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September 18, 2024

FortiClient EMS Exploited: Attack Chain & Post Exploitation Tactics

Read about the methods used to exploit FortiClient EMS and the critical post-exploitation tactics that affect cybersecurity defenses.
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
Emily Megan Lim
Cyber Analyst
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18
Sep 2024

Cyber attacks on internet-facing systems

In the first half of 2024, the Darktrace Threat Research team observed multiple campaigns of threat actors targeting vulnerabilities in internet-facing systems, including Ivanti CS/PS appliances, Palo Alto firewall devices, and TeamCity on-premises.

These systems, which are exposed to the internet, are often targeted by threat actors to gain initial access to a network. They are constantly being scanned for vulnerabilities, known or unknown, by opportunistic actors hoping to exploit gaps in security. Unfortunately, this exposure remains a significant blind spot for many security teams, as monitoring edge infrastructure can be particularly challenging due to its distributed nature and the sheer volume of external traffic it processes.

In this blog, we discuss a vulnerability that was exploited in Fortinet’s FortiClient Endpoint Management Server (EMS) and the post-exploitation activity that Darktrace observed across multiple customer environments.

What is FortiClient EMS?

FortiClient is typically used for endpoint security, providing features such as virtual private networks (VPN), malware protection, and web filtering. The FortiClient EMS is a centralized platform used by administrators to enforce security policies and manage endpoint compliance. As endpoints are remote and distributed across various locations, the EMS needs to be accessible over the internet.

However, being exposed to the internet presents significant security risks, and exploiting vulnerabilities in the system may give an attacker unauthorized access. From there, they could conduct further malicious activities such as reconnaissance, establishing command-and-control (C2), moving laterally across the network, and accessing sensitive data.

CVE-2023-48788

CVE-2023-48788 is a critical SQL injection vulnerability in FortiClient EMS that can allow an attacker to gain unauthorized access to the system. It stems from improper neutralization of special elements used in SQL commands, which allows attackers to exploit the system through specially crafted requests, potentially leading to Remote Code Execution (RCE) [1]. This critical vulnerability was given a CVSS score of 9.8 and can be exploited without authentication.

The affected versions of FortiClient EMS include:

  • FortiClient EMS 7.2.0 to 7.2.2 (fixed in 7.2.3)
  • FortiClient EMS 7.0.1 to 7.0.10 (fixed in 7.0.11)

The vulnerability was publicly disclosed on March 12, 2024, and an exploit proof of concept was released by Horizon3.ai on March 21 [2]. Starting from March 24, almost two weeks after the initial disclosure, Darktrace began to observe at least six instances where the FortiClient EMS vulnerability had likely been exploited on customer networks. Seemingly exploited devices in multiple customer environments were observed performing anomalous activities, including the installation of Remote Monitoring and Management (RMM) tools, which was also reported by other security vendors around the same time [3].

Darktrace’s Coverage

Initial Access

To understand how the vulnerability can be exploited to gain initial access, we first need to explain some components of the FortiClient EMS:

  • The service FmcDaemon.exe is used for communication between the EMS and enrolled endpoint clients. It listens on port 8013 for incoming client connections.
  • Incoming requests are then sent to FCTDas.exe, which translates requests from other server components into SQL requests. This service interacts with the Microsoft SQL database.
  • Endpoint clients communicate with the FmcDaemon on the server on port 8013 by default.

Therefore, an SQL injection attack can be performed by crafting a malicious payload and sending it over port 8013 to the server. To carry out RCE, an attacker may send further SQL statements to enable and use the xp_cmdshell functionality of the Microsoft SQL server [2].

Shortly before post-exploitation activity began, Darktrace had observed incoming connections to some of the FortiClient EMS devices over port 8013 from the external IPs 77.246.103[.]110, 88.130.150[.]101, and 45.155.141[.]219. This likely represented the threat actors sending an SQL injection payload over port 8013 to the EMS device to validate the exploit.

Establish C2

After exploiting the vulnerability and gaining access to an EMS device on one customer network, two additional devices were seen with HTTP POST requests to 77.246.103[.]110 and 212.113.106[.]100 with a new PowerShell user agent.

Interestingly, the IP 212.113.106[.]100 has been observed in various other campaigns where threat actors have also targeted internet-facing systems and exploited other vulnerabilities. Open-source intelligence (OSINT) suggests that this indicator of compromise (IoC) is related to the Sliver C2 framework and has been used by threat actors such as APT28 (Fancy Bear) and APT29 (Cozy Bear) [4].

