshark Packet Captures (PCAP), identified Amadey activity on customer networks, whilst Darktrace RESPOND/Network™ autonomously intervened to halt its progress.

Attack Details

Figure 1: Timeline of Amadey info-stealer kill chain.

Initial Access  

User engagement with malicious email attachments or cracked software results in direct execution of the SmokeLoader loader malware on a device. Once the loader has executed its payload, it is then able to download additional malware, including the Amadey info-stealer.

Unusual Outbound Connections 

After initial access by the loader and download of additional malware, the Amadey info-stealer captures screenshots of network information and sends them to Amadey command and control (C2) servers via HTTP POST requests with no GET to a .php URI. An example of this can be seen in Figure 2.  

Figure 2: PCAP from an affected customer showing screenshots being sent out to the Amadey C2 server via a .jpg file. 

C2 Communications  

The infected device continues to make repeated connections out to this Amadey endpoint. Amadey's C2 server will respond with instructions to download additional plugins in the form of dynamic-link libraries (DLLs), such as "/Mb1sDv3/Plugins/cred64.dll", or attempt to download secondary info-stealers such as RedLine or RaccoonStealer. 

Internal Reconnaissance 

The device downloads executable and DLL files, or stealer configuration files to steal additional network information from software including RealVNC and Outlook. Most compromised accounts were observed downloading additional malware following commands received from the attacker.

Data Exfiltration 

The stolen information is then sent out via high volumes of HTTP connection. It makes HTTP POSTs to malicious .php URIs again, this time exfiltrating more data such as the Amadey version, device names, and any anti-malware software installed on the system.

How did the attackers bypass the rest of the security stack?

Existing N-Day vulnerabilities are leveraged to launch new attacks on customer networks and potentially bypass other tools in the security stack. Additionally, exfiltrating data via low and slow HTTP connections, rather than large file transfers to cloud storage platforms, is an effective means of evading the detection of traditional security tools which often look for large data transfers, sometimes to a specific list of identified “bad” endpoints.

Darktrace Coverage 

Amadey activity was autonomously identified by DETECT and the Cyber AI Analyst. A list of DETECT models that were triggered on deployments during this kill chain can be found in the Appendices. 

Various Amadey activities were detected and highlighted in DETECT model breaches and their model breach event logs. Figure 3 shows a compromised device making suspicious HTTP POST requests, causing the ‘Anomalous Connection / Posting HTTP to IP Without Hostname’ model to breach. It also downloaded an executable file (.exe) from the same IP.

Figure 3: Amadey activity on a customer deployment captured by model breaches and event logs. 

DETECT’s built-in features also assisted with detecting the data exfiltration. Using the PCAP integration, the exfiltrated data was captured for analysis. Figure 4 shows a connection made to the Amadey endpoint, in which information about the infected device, such as system ID and computer name, were sent. 

Figure 4: PCAP downloaded from Darktrace event logs highlighting data egress to the Amadey endpoint. 

Further information about the infected system can be seen in the above PCAP. As outlined by researchers at Ahnlab and shown in Figure 5, additional system information sent includes the Amadey version (vs=), the device’s admin privilege status (ar=), and any installed anti-malware or anti-virus software installed on the infected environment (av=) [3]. 

Figure 5: AhnLab’s glossary table explaining the information sent to the Amadey C2 server. 

Darktrace’s AI Analyst was also able to connect commonalities between model breaches on a device and present them as a connected incident made up of separate events. Figure 6 shows the AI Analyst incident log for a device having breached multiple models indicative of the Amadey kill chain. It displays the timeline of these events, the specific IOCs, and the associated attack tactic, in this case ‘Command and Control’. 

Figure 6: A screenshot of multiple IOCs and activity correlated together by AI Analyst. 

