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December 20, 2023

Ivanti Sentry Vulnerability | Analysis & Insights

Darktrace observed a critical vulnerability in Ivanti Sentry's cybersecurity. Learn how this almost become a huge threat and how we stopped it in its tracks.
Written by
Sam Lister
Specialist Security Researcher
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
Sam Lister
Specialist Security Researcher
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20
Dec 2023

In an increasingly interconnected digital landscape, the prevalence of critical vulnerabilities in internet-facing systems stands as an open invitation to malicious actors. These vulnerabilities serve as a near limitless resource, granting attackers a continually array of entry points into targeted networks.

In the final week of August 2023, Darktrace observed malicious actors validating exploits for one such critical vulnerability, likely the critical RCE vulnerability, CVE-2023-38035, on Ivanti Sentry servers within multiple customer networks. Shortly after these successful tests were carried out, malicious actors were seen delivering crypto-mining and reconnaissance tools onto vulnerable Ivanti Sentry servers.

Fortunately, Darktrace DETECT™ was able to identify this post-exploitation activity on the compromised servers at the earliest possible stage, allowing the customer security teams to take action against affected devices. In environments where Darktrace RESPOND™ was enabled in autonomous response mode, Darktrace was further able inhibit the identified post-exploitation activity and stop malicious actors from progressing towards their end goals.

Exploitation of Vulnerabilities in Ivanti Products

The software provider, Ivanti, offers a variety of widely used endpoint management, service management, and security solutions. In July and August 2023, the Norwegian cybersecurity company, Mnemonic, disclosed three vulnerabilities in Ivanti products [1]/[2]/[3]; two in Ivanti's endpoint management solution, Ivanti Endpoint Manager Mobile (EPMM) (formerly called 'MobileIron Core'), and one in Ivanti’s security gateway solution, Ivanti Sentry (formerly called 'MobileIron Sentry'):

CVE-2023-35078

  • CVSS Score: 10.0
  • Affected Product: Ivanti EPMM
  • Details from Ivanti: [4]/[5]/[6]
  • Vulnerability type: Authentication bypass

CVE-2023-35081

  • CVSS Score: 7.2
  • Affected Product: Ivanti EPMM
  • Details from Ivanti: [7]/[8]/[9]
  • Vulnerability type: Directory traversal

CVE-2023-38035

  • CVSS Score:
  • Affected Product: Ivanti Sentry
  • Details from Ivanti: [10]/[11]/[12]
  • Vulnerability type: Authentication bypass

At the beginning of August 2023, the Cybersecurity and Infrastructure Security Agency (CISA) and the Norwegian National Cyber Security Centre (NCSC-NO) provided details of advanced persistent threat (APT) activity targeting EPMM systems within Norwegian private sector and government networks via exploitation of CVE-2023-35078 combined with suspected exploitation of CVE-2023-35081.

In an article published in August 2023 [12], Ivanti disclosed that a very limited number of their customers had been subjected to exploitation of the Ivanti Sentry vulnerability, CVE-2023-38035, and on the August 22, 2023, CISA added the Ivanti Sentry vulnerability, CVE-2023-38035 to its ‘Known Exploited Vulnerabilities Catalogue’.  CVE-2023-38035 is a critical authentication bypass vulnerability affecting the System Manager Portal of Ivanti Sentry systems. The System Manager Portal, which is accessible by default on port 8433, is used for administration of the Ivanti Sentry system. Through exploitation of CVE-2023-38035, an unauthenticated actor with access to the System Manager Portal can achieve Remote Code Execution (RCE) on the underlying Ivanti Sentry system.

Observed Exploitation of CVE-2023-38035

On August 24, Darktrace observed Ivanti Sentry servers within several customer networks receiving successful SSL connections over port 8433 from the external endpoint, 34.77.65[.]112. The usage of port 8433 indicates that the System Manager Portal was accessed over the connections. Immediately after receiving these successful connections, Ivanti Sentry servers made GET requests over port 4444 to 34.77.65[.]112. The unusual string ‘Wget/1.14 (linux-gnu)’ appeared in the User-Agent headers of these requests, indicating that the command-line utility, wget, was abused to initiate the requests.

Figure 1: Event Log data for an Ivanti Sentry system showing the device breaching a range of DETECT models after contacting 34.77.65[.]112.The suspicious behavior highlighted by DETECT was subsequently investigated by Darktrace’s Cyber AI Analyst™, which was able to weave together these separate behaviors into single incidents representing the whole attack chain.

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Figure 2: AI Analyst Incident representing a chain of suspicious activities from an Ivanti Sentry server.

In cases where Darktrace RESPOND was enabled in autonomous response mode, RESPOND was able to automatically enforce the Ivanti Sentry server’s normal pattern of life, thus blocking further exploit testing.