Unusual file downloads were also observed on four devices, including:

  • “SETUP.MSI” from 212.32.243[.]25 and 89.149.200[.]91 with a cURL user agent
  • “setup.msi” from 212.113.106[.]100 with a Windows Installer user agent
  • “run.zip” from 95.181.173[.]172 with a PowerShell user agent

The .msi files would typically contain the RMM tools Atera or ScreenConnect [5]. By installing RMM tools for C2, attackers can leverage their wide range of functionalities to carry out various tasks, such as file transfers, without the need to install additional tools. As RMM tools are designed to maintain a stable connection to remote systems, they may also allow the attackers to ensure persistent access to the compromised systems.

A scan of the endpoint 95.181.173[.]172 shows various other files such as “RunSchedulerTask.ps1” and “anydesk.exe” being hosted.

Screenshot of the endpoint 95.181.173[.]172 hosting various files [6].
Figure 1: Screenshot of the endpoint 95.181.173[.]172 hosting various files [6].

Shortly after these unusual file downloads, many of the devices were also seen with usage of RMM tools such as Splashtop, Atera, and AnyDesk. The devices were seen connecting to the following endpoints:

  • *[.]relay.splashtop[.]com
  • agent-api[.]atera[.]com
  • api[.]playanext[.]com with user agent AnyDesk/8.0.9

RMM tools have a wide range of legitimate capabilities that allow IT administrators to remotely manage endpoints. However, they can also be repurposed for malicious activities, allowing threat actors to maintain persistent access to systems, execute commands remotely, and even exfiltrate data. As the use of RMM tools can be legitimate, they offer threat actors a way to perform malicious activities while blending into normal business operations, which could evade detection by human analysts or traditional security tools.

One device was also seen making repeated SSL connections to a self-signed endpoint “azure-documents[.]com” (104.168.140[.]84) and further HTTP POSTs to “serv1[.]api[.]9hits[.]com/we/session” (128.199.207[.]131). Although the contents of these connections were encrypted, they were likely additional infrastructure used for C2 in addition to the RMM tools that were used. Self-signed certificates may also be used by an attacker to encrypt C2 communications.

Internal Reconnaissance

Following the exploit, two of the compromised devices then started to conduct internal reconnaissance activity. The following figure shows a spike in the number of internal connections made by one of the compromised devices on the customer’s environment, which typically indicates a network scan.

Advanced Search results of internal connections made an affected device.
Figure 2: Advanced Search results of internal connections made an affected device.

Reconnaissance tools such as Advanced Port Scanner (“www[.]advanced-port-scanner[.]com”) and Nmap were also seen being used by one of the devices to conduct scanning activities. Nmap is a network scanning tool commonly used by security teams for legitimate purposes like network diagnostics and vulnerability scanning. However, it can also be abused by threat actors to perform network reconnaissance, a technique known as Living off the Land (LotL). This not only reduces the need for custom or external tools but also reduces the risk of exposure, as the use of a legitimate tool in the network is unlikely to raise suspicion.

Privilege Escalation

In another affected customer network, the threat actor’s attempt to escalate their privileges was also observed, as a FortiClient EMS device was seen with an unusually large number of SMB/NTLM login failures, indicative of brute force activity. This attempt was successful, and the device was later seen authenticating with the credential “administrator”.

Figure 3: Advanced Search results of NTLM (top) and SMB (bottom) login failures.

Lateral Movement

After escalating privileges, attempts to move laterally throughout the same network were seen. One device was seen transferring the file “PSEXESVC.exe” to another device over SMB. This file is associated with PsExec, a command-line tool that allows for remote execution on other systems.

The threat actor was also observed leveraging the DCE-RPC protocol to move laterally within the network. Devices were seen with activity such as an increase in new RPC services, unusual requests to the SVCCTL endpoint, and the execution of WMI commands. The DCE-RPC protocol is typically used to facilitate communication between services on different systems and can allow one system to request services or execute commands on another.

These are further examples of LotL techniques used by threat actors exploiting CVE-2023-48788, as PsExec and the DCE-RPC protocol are often also used for legitimate administrative operations.

Accomplish Mission

In most cases, the threat actor’s end goal was not clearly observed. However, Darktrace did detect one instance where an unusually large volume of data had been uploaded to “put[.]io”, a cloud storage service, indicating that the end goal of the threat actor had been to steal potentially sensitive data.