When enabled on customer’s deployments, RESPOND was able to take immediate action against Amadey to mitigate its impact on customer networks. RESPOND models that breached include: 

  • Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block
  • Antigena / Network / External Threat / Antigena Suspicious File Block 
  • Antigena / Network / Significant Anomaly / Antigena Controlled and Model Breach

On one customer’s environment, a device made a POST request with no GET to URI ‘/p84Nls2/index.php’ and unepeureyore[.]xyz. RESPOND autonomously enforced a previously established pattern of life on the device twice for 30 minutes each and blocked all outgoing traffic from the device for 10 minutes. Enforcing a device’s pattern of life restricts it to conduct activity within the device and/or user’s expected pattern of behavior and blocks anything anomalous or unexpected, enabling normal business operations to continue. This response is intended to reduce the potential scale of attacks by disrupting the kill chain, whilst ensuring business disruption is kept to a minimum. 

Figure 7: RESPOND actions taken on a customer deployment to disrupt the Amadey kill chain. 

The Darktrace Threat Research team conducted thorough investigations into Amadey activity observed across the customer base. They were able to identify and contextualize this threat across the fleet, enriching AI insights with collaborative human analysis. Pivoting from AI insights as their primary source of information, the Threat Research team were able to provide layered analysis to confirm this campaign-like activity and assess the threat across multiple unique environments, providing a holistic assessment to customers with contextualized insights.

Conclusion

The presence of the Amadey info-stealer in multiple customer environments highlights the continuing prevalence of MaaS and info-stealers across the threat landscape. The Amadey info-stealer in particular demonstrates that by evading N-day vulnerability patches, threat actors routinely launch new attacks. These malicious actors are then able to evade detection by traditional security tools by employing low and slow data exfiltration techniques, as opposed to large file transfers.

Crucially, Darktrace’s AI insights were coupled with expert human analysis to detect, respond, and provide contextualized insights to notify customers of Amadey activity effectively. DETECT captured Amadey activity taking place on customer deployments, and where enabled, RESPOND’s autonomous technology was able to take immediate action to reduce the scale of such attacks. Finally, the Threat Research team were in place to provide enhanced analysis for affected customers to help security teams future-proof against similar attacks.