Figure 3: Event Log for an Ivanti Sentry server showing the device receiving a RESPOND action immediately after trying to 34.77.65[.]112.

The GET requests to 34.77.65[.]112 were responded to with the following HTML document:

Figure 4: Snapshot of the HTML document returned by 34.77.65[.]112.

None of the links within this HTML document were functional. Furthermore, the devices’ downloads of these HTML documents do not appear to have elicited further malicious activities. These facts suggest that the observed 34.77.65[.]112 activities were representative of a malicious actor validating exploits (likely for CVE-2023-38035) on Ivanti Sentry systems.

Over the next 24 hours, these Ivanti Sentry systems received successful SSL connections over port 8433 from a variety of suspicious external endpoints, such as 122.161.66[.]161. These connections resulted in Ivanti Sentry systems making HTTP GET requests to subdomains of ‘oast[.]site’ and ‘oast[.]live’. Strings containing ‘curl’ appeared in the User-Agent headers of these requests, indicating that the command-line utility, cURL, was abused to initiate the requests.

These ‘oast[.]site’ and ‘oast[.]live’ domains are used by the out-of-band application security testing (OAST) service, Interactsh. Malicious actors are known to abuse this service to carry out out-of-band (OOB) exploit testing. It, therefore, seems likely that these activities were also representative of a malicious actor validating exploits for CVE-2023-38035 on Ivanti Sentry systems.

Figure 5: Event Log for Ivanti Sentry system showing the device contacting an 'oast[.]site' endpoint after receiving connections from the suspicious, external endpoint 122.161.66[.]161.

The actors seen validating exploits for CVE-2023-38035 may have been conducting such activities in preparation for their own subsequent malicious activities. However, given the variety of attack chains which ensued from these exploit validation activities, it is also possible that they were carried out by Initial Access Brokers (IABs) The activities which ensued from exploit validation activities identified by Darktrace fell into two categories: internal network reconnaissance and cryptocurrency mining.

Reconnaissance Activities

In one of the reconnaissance cases, immediately after receiving successful SSL connections over port 8443 from the external endpoints 190.2.131[.]204 and 45.159.248[.]179, an Ivanti Sentry system was seen making a long SSL connection over port 443 to 23.92.29[.]148, and making wget GET requests over port 4444 with the Target URIs '/ncat' and ‘/TxPortMap’ to the external endpoints, 45.86.162[.]147 and 195.123.240[.]183.  

Figure 6: Event Log data for an Ivanti Sentry system showing the device making connections to the external endpoints, 45.86.162[.]147, 23.92.29[.]148, and 195.123.240[.]183, immediately after receiving connections from rare external endpoints.

The Ivanti Sentry system then went on to scan for open SMB ports on systems within the internal network. This activity likely resulted from an attacker dropping a port scanning utility on the vulnerable Ivanti Sentry system.

Figure 7: Event Log data for an Ivanti Sentry server showing the device breaching several DETECT models after downloading a port scanning tool from 195.123.240[.]183.

In another reconnaissance case, Darktrace observed multiple wget HTTP requests with Target URIs such as ‘/awp.tar.gz’ and ‘/resp.tar.gz’ to a suspicious, external server (78.128.113[.]130).  Shortly after making these requests, the Ivanti Sentry system started to scan for open SMB ports and to respond to LLMNR queries from other internal devices. These behaviors indicate that the server may have installed an LLMNR poisoning tool, such as Responder. The Ivanti Sentry server also went on to conduct further information-gathering activities, such as LDAP reconnaissance, HTTP-based vulnerability scanning, HTTP-based password searching, and RDP port scanning.

Figure 8: Event Log data for an Ivanti Sentry system showing the device making connections to 78.128.113[.]130, scanning for an open SMB port on internal endpoints, and responding to LLMNR queries from internal endpoints.

In cases where Darktrace RESPOND was active, reconnaissance activities resulted in RESPOND enforcing the Ivanti Sentry server’s pattern of life.

Figure 9: Event Log data for an Ivanti Sentry system receiving a RESPOND action as a result of its SMB port scanning activity.
Figure 10: Event Log data for an Ivanti Sentry system receiving a RESPOND action as a result of its LDAP reconnaissance activity.

Crypto-Mining Activities

In one of the cryptomining cases, Darktrace detected an Ivanti Sentry server making SSL connections to aelix[.]xyz and mining pool endpoints after receiving successful SSL connections over port 8443 from the external endpoint, 140.228.24[.]160.

Figure 11: Event Log data for an Ivanti Sentry system showing the device contacting aelix[.]xyz and mining pool endpoints immediately after receiving connections from the external endpoint, 140.228.24[.]160.