In a recent investigation of a Medusa ransomware incident that took place in July 2024, Darktrace’s Threat Research team found that initial access to the environment had likely been gained through a FortiClient EMS device. An incoming connection from 209.15.71[.]121 over port 8013 was seen, suggesting that CVE-2023-48788 had been exploited. The device had been compromised almost three weeks before the ransomware was actually deployed, eventually resulting in the encryption of files.

Mitigating risk with proactive exposure management and real-time detection

Threat actors have continued to exploit unpatched vulnerabilities in internet-facing systems to gain initial access to a network. This highlights the importance of addressing and patching vulnerabilities as soon as they are disclosed and a fix is released. However, due to the rapid nature of exploitation, this may not always be enough. Furthermore, threat actors may even be exploiting vulnerabilities that are not yet publicly known.

As the end goals for a threat actor can differ – from data exfiltration to deploying ransomware – the post-exploitation behavior can also vary from actor to actor. However, AI security tools such as Darktrace / NETWORK can help identify and alert for post-exploitation behavior based on abnormal activity seen in the network environment.

Despite CVE-2023-48788 having been publicly disclosed and fixed in March, it appears that multiple threat actors, such as the Medusa ransomware group, have continued to exploit the vulnerability on unpatched systems. With new vulnerabilities being disclosed almost every other day, security teams may find it challenging continuously patch their systems.

As such, Darktrace / Proactive Exposure Management could also alleviate the workload of security teams by helping them identify and prioritize the most critical vulnerabilities in their network.

Insights from Darktrace’s First 6: Half-year threat report for 2024

First 6: half year threat report darktrace screenshot

Darktrace’s First 6: Half-Year Threat Report 2024 highlights the latest attack trends and key threats observed by the Darktrace Threat Research team in the first six months of 2024.

  • Focuses on anomaly detection and behavioral analysis to identify threats
  • Maps mitigated cases to known, publicly attributed threats for deeper context
  • Offers guidance on improving security posture to defend against persistent threats

Appendices

Credit to Emily Megan Lim (Cyber Security Analyst) and Ryan Traill (Threat Content Lead)

References

[1] https://nvd.nist.gov/vuln/detail/CVE-2023-48788

[2] https://www.horizon3.ai/attack-research/attack-blogs/cve-2023-48788-fortinet-forticlientems-sql-injection-deep-dive/

[3] https://redcanary.com/blog/threat-intelligence/cve-2023-48788/

[4] https://www.fortinet.com/blog/threat-research/teamcity-intrusion-saga-apt29-suspected-exploiting-cve-2023-42793

[5] https://redcanary.com/blog/threat-intelligence/cve-2023-48788/

[6] https://urlscan.io/result/3678b9e2-ad61-4719-bcef-b19cadcdd929/

List of IoCs

IoC - Type - Description + Confidence

  • 212.32.243[.]25/SETUP.MSI - URL - Payload
  • 89.149.200[.]9/SETUP.MSI - URL - Payload
  • 212.113.106[.]100/setup.msi - URL - Payload
  • 95.181.173[.]172/run.zip - URL - Payload
  • serv1[.]api[.]9hits[.]com - Domain - Likely C2 endpoint
  • 128.199.207[.]131 - IP - Likely C2 endpoint
  • azure-documents[.]com - Domain - C2 endpoint
  • 104.168.140[.]84 - IP - C2 endpoint
  • 77.246.103[.]110 - IP - Likely C2 endpoint
  • 212.113.106[.]100 - IP - C2 endpoint

