Cloud Security: Addressing Common CISO Challenges with Advanced Solutions
This blog highlights how Darktrace / CLOUD leverages self-learning AI to tackle critical cloud security challenges—such as misconfigurations, hybrid environment complexity, securing productivity suites, and agent fatigue—by providing unified visibility, intelligent monitoring, and real-time threat response to empower organizations with proactive protection.
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
Adam Stevens
Senior Director of Product
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28
Nov 2024
Cloud adoption is a cornerstone of modern business with its unmatched potential for scalability, cost efficiency, flexibility, and net-zero targets around sustainability. However, as organizations migrate more workloads, applications, and sensitive data to the cloud it introduces more complex challenges for CISO’s. Let’s dive into the most pressing issues keeping them up at night—and how Darktrace / CLOUD provides a solution for each.
1. Misconfigurations: The Silent Saboteur
Misconfigurations remain the leading cause of cloud-based data breaches. In 2023 alone over 80% of data breaches involved data stored in the cloud.1 Think open storage buckets or overly permissive permissions; seemingly minor errors that are easily missed and can snowball into major disasters. The fallout of breaches can be costly—both financially and reputationally.
How Darktrace / CLOUD Helps:
Darktrace / CLOUD continuously monitors your cloud asset configurations, learning your environment and using these insights to flag potential misconfigurations. New scans are triggered when changes take place, then grouped and prioritised intelligently, giving you an evolving and prioritised view of vulnerabilities, best practice and mitigation strategies.
2. Hybrid Environments: The Migration Maze
Many organizations are migrating to the cloud, but hybrid setups (where workloads span both on-premises and cloud environments) create unique challenges and visibility gaps which significantly increase complexity. More traditional and most cloud native security tooling struggles to provide adequate monitoring for these setups.
How Darktrace / CLOUD Helps:
Provides the ability to monitor runtime activity for both on-premises and cloud workloads within the same user interface. By leveraging the right AI solution across this diverse data set, we understand the behaviour of your on-premises workloads and how they interact with cloud systems, spotting unusual connectivity or data flow activity during and after the migration process.
This unified visibility enables proactive detection of anomalies, ensures seamless monitoring across hybrid environments, and provides actionable insights to mitigate risks during and after the migration process.
3. Securing Productivity Suites: The Last Mile
Cloud productivity suites like Microsoft 365 (M365) are essential for modern businesses and are often the first step for an organization on a journey to Infrastructure as a Service (IaaS) or Platform as a Service (PaaS) use cases. They also represent a prime target for attackers. Consider a scenario where an attacker gains access to an M365 account, and proceeds to; access sensitive emails, downloading files from SharePoint, and impersonating the user to send phishing emails to internal employees and external partners. Without a system to detect these behaviours, the attack may go unnoticed until significant damage is done.
How Darktrace helps:
Darktrace’s Active AI platform integrates with M365 and establishes an understanding of normal business activity, enabling the detection of abnormalities across its suite including Email, SharePoint and Teams. By identifying subtle deviations in behaviour, such as:
• Unusual file accesses
• Anomalous login attempts from unexpected locations or devices.
• Suspicious email forwarding rules created by compromised accounts.
Darktrace’s Autonomous Response can act precisely to block malicious actions, by disabling compromised accounts and containing threats before they escalate. Precise actions also ensure that critical business operations are maintained even when a response is triggered.
4. Agent Fatigue: The Visibility Struggle
To secure cloud environments, visibility is critical. If you don’t know what’s there, how can you secure it? Many solutions require agents to be deployed on every server, workload, and endpoint. But managing and deploying agents across sprawling hybrid environments can be both complex and time-consuming when following change controls, and especially as cloud resources scale dynamically.
How Darktrace / CLOUD Helps:
Darktrace reduces or eliminates the need for widespread agent deployment. Its agentless by default, integrating directly with cloud environments and providing instant visibility without the operational headache. Darktrace ensures coverage with minimal friction. By intelligently graphing the relationships between assets and logically grouping your deployed Cloud resources, you are equipped with real-time visibility to quickly understand and protect your environment.
So why Darktrace / CLOUD?
Darktrace’s Self-Learning AI redefines cloud security by adapting to your unique environment, detecting threats as they emerge, and responding in real-time. From spotting misconfigurations to protecting productivity suites and securing hybrid environments. Darktrace / CLOUD simplifies cloud security challenges without adding operational burdens.
From Chaos to Clarity
Cloud security doesn’t have to be a game of endless whack-a-mole. With Darktrace / CLOUD, CISOs can achieve the visibility, control, and proactive protection they need to navigate today’s complex cloud ecosystems confidently.
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.
Despite the growing popularity of hybrid environments, most organizations face challenges in achieving unified visibility between on-premises and cloud networks. AI-powered platform tools can bridge this gap in visibility to reduce detection and response times and simplify operations.