Appendices

Darktrace Model Detections 

Anomalous File / EXE from Rare External Location

Device / Initial Breach Chain Compromise

Anomalous Connection / Posting HTTP to IP Without Hostname 

Anomalous Connection / POST to PHP on New External Host

Anomalous Connection / Multiple HTTP POSTs to Rare Hostname 

Compromise / Beaconing Activity To External Rare

Compromise / Slow Beaconing Activity To External Rare

Anomalous Connection / Multiple Failed Connections to Rare Endpoint

List of IOCs

f0ce8614cc2c3ae1fcba93bc4a8b82196e7139f7 - SHA1 - Amadey DLL File Hash

e487edceeef3a41e2a8eea1e684bcbc3b39adb97 - SHA1 - Amadey DLL File Hash

0f9006d8f09e91bbd459b8254dd945e4fbae25d9 - SHA1 - Amadey DLL File Hash

4069fdad04f5e41b36945cc871eb87a309fd3442 - SHA1 - Amadey DLL File Hash

193.106.191[.]201 - IP - Amadey C2 Endpoint

77.73.134[.]66 - IP - Amadey C2 Endpoint

78.153.144[.]60 - IP - Amadey C2 Endpoint

62.204.41[.]252 - IP - Amadey C2 Endpoint

45.153.240[.]94 - IP - Amadey C2 Endpoint

185.215.113[.]204 - IP - Amadey C2 Endpoint

85.209.135[.]11 - IP - Amadey C2 Endpoint

185.215.113[.]205 - IP - Amadey C2 Endpoint

31.41.244[.]146 - IP - Amadey C2 Endpoint

5.154.181[.]119 - IP - Amadey C2 Endpoint

45.130.151[.]191 - IP - Amadey C2 Endpoint

193.106.191[.]184 - IP - Amadey C2 Endpoint

31.41.244[.]15 - IP - Amadey C2 Endpoint

77.73.133[.]72 - IP - Amadey C2 Endpoint

89.163.249[.]231 - IP - Amadey C2 Endpoint

193.56.146[.]243 - IP - Amadey C2 Endpoint

31.41.244[.]158 - IP - Amadey C2 Endpoint

85.209.135[.]109 - IP - Amadey C2 Endpoint

77.73.134[.]45 - IP - Amadey C2 Endpoint

moscow12[.]at - Hostname - Amadey C2 Endpoint

moscow13[.]at - Hostname - Amadey C2 Endpoint

unepeureyore[.]xyz - Hostname - Amadey C2 Endpoint

/fb73jc3/index.php - URI - Amadey C2 Endpoint

/panelis/index.php - URI - Amadey C2 Endpoint

/panelis/index.php?scr=1 - URI - Amadey C2 Endpoint

/panel/index.php - URI - Amadey C2 Endpoint

/panel/index.php?scr=1 - URI - Amadey C2 Endpoint

/panel/Plugins/cred.dll - URI - Amadey C2 Endpoint

/jg94cVd30f/index.php - URI - Amadey C2 Endpoint

/jg94cVd30f/index.php?scr=1 - URI - Amadey C2 Endpoint

/o7Vsjd3a2f/index.php - URI - Amadey C2 Endpoint

/o7Vsjd3a2f/index.php?scr=1 - URI - Amadey C2 Endpoint

/o7Vsjd3a2f/Plugins/cred64.dll - URI - Amadey C2 Endpoint

/gjend7w/index.php - URI - Amadey C2 Endpoint

/hfk3vK9/index.php - URI - Amadey C2 Endpoint

/v3S1dl2/index.php - URI - Amadey C2 Endpoint

/f9v33dkSXm/index.php - URI - Amadey C2 Endpoint

/p84Nls2/index.php - URI - Amadey C2 Endpoint

/p84Nls2/Plugins/cred.dll - URI - Amadey C2 Endpoint

/nB8cWack3/index.php - URI - Amadey C2 Endpoint

/rest/index.php - URI - Amadey C2 Endpoint

/Mb1sDv3/index.php - URI - Amadey C2 Endpoint

/Mb1sDv3/index.php?scr=1 - URI - Amadey C2 Endpoint

/Mb1sDv3/Plugins/cred64.dll  - URI - Amadey C2 Endpoint

/h8V2cQlbd3/index.php - URI - Amadey C2 Endpoint

/f5OknW/index.php - URI - Amadey C2 Endpoint

/rSbFldr23/index.php - URI - Amadey C2 Endpoint

/rSbFldr23/index.php?scr=1 - URI - Amadey C2 Endpoint

/jg94cVd30f/Plugins/cred64.dll - URI - Amadey C2 Endpoint

/mBsjv2swweP/Plugins/cred64.dll - URI - Amadey C2 Endpoint

/rSbFldr23/Plugins/cred64.dll - URI - Amadey C2 Endpoint

/Plugins/cred64.dll - URI - Amadey C2 Endpoint

Mitre Attack and Mapping 

Collection:

T1185 - Man the Browser

Initial Access and Resource Development:

T1189 - Drive-by Compromise

T1588.001 - Malware

Persistence:

T1176 - Browser Extensions

Command and Control:

T1071 - Application Layer Protocol

T1071.001 - Web Protocols

T1090.002 - External Proxy

T1095 - Non-Application Layer Protocol

T1571 - Non-Standard Port

T1105 - Ingress Tool Transfer

References 

[1] https://malpedia.caad.fkie.fraunhofer.de/details/win.amadey

[2] https://asec.ahnlab.com/en/41450/

[3] https://asec.ahnlab.com/en/36634/

Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Zoe Tilsiter
Cyber Analyst
Written by
The Darktrace Threat Research Team