Darktrace Model Detections

Anomalous Connection / Callback on Web Facing Device

Anomalous Connection / Multiple HTTP POSTs to Rare Hostname

Anomalous Connection / New User Agent to IP Without Hostname

Anomalous Connection / Posting HTTP to IP Without Hostname

Anomalous Connection / Powershell to Rare External

Anomalous Connection / Rare External SSL Self-Signed

Anomalous Connection / Suspicious Self-Signed SSL

Anomalous Server Activity / Rare External from Server

Anomalous Server Activity / New User Agent from Internet Facing System

Anomalous Server Activity / Server Activity on New Non-Standard Port - External

Compliance / Remote Management Tool On Server

Device / New User Agent

Device / New PowerShell User Agent

Device / Attack and Recon Tools

Device / ICMP Address Scan

Device / Network Range Scan

Device / Network Scan

Device / RDP Scan

Device / Suspicious SMB Scanning Activity

Anomalous Connection / Multiple SMB Admin Session

Anomalous Connection / New or Uncommon Service Control

Anomalous Connection / Unusual Admin SMB Session

Device / Increase in New RPC Services

Device / Multiple Lateral Movement Breaches

Device / New or Uncommon WMI Activity

Device / New or Unusual Remote Command Execution

Device / SMB Lateral Movement

Device / Possible SMB/NTLM Brute Force

Unusual Activity / Successful Admin Brute-Force Activity

User / New Admin Credentials on Server

Unusual Activity / Enhanced Unusual External Data Transfer

Unusual Activity / Unusual External Data Transfer

Unusual Activity / Unusual External Data to New Endpoint

Device / Large Number of Model Breaches

Device / Large Number of Model Breaches from Critical Network Device

MITRE ATT&CK Mapping

Tactic – ID: Technique

Initial Access – T1190: Exploit Public-Facing Application

Resource Development – T1587.003: Develop Capabilities: Digital Certificates

Resource Development – T1608.003: Stage Capabilities: Install Digital Certificate

Command and Control – T1071.001: Application Layer Protocol: Web Protocols

Command and Control – T1219: Remote Access Software

Execution – T1059.001: Command and Scripting Interpreter: PowerShell

Reconnaissance – T1595: Active Scanning

Reconnaissance – T1590.005: Gather Victim Network Information: IP Addresses

Discovery – T1046: Network Service Discovery

Credential Access – T1110: Brute Force

Defense Evasion,Initial Access,Persistence,Privilege Escalation – T1078: Valid Accounts

Lateral Movement – T1021.002: Remote Services: SMB/Windows Admin Shares

Lateral Movement – T1021.003: Remote Services: Distributed Component Object Model

Execution – T1569.002: System Services: Service Execution

Execution – T1047: Windows Management Instrumentation

Exfiltration – T1041: Exfiltration Over C2 Channel

Exfiltration – T1567.002: Exfiltration Over Web Service: Exfiltration to Cloud Storage

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
Emily Megan Lim
Cyber Analyst

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September 17, 2026

The Problem of Re-defining Human Value in the Agentic Age

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Newsfeeds are constantly informing us about the rapid escalation of agentic AI systems. These systems move far beyond simple machine-based computations, and  the focused, defined and bounded assistance that most AI systems started out as.

The next evolution of AI will harness the agentic properties of orchestration, automation, and heightened value-chains in IT, taking on the burden of workflow management, not just workflow delivery.

In nearly all of these instances, promises are made such as ‘this will free up human time’ or ‘this will allow people to focus on higher-order strategy’. However that message is delivered, one thing is clear: in ceding the orchestration and management of work to increasingly sophisticated AI agents and agentic systems, human value will be elevated to a particular and specific layer: the ability to judge its outputs.  As AI takes on more tasks, humans should be able to focus on a higher level of governance; making sure the decisions that AI offers us are ethical, responsible and worthwhile.  

But there are two major problems with that approach.

This blog discusses the problem of how Agentic systems are re-shaping how we review information, where we fit in, and when we make decisions.  It also discusses the problem of how the increased flow of confident, generated information affects the way we make judgement.  This blog considers how human judgement needs to adapt, and how a behavioral defense approach – using techniques pioneered by Darktrace – can help us do that.

The challenge of knowing where human judgement belongs

As we confer more automated decision-making to agentic systems, it might look increasingly less like ‘granting permissions’, and more like ‘surrendering authority’.  

The judgement layer for AI-generated work is not a fixed boundary. We have become used to the idea of a ‘human in the loop’ (HITL) and, historically, relationships between humans and IT systems were reasonably clear and bounded.  Computer and software systems were programmed to carry out certain tasks or automated functions, and humans could control the gates and decision points where actions were undertaken. Even across highly complex computational workflows, human interaction was a controllable node within the process; we were able to configure and regulate. But in the agentic age, where that human interaction sits, and what it can influence shifts every time AI systems are granted autonomy.  

This leads us to the first problem: if humans are moving themselves (or are being moved) into the ‘judgement’ part of the value chain, exactly where and when do we exercise that judgement?  

Humans are no longer the sole shepherds of computer-based or software-controlled outputs.  We are at times at least one step further (and slower) behind the new agentic shepherds.  We might also be blind to what they are doing.  Not only might we be removed and blind to the actions of our AI shepherds, but with the challenge of unknown, unapproved AI systems operating beyond our control, humans might not even know that our work is being shepherded by an AI at all.  Simply put, with the advent of greater levels of autonomy and orchestration, humans are at risk of not even knowing where to apply our newly-extended powers of strategic judgement.

