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August 2, 2023

Darktrace's Detection of Ransomware & Syssphinx

Read how Darktrace identified an attack technique by the threat group, Syssphinx. Learn how Darktrace's quick identification process can spot a threat.
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
Adam Potter
Senior Cyber Analyst
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02
Aug 2023

Introduction

As the threat of costly cyber-attacks continues represent a real concern to security teams across the threat landscape, more and more organizations are strengthening their defenses with additional security tools to identify attacks and protect their networks. As a result, malicious actors are being forced to adapt their tactics, modify existing variants of malicious software, or utilize entirely new variants.  

Symantec recently released an article about Syssphinx, the financially motivated cyber threat group previously known for their point-of-sale attacks. Syssphinx attempts to deploy ransomware on customer networks via a modified version of their ‘Sardonic’ backdoor. Such activity highlights the ability of threat actors to alter the composition and presentation of payloads, tools, and tactics.

Darktrace recently detected some of the same indicators suggesting a likely Syssphinx compromise within the network of a customer trialing the Darktrace DETECT™ and RESPOND™ products. Despite the potential for variations in the construction of backdoors and payloads used by the group, Darktrace’s anomaly-based approach to threat detection allowed it to stitch together a detailed account of compromise activity and identify the malicious activity prior to disruptive events on the customer’s network.

What is Syssphinx?

Syssphinx is a notorious cyber threat entity known for its financially motivated compromises.  Also referred to as FIN8, Syssphinx has been observed as early as 2016 and is largely known to target private sector entities in the retail, hospitality, insurance, IT, and financial sectors.[1]

Although Syssphinx primarily began focusing on point-of-sale style attacks, the activity associated with the group has more recently incorporated ransomware variants into their intrusions in a potential bid to further extract funds from target organizations.[2]

Syssphinx Sardonic Backdoor

Given this gradual opportunistic incorporation of ransomware, it should not be surprising that Syssphinx has slowly expanded its repertoire of tools.  When primarily performing point-of-sale compromises, the group was known for its use of point-of-sale specific malwares including BadHatch, PoSlurp/PunchTrack, and PowerSniff/PunchBuggy/ShellTea.[3]

However, in a seeming response to updates in detection systems while using previous indicators of compromise (IoCs), Syssphinx began to modify its BadHatch malware.  This resulted in the use of a C++ derived backdoor known as “Sardonic”, which has the ability to aggregate host credentials, spawn additional command sessions, and deliver payloads to compromised devices via dynamic-link library (DLL).[4],[5]

Analysis of the latest version of Sardonic reveals further changes to the malware to elude detection. These shifts include the implementation of the backdoor in the C programming language, and additional over-the-network communication obfuscation techniques. [6]

During the post-exploitation phase, the group tends to rely on “living-off-the-land” tactics, whereby an attacker utilizes tools already present within the organization’s digital environment to avoid detection. Syssphinx seems to utilize system-native tools such as PowerShell and the Windows Management Instrumentation (WMI) interface.[7] It is also not uncommon to see Windows-based vulnerability exploits employed on compromised devices. This has been observed by researchers who have examined previous iterations of Syssphinx backdoors.[8] Syssphinx also appears to exhibit elements of strategic patience and discipline in its operations, with significant time gaps in operations noted by researchers. During this time, it appears likely that updates and tweaks were applied to Syssphinx payloads.

Compromise Details

In late April 2023, Darktrace identified an active compromise on the network of a prospective customer who was trialing Darktrace DETECT+RESPOND. The customer, a retailer in EMEA with hundreds of tracked devices, reached out to the Darktrace Analyst team via the Ask the Expert (ATE) service for support and further investigation, following the encryption of their server and backup data storage in an apparent ransomware attack. Although the encryption events fell outside Darktrace’s purview due to a limited set up of trial appliances, Darktrace was able to directly track early stages of the compromise before exfiltration and encryption events began. If a full deployment had been set up and RESPOND functionality had been configured in autonomous response mode, Darktrace may have helped mitigate such encryption events and would have aided in the early identification of this ransomware attack.

Initial Intrusion and Establishment of Command and Control (C2) Infrastructure

As noted by security researchers, Syssphinx largely relies on social engineering and phishing emails to deliver its backdoor payloads. As there were no Darktrace/Email™ products deployed for this customer, it would be difficult to directly observe the exact time and manner of initial payload delivery related to this compromise. This is compounded by the fact that the customer had only recently began using Darktrace’s products during their trial period. Given the penchant for patience and delay by Syssphinx, it is possible that the intrusion began well before Darktrace had visibility of the organization’s network.