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April 16, 2026

中国系サイバー作戦の進化 - それはサイバーリスクおよびレジリエンスにとって何を意味するか

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サイバーセキュリティにおいては、これまではインシデント、侵害、キャンペーン、そして脅威グループを中心にリスクを整理してきました。これらの要素は現在も重要です -しかし個別のインシデントにとらわれていては、エコシステム全体の形成を見逃してしまう危険があります。国家が支援する攻撃者グループは、個別の攻撃を実行したり短期的な目標を達成したりするためだけではなく、サイバー作戦を長期的な戦略上の影響力を構築するために使用するようになっています。  

当社の最新の調査レポート、Crimson Echoにおいてもこうした状況にあわせて視点を変えています。キャンペーンやマルウェアファミリー、あるいはアクターのラベルを個別のイベントとして分類するのではなく、ダークトレースの脅威調査チームは中国系グループのアクティビティを長期的に連続した行動として分析しました。このように視野を拡大することで、これらの攻撃者がさまざまな環境内でどのように存在しているか、すなわち、静かに、辛抱強く、持続的に、そして多くのケースにおいて識別可能な「インシデント」が発生するかなり前から下準備をしている様子が明らかになりました。  

中国系サイバー脅威のこれまでの変化

中国系サイバーアクティビティは過去20年間において4つのフェーズで進化してきたと言えます。初期の、ボリュームを重視したオペレーションは1990年代にから2000年代初めに見られ、それが2010年代にはより構造化された、戦略に沿った活動となり、そして現在の高度な適応性を備えた、アイデンティティを中心とした侵入へと進化しています。  

現在のフェーズの特徴は、大規模、攻撃の自制、そして永続化です。攻撃者はアクセスを確立し、その戦略的価値を評価し、維持します。これはより全体的な変化を反映したものです。つまりサイバー作戦は長期的な経済的および地政学的戦略に組み込まれる傾向が強まっているということです。デジタル環境へのアクセス、特に国家の重要インフラやサプライチェーン、先端テクノロジーにつながるものは、ある種の長期的な戦略的影響力と見られるようになりました。  

複雑な問題に対するダークトレースのビヘイビア分析アプローチ

国家が支援するサイバーアクティビティを分析する際、難しい問題の1つはアトリビューションです。従来のアプローチは多くの場合、特定の脅威グループ、マルウェアファミリー、あるいはインフラに判定を依存していました。しかしこれらは絶えず変化するものであり、さらに中国系オペレーションの場合、しばしば重複が見られます。

Crimson Echo は2022年7月から2025年9月の間の3年間にDarktrace運用環境で観測された異常なアクティビティを回顧的に分析した結果です。ビヘイビア検知、脅威ハンティング、オープンソースインテリジェンス、および構造化されたアトリビューションフレームワーク(Darktrace Cybersecurity Attribution Framework)を用いて、数十件の中~高確度の事例を特定し、繰り返し発生しているオペレーションのパターンを分析しました。  

この長期的視野を持ったビヘイビア中心型アプローチにより、ダークトレースは侵入がどのように展開していくかについての一定のパターンを特定することができ、動作のパターンが重要であることがあらためて確認されました。  

データが示していること

分析からいくつかの明確な傾向が浮かび上がりました:

  • 標的は戦略的に重要なセクターに集中していたのです。データセット全体で、侵入の88%は重要インフラと分類される、輸送、重要製造業、政府、医療、ITサービスを含む組織で発生しています。   
  • 戦略的に重要な西側経済圏が主な焦点です。米国だけで、観測されたケースの22.5%を占めており、ドイツ、イタリア、スペイン、および英国を含めた主要なヨーロッパの経済圏と合わせると侵入の半数以上(55%)がこれらの地域に集中しています。  
  • 侵入の63%近くがインターネットに接続されたシステムのエクスプロイトから始まっており、外部に露出したインフラの持続的リスクがあらためて浮き彫りになりました。  

