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August 8, 2025

Ivanti Under Siege: Investigating the Ivanti Endpoint Manager Mobile Vulnerabilities (CVE-2025-4427 & CVE-2025-4428)

Darktrace investigates active exploitation of Ivanti EPMM vulnerabilities CVE-2025-4427 and CVE-2025-4428. Threat actors can leverage these CVEs for unauthenticated remote code execution, delivering malware like KrustyLoader. This blog explores evolving post-exploitation tactics and emphasizes the need for continuous visibility and machine-speed response across enterprise network environments.
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
Nahisha Nobregas
SOC Analyst
ivanti cve exploitation edge infrastructure Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
08
Aug 2025

Ivanti & Edge infrastructure exploitation

Edge infrastructure exploitations continue to prevail in today’s cyber threat landscape; therefore, it was no surprise that recent Ivanti Endpoint Manager Mobile (EPMM) vulnerabilities CVE-2025-4427 and CVE-2025-4428 were exploited targeting organizations in critical sectors such as healthcare, telecommunications, and finance across the globe, including across the Darktrace customer base in May 2025.

Exploiting these types of vulnerabilities remains a popular choice for threat actors seeking to enter an organization’s network to perform malicious activity such as cyber espionage, data exfiltration and ransomware detonation.

Vulnerabilities in Ivanti EPMM

Ivanti EPMM allows organizations to manage and configure enterprise mobile devices. On May 13, 2025, Ivanti published a security advisory [1] for their Ivanti Endpoint Manager Mobile (EPMM) devices addressing a medium and high severity vulnerability:

  • CVE-2025-4427, CVSS: 5.6: An authentication bypass vulnerability
  • CVE-2025-4428, CVSS: 7.2: Remote code execution vulnerability

Successfully exploiting both vulnerabilities at the same time could lead to unauthenticated remote code execution from an unauthenticated threat actor, which could allow them to control, manipulate, and compromise managed devices on a network [2].

Shortly after the disclosure of these vulnerabilities, external researchers uncovered evidence that they were being actively exploited in the wild and identified multiple indicators of compromise (IoCs) related to post-exploitation activities for these vulnerabilities [2] [3]. Research drew particular attention to the infrastructure utilized in ongoing exploitation activity, such as leveraging the two vulnerabilities to eventually deliver malware contained within ELF files from Amazon Web Services (AWS) S3 bucket endpoints and to deliver KrustyLoader malware for persistence. KrustyLoader is a Rust based malware that was discovered being downloaded in compromised Ivanti Connect Secure systems back in January 2024 when the zero-day critical vulnerabilities; CVE-2024-21887 and CVE-2023-46805 [10].

This suggests the involvement of the threat actor UNC5221, a suspected China-nexus espionage actor [3].

In addition to exploring the post-exploit tactics, techniques, and procedures (TTPs) observed for these vulnerabilities across Darktrace’s customer base, this blog will also examine the subtle changes and similarities in the exploitation of earlier Ivanti vulnerabilities—specifically Ivanti Connect Secure (CS) and Policy Secure (PS) vulnerabilities CVE-2023-46805 and CVE-2024-21887 in early 2024, as well as CVE-2025-0282 and CVE-2025-0283, which affected CS, PS, and Zero Trust Access (ZTA) in January 2025.

Darktrace Coverage

In May 2025, shortly after Ivanti disclosed vulnerabilities in their EPMM product, Darktrace’s Threat Research team identified attack patterns potentially linked to the exploitation of these vulnerabilities across multiple customer environments. The most noteworthy attack chain activity observed included exploit validation, payload delivery via AWS S3 bucket endpoints, subsequent delivery of script-based payloads, and connections to dpaste[.]com, possibly for dynamic payload retrieval. In a limited number of cases, connections were also made to an IP address associated with infrastructure linked to SAP NetWeaver vulnerability CVE-2025-31324, which has been investigated by Darktrace in an earlier case.