From One Alert to a Wider Campaign: Uncovering Novel Threats Through Behavioral Analysis
Key Findings
Darktrace’s Threat Research team is exploring a new method to identify malicious campaigns by correlating similar anomalous activity across its customer fleet.
The method uses unsupervised clustering to identify shared behavioral patterns across multiple customer deployments without relying on prior indicators of compromise (IoCs).
The approach identified shared infrastructure across dozens of customers, with activity linked to the likely POPA botnet.
A second cluster identified anomalous activity associated with Monero cryptocurrency mining.
How does Darktrace conduct threat research?
The Darktrace Threat Research team aims to identify malicious campaign activity affecting multiple customers. By their nature, these campaigns will lead to shared behavioral patterns across multiple victims.
In this piece, we describe a method for identifying malicious campaign activity that uses this simple observation as a guiding principle. We begin by outlining a few key facts about the modern threat landscape and why behavioral detection is necessary to meet the challenges it presents. We then explore how fleet-wide behavioral detections can be leveraged using the principle above to systematically identify multi-deployment campaigns impacting the Darktrace customer fleet. Finally, we demonstrate that this research is not purely theoretical, using real examples of campaign activity detected through this novel approach.
Shifting to a behavioral-focused approach
The modern era of cyber-defense necessitates detection capabilities beyond traditional signature-based methods. Technologies such as antivirus signatures, hash-based malware identification, and IP blocklists have historically been successful in defending against known threats. However, they are easily circumvented by attackers and inherently limited in defending against the novel attack techniques that today's threat landscape is producing.
Recent breakthroughs in AI represent a transformational risk multiplier that cannot be ignored. AI systems do not merely execute existing attacks at machine speed; there are signs they can also synthesize novel tradecraft [1], increasing the likelihood that defenders will encounter attack patterns that have never previously been observed. Meanwhile vulnerability disclosures continue to accelerate. More than 48,000 CVEs were published in 2025, and published statistics to date indicate that 2026 is already on track to significantly exceed that figure [2]. While multiple factors may contribute to vulnerability discovery, AI is seen as a key factor in this rapid growth. Organizations should expect a growing proportion of malicious activity to fall outside the scope of detections that rely solely on prior knowledge of known threats.
By design, Darktrace’s behavioral and anomaly-focused detection capabilities are well-suited to this testing landscape. With a focus on detecting unknown and unexpected activity that deviates from the baseline of “normal” behavior, detection of CVE exploitation without prior knowledge of the vulnerability and before public disclosure, for example, is achievable [3][4].
Darktrace’s diverse customer base benefits from a further layer of protection: working with only deidentified alert metadata, our threat researchers hunt proactively across the fleet. Beyond the level of the individual customer, there is a second, larger vantage to be leveraged.
Darktrace data provides unique insight into the unexpected behavioral patterns that occur across its customer fleet. What can we see if we look at this fleet-wide telemetry and hunt for systematic behavioral campaigns that impact our customer base?
Correlating across the customer base
At the deployment level, Darktrace focuses on whether activity is unusual in the context of an organization's normal behavior, but as we zoom out and look at these alerts at the fleet level, the problem fundamentally changes . We are no longer seeking anomalies in largely normal data; we are seeking systematic patterns within data already known to be anomalous. An isolated behavior anomaly may be notable for a single customer; the same fingerprint across dozens is a signal of a structured campaign. Hunting for such campaigns and sharing our findings are key areas of focus for Darktrace’s Threat Research team. In this spirit, Darktrace is actively exploring a a new research approach that leverages deidentified alert metadata to increase campaign detection ability.
This approach is based on the premise that an expert human analyst presented with a set of alerts will identify likely related activity by assessing whether the metadata fields indicate a relationship. For example, two connections to a rare endpoint that exhibits naming structure that is likely from a single algorithm are a strong indicator, as is the use of an unusual port carrying a given protocol (e.g. port 6000 for SSL). Darktrace’s approach to campaign detection encodes this expert human judgement process through deterministic embedding functions that obtain reproducible vector representations of alert data. Unsupervised clustering is then applied to these vector representations to obtain groups of alerts that describe semantically similar alert data across multiple customers. These clusters therefore represent similar anomalous activity that has occurred across multiple customers. In other words, these clusters are candidates for malicious campaign activity.
Figure 1: Layers of AI-based analysis provided through Darktrace products and cross-customer correlation.
Case study 1 – unusual connections to shared underlying infrastructure
In one recent case, the clustering produced several tight groups of alerts spanning dozens of customers. Each group centered on a single domain whose subdomains were algorithmically generated (e.g. s01699, s1736, s91). Examined side by side, these clusters shared an unmistakable behavioral shape: each involved a domain with a clear naming pattern (e.g. s016841786.net-echo[.]com, s1670.pixellog[.]io, s217.pixellog[.]io) , with connections over SSL on the unusual port 6000 to servers hosted by non-mainstream providers (e.g. OVH, Hetzner, GTHost, Leaseweb), rather than generally expected content-delivery networks.