In a cryptomining case on another customer’s network, an Ivanti Sentry server was seen making GET requests indicative of Kinsing malware infection. These requests included wget GET requests to 185.122.204[.]197 with the Target URIs ‘/unk.sh’ and ‘/se.sh’ and a combination of GET and POST requests to 185.221.154[.]208 with the User-Agent header ‘Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/99.0.4844.51 Safari/537.36’ and the Target URIs, ‘/mg’, ‘/ki’, ‘/get’, ‘/h2’, ‘/ms’, and ‘/mu’. These network-based artefacts have been observed in previous Kinsing infections [13].

Figure 12: Event Log data for an Ivanti Sentry system showing the device displaying likely Kinsing C2 activity.

On customer environments where RESPOND was active, Darktrace was able to take swift autonomous action by blocking cryptomining connection attempts to malicious command-and-control (C2) infrastructure, in this case Kinsing servers.

Figure 13: Event Log data for an Ivanti Sentry server showing the device receiving a RESPOND action after attempting to contact Kinsing C2 infrastructure.

Fortunately, due to Darktrace DETECT+RESPOND prompt identification and targeted actions against these emerging threats, coupled with remediating steps taken by affected customers’ security teams, neither the cryptocurrency mining activities nor the network reconnaissance activities led to significant disruption.  

Figure 14: Timeline of observed malicious activities.

Conclusion The inevitable presence of critical vulnerabilities in internet-facing systems underscores the perpetual challenge of defending against malicious intrusions. The near inexhaustible supply of entry routes into organizations’ networks available to malicious actors necessitates a more proactive and vigilant approach to network security.

While it is, of course, essential for organizations to secure their digital environments through the regular patching of software and keeping abreast of developing vulnerabilities that could impact their network, it is equally important to have a safeguard in place to mitigate against attackers who do manage to exploit newly discovered vulnerabilities.

In the case of Ivanti Sentry, Darktrace observed malicious actors validating exploits against affected servers on customer networks just a few days after the public disclosure of the critical vulnerability.  This activity was followed up by a variety of malicious and disruptive, activities including cryptocurrency mining and internal network reconnaissance.

Darktrace DETECT immediately detected post-exploitation activities on compromised Ivanti Sentry servers, enabling security teams to intervene at the earliest possible stage. Darktrace RESPOND, when active, autonomously inhibited detected post-exploitation activities. These DETECT detections, along with their accompanying RESPOND interventions, prevented malicious actors from being able to progress further towards their likely harmful objectives.

Credit to Sam Lister, Senior Cyber Analyst, and Trent Kessler, SOC Analyst  

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Appendices

MITRE ATT&CK Mapping

Initial Access techniques:

  • Exploit Public-Facing Application (T1190)

Credential Access techniques:

  • Unsecured Credentials: Credentials In Files (T1552.001)
  • Adversary-in-the-Middle: LLMNR/NBT-NS Poisoning and SMB Relay (T1557.001)

Discovery

  • Network Service Discovery (T1046)
  • Remote System Discovery (T1018)
  • Account Discovery: Domain Account (T1087.002)

Command and Control techniques:

  • Application Layer Protocol: Web Protocols (T1071.001)
  • Ingress Tool Transfer (T1105)
  • Non-Standard Port (T1571)
  • Encrypted Channel: Asymmetric Cryptography (T1573.002)

Impact techniques

  • Resource Hijacking (T1496)
List of IoCs

Exploit testing IoCs:

·      34.77.65[.]112

·      Wget/1.14 (linux-gnu)

·      cjjovo7mhpt7geo8aqlgxp7ypod6dqaiz.oast[.]site • 178.128.16[.]97

·      curl/7.19.7 (x86_64-redhat-linux-gnu) libcurl/7.19.7 NSS/3.27.1 zlib/1.2.3 libidn/1.18 libssh2/1.4.2

·      cjk45q1chpqflh938kughtrfzgwiofns3.oast[.]site • 178.128.16[.]97

·      curl/7.29.0

Kinsing-related IoCs:

·      185.122.204[.]197

·      /unk.sh

·      /se.sh

·      185.221.154[.]208

·      185.221.154[.]208

·      45.15.158[.]124

·      Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/99.0.4844.51 Safari/537.36

·      /mg

·      /ki

·      /get

·      /h2

·      /ms

·      /mu

·      vocaltube[.]ru • 185.154.53[.]140

·      92.255.110[.]4

·      194.87.254[.]160

Responder-related IoCs:

·      78.128.113[.]130

·      78.128.113[.]34

·      /awp.tar.gz

·      /ivanty

·      /resp.tar.gz

Crypto-miner related IoCs:

·      140.228.24[.]160

·      aelix[.]xyz • 104.21.60[.]147 / 172.67.197[.]200

·      c8446f59cca2149cb5f56ced4b448c8d (JA3 client fingerprint)

·      b5eefe582e146aed29a21747a572e11c (JA3 client fingerprint)