Exploit Validation

Darktrace observed devices within multiple customer environments making connections related to Out-of-Band Application Security Testing (OAST). These included a range of DNS requests and connections, most of which featured a user agent associated with the command-line tool cURL, directed toward associated endpoints. The hostnames of these endpoints consisted of a string of randomly generated characters followed by an OAST domain, such as 'oast[.]live', 'oast[.]pro', 'oast[.]fun', 'oast[.]site', 'oast[.]online', or 'oast[.]me'. OAST endpoints can be leveraged by malicious actors to trigger callbacks from targeted systems, such as for exploit validation. This activity, likely representing the initial phase of the attack chain observed across multiple environments, was also seen in the early stages of previous investigations into the exploitation of Ivanti vulnerabilities [4]. Darktrace also observed similar exploit validation activity during investigations conducted in January 2024 into the Ivanti CS vulnerabilities CVE-2023-46805 and CVE-2024-21887.

Payload Delivery via AWS

Devices across multiple customer environments were subsequently observed downloading malicious ELF files—often with randomly generated filenames such as 'NVGAoZDmEe'—from AWS S3 bucket endpoints like 's3[.]amazonaws[.]com'. These downloads occurred over HTTP connections, typically using wget or cURL user agents. Some of the ELF files were later identified to be KrustyLoader payloads using open-source intelligence (OSINT). External researchers have reported that the KrustyLoader malware is executed in cases of Ivanti EPMM exploitation to gain and maintain a foothold in target networks [2].

In one customer environment, after connections were made to the endpoint fconnect[.]s3[.]amazonaws[.]com, Darktrace observed the target system downloading the ELF file mnQDqysNrlg via the user agent Wget/1.14 (linux-gnu). Further investigation of the file’s SHA1 hash (1dec9191606f8fc86e4ae4fdf07f09822f8a94f2) linked it to the KrustyLoader malware [5]. In another customer environment, connections were instead made to tnegadge[.]s3[.]amazonaws[.]com using the same user agent, from which the ELF file “/dfuJ8t1uhG” was downloaded. This file was also linked to KrustyLoader through its SHA1 hash (c47abdb1651f9f6d96d34313872e68fb132f39f5) [6].

The pattern of activity observed so far closely mirrors previous exploits associated with the Ivanti vulnerabilities CVE-2023-46805 and CVE-2024-21887 [4]. As in those cases, Darktrace observed exploit validation using OAST domains and services, along with the use of AWS endpoints to deliver ELF file payloads. However, in this instance, the delivered payload was identified as KrustyLoader malware.

Later-stage script file payload delivery

In addition to the ELF file downloads, Darktrace also detected other file downloads across several customer environments, potentially representing the delivery of later-stage payloads.

The downloaded files included script files with the .sh extension, featuring randomly generated alphanumeric filenames. One such example is “4l4md4r.sh”, which was retrieved during a connection to the IP address 15.188.246[.]198 using a cURL-associated user agent. This IP address was also linked to infrastructure associated with the SAP NetWeaver remote code execution vulnerability CVE-2025-31324, which enables remote code execution on NetWeaver Visual Composer. External reporting has attributed this infrastructure to a China-nexus state actor [7][8][9].

In addition to the script file downloads, devices on some customer networks were also observed making connections to pastebin[.]com and dpaste[.]com, two sites commonly used to host or share malicious payloads or exploitation instructions [2]. Exploits, including those targeting Ivanti EPMM vulnerabilities, can dynamically fetch malicious commands from sites like dpaste[.]com, enabling threat actors to update payloads. Unlike the previously detailed activity, this behavior was not identified in any prior Darktrace investigations into Ivanti-related vulnerabilities, suggesting a potential shift in the tactics used in post-exploitation stages of Ivanti attacks.

Conclusion

Edge infrastructure vulnerabilities, such as those found in Ivanti EPMM and investigated across customer environments with Darktrace / NETWORK, have become a key tool in the arsenal of attackers in today’s threat landscape. As highlighted in this investigation, while many of the tactics employed by threat actors following successful exploitation of vulnerabilities remain the same, subtle shifts in their methods can also be seen.

These subtle and often overlooked changes enable threat actors to remain undetected within networks, highlighting the critical need for organizations to maintain continuous extended visibility, leverage anomaly based behavioral analysis, and deploy machine speed intervention across their environments.