Shadow AI – the use of unapproved AI systems or processes – is a growing threat to the role of effective governance and oversight. Shadow AI isn't just the AI you can't see. Its the AI you already know about being used in an unapproved way. The ability to generate effective oversight of the AI systems you use (or that are used on your behalf) will be increasingly important to ensure that human judgement in the AI value chain is effective, and deliberately placed.

The problem of what makes good judgement

The second problem lies in how flawed human judgement can be.  Humans are historically, notoriously, and, sometimes dangerously, unreliable when it comes to exercising judgement.  Humans are prone to the worst kinds of bias, the seduction of malign influence, and the sometimes-overwhelming urge to succeed. AI has long had a known flaw of operating with sycophancy, providing outputs that tend to agree with or flatter the human user.  But as AI grows ever more effective, there is a risk of both hyper-enablement (where humans increasingly and knowingly enable AI despite potential harm), as well as the greater risk of suggestion. Both of these aspects could skew the newly-elevated input of human judgement.

Imagine a highly competent AI system that has just orchestrated and managed a dizzying array of processes and workflows.  The AI is designed to present the human decision-maker with recommendations; based on analysis, comparison and other programmed factors.  This is where the human judgement layer is enabled.  But what if that judgement is summarily diffused by an AI-based recommendation that emulates the decision, provides plausible but unattractive alternatives, then suggests (or, worse, directs) the human end-user to take a particular course of action.

The risk here is that you are given a recommendation, tailored to your preferences (which the AI has learned, or which you have divulged), and which appears to make perfect sense.  It appears to be a well-weighted recommendation, with sound arguments that tap into our inherent biases or inclinations so that a specific decision-path is followed. With the growth of agentic systems specifically designed to match user profiles (from Cowork agents to ‘digital twin’ models), the likelihood of agentic influence could badly skew human judgement or, at the least, devalue the proposition that humans are taking a higher-layer of strategic control over AI-based decisions.

If AI convincingly recommends something that may be problematic, it can be difficult to discern both accurate data, and the context required to make the right judgement.

Given the two problems described above, the job of exercising valuable human judgement in the agentic age can draw down to these two questions:

  • When should humans intervene in the agentic process?  
  • How can we make the best possible judgement calls?

What humans contribute that AI cannot

For all the flaws that make human judgement unreliable, people have the edge over even the most sophisticated and powerful AI systems when it comes to issues such as ethics and social context.  An AI system can, with startling granularity, rank the value of adopting a new business proposal: offering predictive metrics on costs, returns, market value, time-to-deliver operations, conformance with legal registers, etc.  But it can’t tell if the business proposal is ethically sound, or if the business venture will potentially affect groups outside of the analyzed proposal. It can’t tell you if the CEO has a ‘bad feeling’ about this effort.  It can’t tell you if this is the right thing to do.  

The ability to add social context, balance complex interpersonal dynamics, understand nuance, and to go beyond what seems economically reasonable is where human judgement can add value.  

Human judgement is difficult to encapsulate in metrics. And the way we train our development may need to adapt too. Rather than building up a gradual, experiential knowledge base, we should think about training the skill of judgement itself; especially for an agentic age.

How behavioral security strengthens AI governance

If this all feels like a vicious circle (‘I need AI help to make good judgements’ / ‘AI can twist what I need to judge’) it needn’t be. The key to this is having a defense-in-depth approach, with tools that can actually help.

This is precisely where behavioral security becomes important. The complex and nuanced way that humans exercise judgement is often rooted in our ability to recognize behavior that doesn't look right. We may not always be able to articulate it immediately, but we can often identify when an action, recommendation, or outcome feels inconsistent with the context around it. As AI systems take on more responsibility across the decision chain, preserving that ability to recognize meaningful deviations becomes increasingly important.

Darktrace’s / SECURE AI is designed to do exactly that. It applies behavioral security to AI ecosystems, helping organizations understand how people, AI tools, identities, and agents interact across the business. By learning the patterns of normal AI usage and surfacing activity that deviates from those patterns, it provides security teams with the context needed to investigate risk, understand unusual behavior, and make informed governance decisions. Rather than relying solely on predefined rules or assumptions, this behavioral understanding helps organizations distinguish between expected AI activity and behavior that warrants closer scrutiny.