サイバー作戦の2つのモデル

データセット全体で、中国系のアクティビティは2つの作戦モデルに従っていることが確認されました。  

1つ目は“スマッシュアンドグラブ”(強奪)型と表現することができます。これらはスピードのために最適化された短期型の侵入です。攻撃者はすばやく動き  – しばしば48時間以内にデータを抜き出し  – ステルス性よりも規模を重視します。これらの侵害の期間の中央値は10日ほどです。検知の危険を冒しても短期的利益を得ようとしていることが明らかです。  

2つ目は“ローアンドスロー”(低速)型です。これらのオペレーションはデータセット内ではあまり多くありませんでしたが、潜在的影響はより重大です。ここでは攻撃者は持続性を重視し、アイデンティティシステムや正規の管理ツールを通じて永続的なアクセスを確立し、数か月間、場合によっては数年にわたって検知されないままアクセスを維持しようとします。1つの注目すべきケースでは、脅威アクターは環境に完全に侵入して永続性を確立し、600日以上経ってからようやく再浮上した例もありました。このようなオペレーションの一時停止は侵入の深さと脅威アクターの長期的な戦略的意図の両方を表しています。このことはサイバーアクセスが長期にわたって保有し活用するべき戦略的資産であることを示しており、これは最も戦略的に重要なセクターにおいて最もよく見られたパターンです。  

同じ作戦エコシステムにおいて両方のモデルを並行して利用し、標的の価値、緊急性、意図するアクセスに基づいて適切なモデルを選択することも可能だという点に注意することも重要です。“スマッシュアンドグラブ” モデルが見られたからといって諜報活動が失敗したとのみ解釈すべきではなく、むしろ目標に沿った作戦上の選択かもしれないと見るべきでしょう。“ローアンドスロー” 型は粘り強い活動のために最適化され、“スマッシュアンドグラブ” 型はスピードのために最適化されています。どちらも意図的な作戦上の選択と見られ、必ずしも能力を表していません。  

サイバーリスクを再考する

多くの組織にとって、サイバーリスクはいまだに一連の個別のイベントとして位置づけられています。何かが発生し、検知され、封じ込められ、組織はそれを乗り越えて前に進みます。しかし永続的アクセスは、特にクラウド、アイデンティティベースのSaaSやエージェント型システム、そして複雑なサプライチェーンネットワークが相互接続された環境では、重大な持続的露出リスクを作り出します。システムの中断やデータの流出が発生していなくても、そのアクセスによって業務や依存関係、そして戦略的意思決定についての情報を得られるかもしれません。サイバーリスクはますます長期的な競合情報収集に似てきています。

その影響はSOCだけの問題ではありません。組織はガバナンス、可視性、レジリエンスについての考え方を見直し、サイバー露出をインシデント対応の問題ではなく構造的なビジネスリスクとして扱う必要があります。  

次の目標

この調査の目的は、これらの脅威の仕組みについてより明確な理解を提供することにより、防御者がより早期にこれらを識別しより効果的に対応できるようにすることです。これには、インジケーターの追跡からビヘイビアの理解にシフトすること、アイデンティティプロバイダーを重要インフラリスクとして扱うこと、サプライヤーの監視を拡大すること、迅速な封じ込めのための能力に投資すること、などが含まれます。  

ダークトレースの最新調査、”Crimson Echo: ビヘイビア分析を通じて中国系サイバー諜報技術を理解する” についてより詳しく知るには、ビジネスリーダー、CISO、SOCアナリストに向けたCrimson Echoレポートのエグゼクティブサマリーを ここからダウンロードしてください。 

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About the author
Nathaniel Jones
VP, Security & AI Strategy, Field CISO

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April 16, 2026

Why Behavioral AI Is the Answer to Mythos

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How AI is breaking the patch-and-prevent security model

The business world was upended last week by the news that Anthropic has developed a powerful new AI model, Claude Mythos, which poses unprecedented risk because of its ability to expose flaws in IT systems.  

Whether it’s Mythos or OpenAI’s GPT-5.4-Cyber, which was just announced on Tuesday, supercharged AI models in the hands of hackers will allow them to carry out attacks at machine speed, much faster than most businesses can stop them.  