Credit to Nahisha Nobregas (Senior Cyber Analyst) and Anna Gilbertson (Senior Cyber Analyst)

Appendices

Mid-High Confidence IoCs

(IoC – Type - Description)

-       trkbucket.s3.amazonaws[.]com – Hostname – C2 endpoint

-       trkbucket.s3.amazonaws[.]com/NVGAoZDmEe – URL – Payload

-       tnegadge.s3.amazonaws[.]com – Hostname – C2 endpoint

-       tnegadge.s3.amazonaws[.]com/dfuJ8t1uhG – URL – Payload

-       c47abdb1651f9f6d96d34313872e68fb132f39f5 - SHA1 File Hash – Payload

-       4abfaeadcd5ab5f2c3acfac6454d1176 - MD5 File Hash - Payload

-       fconnect.s3.amazonaws[.]com – Hostname – C2 endpoint

-       fconnect.s3.amazonaws[.]com/mnQDqysNrlg – URL - Payload

-       15.188.246[.]198 – IP address – C2 endpoint

-       15.188.246[.]198/4l4md4r.sh?grep – URL – Payload

-       185.193.125[.]65 – IP address – C2 endpoint

-       185.193.125[.]65/c4qDsztEW6/TIGHT_UNIVERSITY – URL – C2 endpoint

-       d8d6fe1a268374088fb6a5dc7e5cbb54 – MD5 File Hash – Payload

-       64.52.80[.]21 – IP address – C2 endpoint

-       0d8da2d1.digimg[.]store – Hostname – C2 endpoint

-       134.209.107[.]209 – IP address – C2 endpoint

Darktrace Model Detections

-       Compromise / High Priority Tunnelling to Bin Services (Enhanced Monitoring Model)

-       Compromise / Possible Tunnelling to Bin Services

-       Anomalous Server Activity / New User Agent from Internet Facing System

-       Compliance / Pastebin

-       Device / Internet Facing Device with High Priority Alert

-       Anomalous Connection / Callback on Web Facing Device

-       Anomalous File / Script from Rare External Location

-       Anomalous File / Incoming ELF File

-       Device / Suspicious Domain

-       Device / New User Agent

-       Anomalous Connection / Multiple Connections to New External TCP Port

-       Anomalous Connection / New User Agent to IP Without Hostname

-       Anomalous File / EXE from Rare External Location

-       Anomalous File / Internet Facing System File Download

-       Anomalous File / Multiple EXE from Rare External Locations

-       Compromise / Suspicious HTTP and Anomalous Activity

-       Device / Attack and Recon Tools

-       Device / Initial Attack Chain Activity

-       Device / Large Number of Model Alerts

-       Device / Large Number of Model Alerts from Critical Network Device

References

1.     https://forums.ivanti.com/s/article/Security-Advisory-Ivanti-Endpoint-Manager-Mobile-EPMM?language=en_US

2.     https://blog.eclecticiq.com/china-nexus-threat-actor-actively-exploiting-ivanti-endpoint-manager-mobile-cve-2025-4428-vulnerability

3.     https://www.wiz.io/blog/ivanti-epmm-rce-vulnerability-chain-cve-2025-4427-cve-2025-4428

4.     https://www.darktrace.com/blog/the-unknown-unknowns-post-exploitation-activities-of-ivanti-cs-ps-appliances

5.     https://www.virustotal.com/gui/file/ac91c2c777c9e8638ec1628a199e396907fbb7dcf9c430ca712ec64a6f1fcbc9/community

6.     https://www.virustotal.com/gui/file/f3e0147d359f217e2aa0a3060d166f12e68314da84a4ecb5cb205bd711c71998/community

7.     https://www.virustotal.com/gui/ip-address/15.188.246.198

8.     https://blog.eclecticiq.com/china-nexus-nation-state-actors-exploit-sap-netweaver-cve-2025-31324-to-target-critical-infrastructures

9.     https://www.darktrace.com/blog/tracking-cve-2025-31324-darktraces-detection-of-sap-netweaver-exploitation-before-and-after-disclosure

10.  https://www.synacktiv.com/en/publications/krustyloader-rust-malware-linked-to-ivanti-connectsecure-compromises

The content provided in this blog is published by Darktrace for general informational purposes only and reflects our understanding of cybersecurity topics, trends, incidents, and developments at the time of publication. While we strive to ensure accuracy and relevance, the information is provided “as is” without any representations or warranties, express or implied. Darktrace makes no guarantees regarding the completeness, accuracy, reliability, or timeliness of any information presented and expressly disclaims all warranties.