Figure 2: An example model alert triggering on unusual outbound connections and data transfer to s1670.pixellog[.]io.
Closer inspection revealed that the same subdomain label appeared under different domains (e.g. s1786.net-echo[.]com and s1786.pixellog[.]io). Following this lead, Darktrace’s threat researchers pivoted across the customer base and found numerous distinct subdomain labels reused under more than one registered domain. In several cases, the same label resolved to the same IP across different domains (e.g. s1704 under both pixellog[.]io and net-echo[.]com → 148.113.222[.]244; s01684 → 15.235.67[.]95), suggesting the use of ephemeral subdomain infrastructure in an attempt to avoid signature-based defense techniques.
This example demonstrates how multi-deployment data can uncover shared infrastructure that may be difficult to identify at the individual deployment level alone. In particular, the presented method facilitated this discovery by producing coherent, tight clusters of geometrically similar vector embeddings of malicious activity from large-scale fleet data, which could then be presented to a researcher for further investigation. This activity highlighted across the Darktrace customer base was ultimately linked to likely POPA botnet activity through use of open-source intelligence (OSINT) [5].
Case Study 2 – anomalous user agent and txt URI
A second example shows the method surfacing a single, self-contained cluster. The clustering was not given any indicators to look for; instead, it grouped devices that shared an unusual combination of features. On inspection, that shared consisted of:
- unusual user agent (tes)
- specific request path (/md5_GIP.txt)
- uncommon destination port (83)
- hostname impersonating Google (matching regular expression ^www\.google\.com\.[a-zA-Z0-9]+\.fast-dns-host\.com$)
Treating these shared features as IoCs, Darktrace’s threat researchers then pivoted to an exploration across the customer base to investigate the full breadth of the campaign. This search identified further commonalities; additional domains which shared the same IP as the domain family highlighted in the cluster, as well as evidence of cryptocurrency mining on affected devices. Additional OSINT research also aligned with these findings, linking the discovered payloads and infrastructure to Monero cryptocurrency mining [6].
Conclusion
The method outlined in this blog is under active development, with ongoing technical refinement providing opportunities to further enhance its capabilities. While the capability continues to mature, this approach to threat hunting leverages the powerful vantage point of fleetwide telemetry across a diverse customer base to help protect Darktrace customers. This method allows campaigns that at the deployment level appear as isolated alerts to be seen at their true scale and spread. It enables proactive fleet-wide analysis without prior IoCs to identify campaigns impacting Darktrace customers.
This is possible because of what the method is built on: the atomic unit is the Darktrace alert, a signal that encodes years of cyber expertise, while the multi-deployment nature of the method allows organizations to indirectly benefit from findings from other networks through shared intelligence by Darktrace’s Threat Research team.
Credit to Daniel Levy (Threat Hunting Data Scientist), Paul Jennings (Principal Cyber Analyst), Emma Foulger (Global Head, Threat Operations)
“I wonder if running that tool influenced the connectivity in some way.”
“I might need to target down or reboot, but first, I'll investigate the network using ARP. I wonder if ARP is responding properly. If I see "ip neigh stale," that could indicate a problem. I think arping might help in this situation, but I’ll need to keep an eye on how the network is behaving. It's all about figuring out where the issue lies!”
図10：エージェントの異常なLDAPアクティビティに対してトリガーされたこのLinuxサーバーに対するモデル検知は、Cyber AI Analystによって自律的に調査され、その調査結果にはエージェントの異常なSMBセッションアクティビティに関する内容が含まれ、Cyber AIAnalystインシデントに記録されました。
DarktraceのCyber AI Analystは、エージェントのアクティビティによってトリガーされたモデル検知を自律的に調査し、その調査結果を1つの重要なインシデントレポートにまとめました。インシデント報告書の内容は、エージェントによるネットワークスキャンから始まりコーディング演習内容のSMBベースの上書きで終わる、エージェントの行動の各段階を詳しくカバーしています。
図20：Cyber AI Analystは、モデル検知に対する自律的な調査を通じて得られた所見をつなぎ合わせ、エージェントの進行状況のタイムラインを作成し、アクティビティを視覚的に表したグラフとともに提供しました。
Cyber Verification Program下のAnthropic Opus 4.6モデルなど、他のフロンティアモデルを使用したエージェントも、Darktraceのシミュレートされた環境で同じ設定とタスクを使ってテストされました。反復の度に、エージェントは演習の内容または採点プログラムを変更して不正を行おうとしました。基盤となるモデルに関係なく、エージェントは自分が置かれた環境を侵害することで、100％のスコアを達成する方法を見つけることに成功していました。これらのエージェントによるハッキングは、アクティビティの内容こそ違っていましたが、すべてその環境のベースラインの振る舞いから大きく逸脱した動作を引き起こしていました。これらの逸脱はDarktraceプラットフォームに十分な検知の機会を提供し、エージェントの逃げ場はありませんでした。
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