However, beginning on April 30, 2023, at 07:17:31 UTC, Darktrace observed the domain controller dc01.corp.XXXX  making repeated SSL connections to the endpoint 173-44-141-47[.]nip[.]io. In addition to the multiple open-source intelligence (OSINT) flags for this endpoint, the construction of the domain parallels that of the initial domain used to deliver a backdoor, as noted by Symantec in their analysis (37-10-71-215[.]nip[.]io). This activity likely represented the initial beaconing being performed by the compromised device. Additionally, an elevated level of incoming external data over port 443 was observed during this time, which may be associated with the delivery of the Sardonic backdoor payload. Given the unusual use of port 443 to perform SSH connections later seen in the kill chain of this attack, this activity could also parallel the employment of embedded backdoor payloads seen in the latest iteration of the Sardonic backdoor noted by Symantec.

Figure 1: Graph of the incoming external data surrounding the time of the initial establishment of command and control communication for the domain controller. As seen in the graph, the spike in incoming external data during this time may parallel the delivery of Syssphinx Sardonic backdoor.

Regardless, the domain controller proceeded to make repeated connections over port 443 to the noted domain.

Figure 2: Breach event log for the domain controller making repeated connections over port 443 to the rare external destination endpoint in constitute the establishment of C2 communication.

Internal Reconnaissance/Privilege Escalation

Following the establishment of C2 communication, Darktrace detected numerous elements of internal reconnaissance. On Apr 30, 2023, at 22:06:26 UTC, the desktop device desktop_02.corp.XXXX proceeded to perform more than 100 DRSGetNCChanges requests to the aforementioned domain controller. These commands, which are typically implemented over the RPC protocol on the DRSUAPI interface, are frequently utilized in Active Directory sync attacks to copy Active Directory information from domain controllers. Such activity, when not performed by new domain controllers to sync Active Directory contents, can indicate malicious domain or user enumeration, credential compromise or Active Directory enumeration.

Although the affected device made these requests to the previously noted domain controller, which was already compromised, such activity may have further enabled the compromise by allowing the threat actor to transfer these details to a more easily manageable device.

The device performing these DRSGetNCChanges requests would later be seen performing lateral movement activity and making connections to malicious endpoints.

Figure 3: Breach log highlighting the DRS operations performed by the corporate device to the destination domain controller. Such activity is rarely authorized for devices not tagged as administrative or as domain controllers.

Execution and Lateral Movement

At 23:09:53 UTC on April 30, 2023, the original domain server proceeded to make multiple uncommon WMI calls to a destination server on the same subnet (server01.corp.XXXX). Specifically, the device was observed making multiple RPC calls to IWbem endpoints on the server, which included login and ExecMethod (method execution) commands on the destination device. This destination device later proceeded to conduct additional beaconing activity to C2 endpoints and exfiltrate data.

Figure 4: Breach log for the domain controller performing WMI commands to the destination server during the lateral movement phase of the breach.

Similarly, beginning on May 1, 2023, at 00:11:09 UTC, the device desktop_02.corp.XXXX made multiple WMI requests to two additional devices, one server and one desktop, within the same subnet as the original domain controller. During this time, desktop_02.corp.XXXX  also utilized SMBv1, an outdated and typically non-compliant version communication protocol, to write the file rclone.exe to the same two destination devices. Rclone.exe, and its accompanying bat file, is a command-line tool developed by IT provider Rclone, to perform file management tasks. During this time, Darktrace also observed the device reading and deleting an unexpected numeric file on the ADMIN$ of the destination server, which may represent additional defense evasion techniques and tool staging.

Figure 5: Event log highlighting the writing of rclone.exe using the outdated SMBv1 communication protocol.
Figure 6: SMB logs indicating the reading and deletion of numeric string files on ADMIN$ shares of the destination devices during the time of the rclone.exe SMB writes. Such activity may be associated with tool staging and could indicate potential defense evasion techniques.

Given that the net loader sample analyzed by Symantec injects the backdoor into a WmiPrvSE.exe process, the use of WMI operations is not unexpected. Employment of WMI also correlates with the previously mentioned “living-off-the-land” tactics, as WMI services are commonly used for regular network and system administration purposes. Moreover, the staging of rclone.exe, a legitimate file management tool, for data exfiltration underscores attempts to blend into existing and expected network traffic and remain undetected on the customer’s network.