Nothing in this blog constitutes legal, technical, or professional advice, and readers should consult qualified professionals before acting on any information contained herein.

Any references to third-party organizations, technologies, threat actors, or incidents are for informational purposes only and do not imply affiliation, endorsement, or recommendation.

Darktrace, its affiliates, employees, or agents shall not be held liable for any loss, damage, or harm arising from the use of or reliance on the information in this blog.

The cybersecurity landscape evolves rapidly, and blog content may become outdated or superseded. We reserve the right to update, modify, or remove any content without notice.

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
Nahisha Nobregas
SOC Analyst

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May 20, 2026

Prompt Security in Enterprise AI: Strengths, Weaknesses, and Common Approaches

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How enterprise AI Agents are changing the risk landscape  

Generative AI Agents are changing the way work gets done inside enterprises, and subsequently how security risks may emerge. Organizations have quickly realized that providing these agents with wider access to tooling, internal information, and granting permissions for the agent to perform autonomous actions can greatly increase the efficiency of employee workflows.

Early deployments of Generative AI systems led many organizations to scope individual components as self-contained applications: a chat interface, a model, and a prompt, with guardrails placed at the boundary. Research from Gartner has shown that while the volume and scope of Agentic AI deployments in enterprise environments is rapidly accelerating, many of the mechanisms required to manage risk, trust, and cost are still maturing.

The issue now resides on whether an agent can be influenced, misdirected, or manipulated in ways that leads to unsafe behavior across a broader system.

Why prompt security matters in enterprise AI

Prompt security matters in enterprise AI because prompts are the primary way users and systems interact with Agentic AI models, making them one of the earliest and most visible indicators of how these systems are being used and where risk may emerge.

For security teams, prompt monitoring is a logical starting point for understanding enterprise AI usage, providing insight into what types of questions are being asked and tasks are being given to AI Agents, how these systems are being guided, and whether interactions align with expected behavior. Complete prompt security takes this one step further, filtering out or blocking sensitive or dangerous content to prevent risks like prompt injection and data leakage.

However, visibility only at the prompt layer can create a false sense of security. Prompts show what was asked, but not always why it was asked, or what downstream actions were triggered by the agent across connected systems, data sources, or applications.

What prompt security reveals  

The primary function of prompt security is to minimize risks associated with generative and agentic AI use, but monitoring and analysis of prompts can also grant insight into use cases for particular agents and model. With comprehensive prompt security, security teams should be able to answer the following questions for each prompt:

  • What task was the user attempting to complete?
  • What data was included in the request, and was any of the data high-risk or confidential?
  • Was the interaction high-risk, potentially malicious, or in violation of company policy?
  • Was the prompt anomalous (in comparison to previous prompts sent to the agent / model)?

Improving visibility at this layer is a necessary first step, allowing organizations to establish a baseline for how AI systems are being used and where potential risks may exist.  

Prompt security alone does not provide a complete view of risk. Further data is needed to understand how the prompt is interpreted, how context is applied, what autonomous actions the agent takes (if any), or what downstream systems are affected. Understanding the outcome of a query is just as important for complete prompt security as understanding the input prompt itself – for example, a perfectly normal, low-risk prompt may inadvertently result in an agent taking a high-risk action.

Comprehensive AI security systems like Darktrace / SECURE AI can monitor and analyze both the prompt submitted to a Generative AI system, as well as the responses and chain-of-thought of the system, providing greater insight into the behavior of the system. Darktrace / SECURE AI builds on the core Darktrace methodology, learning the expected behaviors of your organization and identifying deviations from the expected pattern of life.

How organizations address prompt security today

As prompt-level visibility has become a focus, a range of approaches have emerged to make this activity more observable and controllable. Various monitoring and logging tools aim to capture prompt inputs to be analyzed after the fact.  

Input validation and filtering systems attempt to intervene earlier, inspecting prompts before they reach the model. These controls look for known jailbreak patterns, language indicative of adversarial attacks, or ambiguous instructions which could push the system off course.

Importantly, for a prompt security solution to be accurate and effective, prompts must be continually observed and governed, rather than treated as a point-in-time snapshot.  