This news underscores a stark reality for all leaders: Patching holes alone is not a sufficient control against modern cyberattacks. You must assume that your software is already vulnerable right now. And while LLMs are very good at spotting vulnerabilities, they’re pretty bad at reliably patching them.

Project Glasswing members say it could take months or years for patches to be applied. While that work is done, enterprises must be protected against Zero-Day attacks, or security holes that are still undiscovered.  

Most cybersecurity strategies today are built like a daily multivitamin: broad, preventative, and designed to keep the system generally healthy over time. Patch regularly. Update software. Reduce known vulnerabilities. It’s necessary, disciplined, and foundational. But it’s also built for a world where the risks are well known and defined, cycles are predictable, and exposure unfolds at a manageable pace.

What happens when that model no longer holds?

The AI cyber advantage: Behavioral AI

The vulnerabilities exposed by AI systems like Mythos aren’t the well-understood risks your “multivitamin” was designed to address. They are transient, fast-emerging entry points that exist just long enough to be exploited.

In that environment, prevention alone isn’t enough. You don’t need more vitamins—you need a painkiller. The future of cybersecurity won’t be defined by how well you maintain baseline health. It will be defined by how quickly you respond when something breaks and every second counts.

That’s why behavioral AI gives businesses a durable cyber advantage. Rather than trying to figure out what the attacker looks like, it learns what “normal” looks like across the digital ecosystem of each individual business.  

That’s exactly how behavioral AI works. It understands the self, or what's normal for the organization, and then it can spot deviations in from normal that are actually early-stage attacks.

The Darktrace approach to cybersecurity

At Darktrace, we’ve been defending our 10,000 customers using behavioral AI cybersecurity developed in our AI Research Centre in Cambridge, U.K.

Darktrace was built on the understanding that attacks do not arrive neatly labeled, and that the most damaging threats often emerge before signatures, indicators, or public disclosures can catch up.  

Our AI algorithms learn in real time from your personalized business data to learn what’s normal for every person and every asset, and the flows of data within your organization. By continuously understanding “normal” across your entire digital ecosystem, Darktrace identifies and contains threats emerging from unknown vulnerabilities and compromised supply chain dependencies, autonomously curtailing attacks at machine speed.  

Security for novel threats

Darktrace is built for a world where AI is not just accelerating attacks, but fundamentally reshaping how they originate. What makes our AI so unique is that it's proven time and again to identify cyber threats before public vulnerability disclosures, such as critical Ivanti vulnerabilities in 2025 and SAP NetWeaver exploitations tied to nation-state threat actors.  

As AI reshapes how vulnerabilities are found and exploited, cybersecurity must be anchored in something more durable than a list of known flaws. It requires a real-time understanding of the business itself: what belongs, what does not, and what must be stopped immediately.

What leaders should do right now

The leadership priority must shift accordingly.

First, stop treating unknown vulnerabilities as an edge case. AI‑driven discovery makes them the norm. Security programs built primarily around known flaws, signatures, and threat intelligence will always lag behind an attacker that is operating in real time.

Second, insist on an understanding of what is actually normal across the business. When threats are novel, labels are useless. The earliest and most reliable signal of danger is abnormal behavior—systems, users, or data flows that suddenly depart from what is expected. If you cannot see that deviation as it happens, you are effectively blind during the most critical window.

Finally, assume that the next serious incident will occur before remediation guidance is available. Ask what happens in those first minutes and hours. The organizations that maintain resilience are not the ones waiting for disclosure cycles to catch up—they are the ones that can autonomously identify and contain emerging threats as they unfold.

This is the reality of cybersecurity in an AI‑shaped world. Patching and prevention remain important foundations, but the advantage now belongs to those who can respond instantly when the unpredictable occurs.

Behavioral AI is security designed not just for known threats, but for the ones that AI will discover next.

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About the author
Ed Jennings
President and CEO
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