Data Exfiltration and Impact

Initial stages of data exfiltration actually began prior to some of the lateral movement events described above. On April 30, 2023, 23:09:47 the device server01.corp.XXXX, transferred nearly 11 GB of data to 173.44[.]141[.]47, as well as to the rare external IP address 170.130[.]55[.]77, which appears to have served as the main exfiltration destination during this compromise. Furthermore, the host made repeated connections to the same external IP associated with the initial suspicious beaconing activity (173.44[.]141[.]47) over SSL.

While the data exfiltration event unfolded, the device, server01.corp.XXXX, made multiple HTTP requests to 37.10[.]71[.]215, which featured URIs requesting the rclone.exe and rclone.bat files. This IP address was directly involved in the sample analyzed by Symantec. Furthermore, one of the devices that received the SMB file writes of rclone.exe and the WMI commands from desktop_02.corp.XXXX also performed SSL beaconing to endpoints associated with the compromise.

Between 01:20:45 - 03:31:41 UTC on May 1, 2023, a Darktrace detected a series of devices on the network performing a repeated pattern of activity, namely external connectivity followed by suspicious file downloads and external data transfer operations. Specifically, each affected device made multiple HTTP requests to 37.10[.]71[.]215 for rclone files. The devices proceeded to download the executable and/or binary files, and then transfer large amounts of data to the aforementioned endpoints, 170.130[.]55[.]77 and or 173-44-141-47[.]nip[.]io. Although the devices involved in data exfiltration utilized port 443 as a destination port, the connections actually used the SSH protocol. Darktrace recognized this behavior as unusual as port 443 is typically associated with the SSL protocol, while port 22 is reserved for SSH. Therefore, this activity may represent the threat actor’s attempts to remain undetected by security tools.

This unexpected use of SSH over port 443 also correlates with the descriptions of the new Sardonic backdoor according to threat researchers. Further beaconing and exfiltration activity was performed by an additional host one day later whereby the device made suspicious repeated connections to the aforementioned external hosts.

Figure 7: Connection details highlighting the use of port 443 for SSH connections during the exfiltration events.

In total, nine separate devices were involved in this pattern of activity. Five of these devices were labeled as ‘administrative’ devices according to their hostnames. Over the course of the entire exfiltration event, the attackers exfiltrated almost 61 GB of data from the organization’s environment.

Figure 8: Graph showing the levels of external data transfer from a breach device for one day on either side of the breach time. There is a large spike in such activity during the time of the breach that underscores the exfiltration events.

In addition to the individual anomaly detections by DETECT, Darktrace’s Cyber AI Analyst™ launched an autonomous investigation into the unusual behavior carried out by affected devices, connecting and collating multiple security events into one AI Analyst Incident. AI Analyst ensures that Darktrace can recognize and link the individual steps of a wider attack, rather than just identifying isolated incidents. While traditional security tools may mistake individual breaches as standalone activity, Darktrace’s AI allows it to provide unparalleled visibility over emerging attacks and their kill chains. Furthermore, Cyber AI Analyst’s instant autonomous investigations help to save customer security teams invaluable time in triaging incidents in comparison with human teams who would have to commit precious time and resources to conduct similar pattern analysis.

In this specific case, AI Analyst identified 44 separate security events from 18 different devices and was able to tie them together into one incident. The events that made up this AI Analyst Incident included:

  • Possible SSL Command and Control
  • Possible HTTP Command and Control
  • Unusual Repeated Connections
  • Suspicious Directory Replication ServiceActivity
  • Device / New or Uncommon WMI Activity
  • SMB Write of Suspicious File
  • Suspicious File Download
  • Unusual External Data Transfer
  • Unusual External Data Transfer to MultipleRelated Endpoints
Figure 9: Cyber AI Incident log highlighting multiple unusual anomalies and connecting them into one incident.

Had Darktrace RESPOND been enabled in autonomous response mode on the network of this prospective customer, it would have been able to take rapid mitigative action to block the malicious external connections used for C2 communication and subsequent data exfiltration, ideally halting the attack at this stage. As previously discussed, the limited network configuration of this trial customer meant that the encryption events unfortunately took place outside of Darktrace’s scope. When fully configured on a customer environment, Darktrace DETECT can identify such encryption attempts as soon as they occur. Darktrace RESPOND, in turn, would be able to immediately intervene by applying preventative actions like blocking internal connections that may represent file encryption, or limiting potentially compromised devices to a previously established pattern of life, ensuring they cannot carry out any suspicious activity.