Where prompt security breaks down in real environments

In more complex environments, especially those involving multiple agents or extensive tool use, AI security becomes harder to define and control.

Agent-to-Agent communications can be harder to monitor and trace as these happen without direct user interaction. Communication between agents can create routes for potential context leakage between agents, unintentional privilege escalation, or even data leakage from a higher privileged agent to a lower privileged one.

Risk is shaped not just by what is asked, but by the conditions in which that prompt operates and the actions an agent takes. Controls at the orchestration layer are starting to reflect this reality. Techniques such as context isolation, scoped memory, and role-based boundaries aim to limit how far a prompt’s influence can extend.  

Furthermore, Shadow AI usage can be difficult to monitor. AI systems that are deployed outside of formal governance structures and Generative AI systems hosted on unknown endpoints can fly under the radar and can go unseen by monitoring tools, leaving a critical opening where adversarial prompts may go undetected. Darktrace / SECURE AI features comprehensive detection of Shadow AI usage, helping organizations identify potential risk areas.

How prompt security fits in a broader AI risk model

Prompt security is an important starting point, but it is not a complete security strategy. As AI systems become more integrated into enterprise environments, the risks extend to what resources the system can access, how it interprets context, and what actions it is allowed to take across connected tools and workflows.

This creates a gap between visibility and control. Prompt security alone allows security teams to observe prompt activity but falls short of creating a clear understanding of how that activity translates into real-world impact across the organization.

Closing that gap requires a broader approach, one that connects signals across human and AI agent identities, SaaS, cloud, and endpoint environments. It means understanding not just how an AI system is being used, but how that usage interacts with the rest of the digital estate.

Prompt security, in that sense, is less of a standalone solution and more of an entry point into a larger problem: securing AI across the enterprise as a whole.

Explore how Darktrace / SECURE AI brings prompt security to enterprises

Darktrace brings more than a decade of AI expertise, built on an enterprise‑wide platform designed to operate in and understand the behaviors of the complex, ambiguous environments where today’s AI now lives. With Darktrace / SECURE AI, enterprises can safely adopt, manage, monitor, and build AI within their business.  

Learn about Darktrace / SECURE AI here.

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Jamie Bali
Technical Author (AI) Developer

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May 20, 2026

State of AI Cybersecurity 2026: 77% of security stacks include AI, but trust is lagging

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Findings in this blog are taken from Darktrace’s annual State of AI Cybersecurity Report 2026.

AI is a contributing member of nearly every modern cybersecurity team. As we discussed earlier in this blog series, rapid AI adoption is expanding the attack surface in ways that security professionals have never before experienced while also empowering attackers to operate at unprecedented speed and scale. It’s only logical that defenders are harnessing the power of AI to fight back.

After all, AI can help cybersecurity teams spot the subtle signs of novel threats before humans can, investigate events more quickly and thoroughly, and automate response. But although AI has been widely adopted, this technology is also frequently misunderstood, and occasionally viewed with suspicion.

For CISOs, the cybersecurity marketplace can be noisy. Making sense of competing vendors’ claims to distinguish the solutions that truly deliver on AI’s full potential from those that do not isn’t always easy. Without a nuanced understanding of the different types of AI used across the cybersecurity stack, it is difficult to make informed decisions about which vendors to work with or how to gain the most value from their solutions. Many security leaders are turning to Managed Security Service Providers (MSSPs) for guidance and support.

The right kinds of AI in the right places?

Back in 2024, when we first conducted this annual survey, more than a quarter of respondents were only vaguely familiar with generative AI or hadn’t heard of it at all. Today, GenAI plays a role in 77% of security stacks. This percentage marks a rapid increase in both awareness and adoption over a relatively short period of time.

According to security professionals, different types of AI are widely integrated into cybersecurity tooling:

  • 67% report that their organization’s security stack uses supervised machine learning
  • 67% report that theirs uses agentic AI
  • 58% report that theirs uses natural language processing (NLP)
  • 35% report that theirs uses unsupervised machine learning

But their responses suggest that organizations aren’t always using the most valuable types of AI for the most relevant use cases.