This matters because we are already in an era of information overload. If humans are expected to elevate their value through strategic judgement, the ability to do this without being overwhelmed by data (good or bad) will be critical.  

We need the ability to discern when we're being misled by AI, and whether our judgement calls are being made on the basis of accurate, contextual information. Darktrace / SECURE AI provides that additional layer of defensive security for activity we cannot easily see. Whether it is suspected Shadow AI or skewed recommendations, the net result is a protected organization, where users can more effectively use AI to make positive judgements.

For those where that judgement is a critical skill (both individuals, as well as those working in security teams), improving our metacognition - the ability to understand information in a broader context - will supercharge the value of human judgement. When those judgements are grounded in context rather than assumptions we have better information to make sound decisions.

Conclusion

Human judgement is a skill that is honed over time and experience.  Darktrace’s / SECURE AI employs the same principles, but at machine-speed. Rather than influencing or directing, Darktrace / SECURE AI offers AI-enabled assurance; providing human-based judgement with the right context to make a balanced decision.  

What we judge can be valued by the legitimacy of its outputs. For AI, those outputs are valued on the speed and accuracy of the information provided.  Increasingly for humans, the value of our outputs will be based on the validity of our judgement, and how we justify our decisions in ways that engineer confidence.  

Humans often know more than we can express, while AI is prone to expressing more than it truly understands. Humans can bridge the context AI often fails to appreciate. When that judgement is supported by relevant, impartial AI systems, this is the future space where good AI governance will be exercised.

Discover Darktrace / SECURE AI.

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Jason Lusted
AI Governance Advisor

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September 4, 2026

Darktrace Advances Incident Investigation and AI-Agent Security with OpenAI Daybreak Models

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Earlier this year, Darktrace joined OpenAI’s Daybreak Defense Network to explore how their cyber capabilities can be integrated within Darktrace products and services to transform how security teams move from signal to action.

At the heart of this work is Darktrace's behavioral understanding of customer environments and identification of complex security incidents, combined with OpenAI models that can add context to help explain why an incident matters and its potential impact on the business. By bringing these capabilities into defensive workflows security teams already use, the goal is to give defenders not just greater visibility, but the context and guidance they need to act with confidence.

Since joining the program, we've been working with OpenAI to explore how these capabilities can address specific security challenges for defenders.

The problem we're solving

Attackers continue to change how they operate, including by using AI to increase the speed and scale of some techniques. Security teams are already managing a large volume of alerts, and the question isn't just what's happening, but how it could affect the organization. Even when an incident is fully investigated and correlated, technical severity alone doesn't tell a security team how much it actually matters to the business. That same challenge extends to internal AI adoption. As organizations adopt more AI systems and agents, security teams need visibility into their behavior, access and activity, along with the broader business context needed to identify and investigate potential risk.

Darktrace's Adaptive AI™ builds a detailed, organization-specific picture of what's normal for each environment, and uses that picture to investigate threats and identify complex security activity across domains. OpenAI's models can build on Darktrace's correlated, technically prioritized incidents by adding context that can help defenders understand what may be at stake.

What we're building

Our work is focused on two areas: supporting security investigation and response, and helping defenders identify risky behavior across enterprise AI systems and agents.

The first aligns Darktrace's behavioral understanding with OpenAI models to support  security investigation and prioritization. Darktrace's Adaptive AI continuously learns the unique patterns of normal behavior within each customer it protects, creating a deep, organization-specific understanding of its digital estate. When unusual activity emerges, OpenAI's models can draw on that context to help analysts investigate the incident, understand its significance and assess potential business consequences — reducing the need to manually assemble context from fragmented signals.

Second, we are exploring how these capabilities can support AI-agent and runtime security through Darktrace / SECURE AI™. OpenAI’s Daybreak models can build on the detections and visibility Darktrace / SECURE AI provides, connecting signals across a customer's environment and help defenders identify potentially risky behavior involving AI systems and agents. Activity that might appear isolated can instead be connected with related signals, helping defenders investigate the broader context and determine appropriate remediation.

Darktrace brings deep cybersecurity expertise, an evolving understanding of each customer's environment, and AI-driven identification of threats across the digital estate. Through the Daybreak Defense Network, Darktrace is exploring how OpenAI models can augment those capabilities in defensive security workflows — supporting incident investigation and response and improving visibility into AI-agent and runtime risk.

These capabilities are still in development, and we're excited to continue building on this work.

To learn more about how Darktrace continues to innovate to meet today's most pressing security challenges, register for our upcoming launch broadcast here.

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