·      pool.supportxmr[.]com

·      xmr.2miners[.]com

·      xmr.2miners[.]com

·      monerooceans[.]stream

·      xmr-eu2.nanopool[.]org

Port scanner-related IoCs:

·      122.161.66[.]161

·      192.241.235[.]32

·      45.86.162[.]147

·      /ncat

·      Wget/1.14 (linux-gnu)

·      45.159.248[.]179

·      142.93.115[.]146

·      23.92.29[.]148

·      /TxPortMap

·      195.123.240.183

·      6935a8d379e086ea1aed159b8abcb0bc8acf220bd1cbc0a84fd806f14014bca7 (SHA256 hash of downloaded file)

Darktrace DETECT Model Breaches

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

·      Device / New User Agent

·      Anomalous Connection / New User Agent to IP Without Hostname

·      Device / New User Agent and New IP

·      Anomalous Connection / Application Protocol on Uncommon Port

·      Anomalous Connection / Callback on Web Facing Device

·      Compromise / High Volume of Connections with Beacon Score

·      Compromise / Large Number of Suspicious Failed Connections

·      Compromise / High Volume of Connections with Beacon Score

·      Compromise / Beacon for 4 Days

·      Compromise / Agent Beacon (Short Period)

·      Device / Large Number of Model Breaches

·      Anomalous Server Activity / Rare External from Server

·      Compromise / Large Number of Suspicious Successful Connections

·      Compromise / Monero Mining

·      Compromise / High Priority Crypto Currency Mining

·      Compromise / Sustained TCP Beaconing Activity To Rare Endpoint

·      Device / Internet Facing Device with High Priority Alert

·      Device / Suspicious SMB Scanning Activity

·      Device / Internet Facing Device with High Priority Alert

·      Device / Network Scan

·      Device / Unusual LDAP Bind and Search Activity

·      Compliance / Vulnerable Name Resolution

·      Device / Anomalous SMB Followed By Multiple Model Breaches

·      Device / New User Agent To Internal Server

·      Anomalous Connection / Suspicious HTTP Activity

·      Anomalous Connection / Unusual Internal Connections

·      Anomalous Connection / Suspicious HTTP Activity

·      Device / RDP Scan

·      Device / Large Number of Model Breaches

·      Compromise / Beaconing Activity To External Rare

·      Compromise / Beacon to Young Endpoint

·      Anomalous Connection / Suspicious HTTP Activity

·      Compromise / Suspicious Internal Use Of Web Protocol

·      Anomalous File / EXE from Rare External Location

·      Anomalous File / Internet Facing System File Download

·      Device / Suspicious SMB Scanning Activity

·      Device / Internet Facing Device with High Priority Alert

·      Device / Network Scan

·      Device / Initial Breach Chain Compromise

References

[1] https://www.mnemonic.io/resources/blog/ivanti-endpoint-manager-mobile-epmm-authentication-bypass-vulnerability/
‍[2] https://www.mnemonic.io/resources/blog/threat-advisory-remote-file-write-vulnerability-in-ivanti-epmm/
‍[3] https://www.mnemonic.io/resources/blog/threat-advisory-remote-code-execution-vulnerability-in-ivanti-sentry/
‍[4] https://www.ivanti.com/blog/cve-2023-35078-new-ivanti-epmm-vulnerability
‍[5] https://forums.ivanti.com/s/article/CVE-2023-35078-Remote-unauthenticated-API-access-vulnerability?language=en_US
‍[6] https://forums.ivanti.com/s/article/KB-Remote-unauthenticated-API-access-vulnerability-CVE-2023-35078?language=en_US
‍[7] https://www.ivanti.com/blog/cve-2023-35081-new-ivanti-epmm-vulnerability
‍[8] https://forums.ivanti.com/s/article/CVE-2023-35081-Arbitrary-File-Write?language=en_US
‍[9] https://forums.ivanti.com/s/article/KB-Arbitrary-File-Write-CVE-2023-35081?language=en_US
‍[10] https://www.ivanti.com/blog/cve-2023-38035-vulnerability-affecting-ivanti-sentry
‍[11] https://forums.ivanti.com/s/article/CVE-2023-38035-API-Authentication-Bypass-on-Sentry-Administrator-Interface?language=en_US
‍[12] https://forums.ivanti.com/s/article/KB-API-Authentication-Bypass-on-Sentry-Administrator-Interface-CVE-2023-38035?language=en_US
‍[13] https://isc.sans.edu/diary/Your+Business+Data+and+Machine+Learning+at+Risk+Attacks+Against+Apache+NiFi/29900

Written by
Sam Lister
Specialist Security Researcher
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
Sam Lister
Specialist Security Researcher

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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.

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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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About the author
Dr. Tim Bazalgette
Chief AI Officer, Darktrace
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