Despite all the recent attention AI has gotten, supervised machine learning isn’t new. Cybersecurity vendors have been experimenting with models trained on hand-labeled datasets for over a decade. These systems are fed large numbers of examples of malicious activity – for instance, strains of ransomware – and use these examples to generalize common indicators of maliciousness – such as the TTPs of multiple known ransomware strains – so that the models can identify similar attacks in the future. This approach is more effective than signature-based detection, since it isn’t tied to an individual byte sequence or file hash. However, supervised machine learning models can miss patterns or features outside the training data set. When adversarial behavior shifts, these systems can’t easily pivot.

Unsupervised machine learning, by contrast, can identify key patterns and trends in unlabeled data without human input. This enables it to classify information independently and detect anomalies without needing to be taught about past threats. Unsupervised learning can continuously learn about an environment and adapt in real time.

One key distinction between supervised and unsupervised machine learning is that supervised learning algorithms require periodic updating and re-training, whereas unsupervised machine learning trains itself while it works.

The question of trust

Even as AI moves into the mainstream, security professionals are eyeing it with a mix of enthusiasm and caution. Although 89% say they have good visibility into the reasoning behind AI-generated outputs, 74% are limiting AI’s ability to take autonomous action in their SOC until explainability improves. 86% do not allow AI to take even small remediation actions without human oversight.

This model, commonly known as “human in the loop,” is currently the norm across the industry. It seems like a best-of-both-worlds approach that allows teams to experience the benefits of AI-accelerated response without relinquishing control – or needing to trust an AI system.

Keeping humans somewhat in the loop is essential for getting the best out of AI. Analysts will always need to review alerts, make judgement calls, and set guardrails for AI's behavior. Their input helps AI models better understand what “normal” looks like, improving their accuracy over time.

However, relying on human confirmation has real costs – it delays response, increases the cognitive burden analysts must bear, and creates potential coverage gaps when security teams are overwhelmed or unavailable. The traditional model, in which humans monitor and act on every alert, is no longer workable at scale.

If organizations depend too heavily on in-the-loop humans, they risk recreating the very problem AI is meant to solve: backlogs of alerts waiting for analyst review. Removing the human from the loop can buy back valuable time, which analysts can then invest in building a proactive security posture. They can also focus more closely on the most critical incidents, where human attention is truly needed.

Allowing AI to operate autonomously requires trust in its decision-making. This trust can be built gradually over time, with autonomous operations expanding as trust grows. But it also requires knowledge and understanding of AI — what it is, how it works, and how best to deploy it at enterprise scale.

Looking for help in all the right places

To gain access to these capabilities in a way that’s efficient and scalable, growing numbers of security leaders are looking for outsourced support. In fact, 85% of security professionals prefer to obtain new SOC capabilities in the form of a managed service.

This makes sense: Managed Security Service Providers (MSSPs) can deliver deep, continuously available expertise without the cost and complexity of building an in-house team. Outsourcing also allows organizations to scale security coverage up or down as needs change, stay current with evolving threats and regulatory requirements, and leverage AI-native detection and response without needing to manage the AI tools themselves.

Preferences for MSSP-delivered security operations are particularly strong in the education, energy (87%), and healthcare sectors. This makes sense: all are high-value targets for threat actors, and all tend to have limited cybersecurity budgets, so the need for a partner who can deliver affordable access to expertise at scale is strong. Retailers also voiced a strong preference for MSSP-delivered services. These companies are tasked with managing large volumes of consumer personal and financial data, and with transforming an industry traditionally thought of as a late adopter to a vanguard of cyber defense. Technology companies, too, have a marked preference for SOC capabilities delivered by MSSPs. This may simply be because they understand the complexity of the threat landscape – and the advantages of specialized expertise — so well.

In order to help as many organizations as possible – from major enterprises to small and midmarket companies – benefit from enterprise-grade, AI-native security, Darktrace is making it easier for MSSPs to deliver its technology. The ActiveAI Security Portal introduces an alert dashboard designed to increase the speed and efficiency of alert triage, while a new AI-powered managed email security solution is giving MSSPs an edge in the never-ending fight against advanced phishing attacks – helping partners as well as organizations succeed on the frontlines of cyber defense.

Explore the full State of AI Cybersecurity 2026 report for deeper insights into how security leaders are responding to AI-driven risks.

Learn more about securing AI in your enterprise.

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