Conclusion

Despite the limitations posed by the customer’s trial configuration, Darktrace demonstrated its ability to detect malicious activity associated with Syssphinx and track it across multiple stages of the kill chain.

Darktrace’s ability to identify the early stages of a compromise and various steps of the kill chain, highlights the necessity for machine learning-enabled, anomaly-based detection. In the face of threats such as Syssphinx, that exhibit the propensity to recast backdoor payloads and incorporate on “living-off-the-land” tactics, signatures and rules-based detection may not prove as effective. While Syssphinx and other threat groups will continue to adopt new tools, methods, and techniques, Darktrace’s Self-Learning AI is uniquely positioned to meet the challenge of such threats.

Appendix

DETECT Model Breaches Observed

•      Anomalous Server Activity / Anomalous External Activity from Critical Network Device

•      Anomalous Connection / Anomalous DRSGetNCChanges Operation

•      Device / New or Uncommon WMI Activity

•      Compliance / SMB Drive Write

•      Anomalous Connection / Data Sent to Rare Domain

•      Anomalous Connection / Uncommon 1 GiB Outbound

•      Unusual Activity / Unusual External Data Transfer

•      Unusual Activity / Unusual External Data to New Endpoints

•      Compliance / SSH to Rare External Destination

•      Anomalous Connection / Unusual SMB Version 1 Connectivity

•      Anomalous File / EXE from Rare External Location

•      Anomalous File / Script from Rare External Location

•      Compromise / Suspicious File and C2

•      Device / Initial Breach Chain Compromise

AI Analyst Incidents Observed

•      Possible SSL Command and Control

•      Possible HTTP Command and Control

•      Unusual Repeated Connections

•      Suspicious Directory Replication Service Activity

•      Device / New or Uncommon WMI Activity

•      SMB Write of Suspicious File

•      Suspicious File Download

•      Unusual External Data Transfer

•      Unusual External Data Transfer to Multiple Related Endpoints

IoCs

IoC - Type - Description

37.10[.]71[.]215 – IP – C2 + payload endpoint

173-44-141-47[.]nip[.]io – Hostname – C2 – payload

173.44[.]141[.]47 – IP – C2 + potential payload

170.130[.]55[.]77 – IP – Data exfiltration endpoint

Rclone.exe – Exe File – Common data tool

Rclone.bat – Script file – Common data tool

MITRE ATT&CK Mapping

Command and Control

T1071 - Application Layer Protocol

T1071.001 – Web protocols

T1573 – Encrypted channels

T1573.001 – Symmetric encryption

T1573.002 – Asymmetric encryption

T1571 – Non-standard port

T1105 – Ingress tool transfer

Execution

T1047 – Windows Management Instrumentation

Credential Access

T1003 – OS Credential Dumping

T1003.006 – DCSync

Lateral Movement

T1570 – Lateral Tool Transfer

T1021 - Remote Services

T1021.002 - SMB/Windows Admin Shares

T1021.006 – Windows Remote Management

Exfiltration

T1048 - Exfiltration Over Alternative Protocol

T1048.001 - Exfiltration Over Symmetric Encrypted Non-C2 Protocol

T1048.002 - Exfiltration Over Symmetric Encrypted Non-C2 Protocol

T1041 - Exfiltration Over C2 Channel

References

[1] https://cyberscoop.com/syssphinx-cybercrime-ransomware/

[2] https://symantec-enterprise-blogs.security.com/blogs/threat-intelligence/Syssphinx-FIN8-backdoor

[3] https://www.bleepingcomputer.com/news/security/fin8-deploys-alphv-ransomware-using-sardonic-malware-variant/

[4] https://symantec-enterprise-blogs.security.com/blogs/threat-intelligence/Syssphinx-FIN8-backdoor

[5] https://thehackernews.com/2023/07/fin8-group-using-modified-sardonic.html

[6] https://symantec-enterprise-blogs.security.com/blogs/threat-intelligence/Syssphinx-FIN8-backdoor

[7] https://symantec-enterprise-blogs.security.com/blogs/threat-intelligence/Syssphinx-FIN8-backdoor

[8] https://www.mandiant.com/resources/blog/windows-zero-day-payment-cards

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
Adam Potter
Senior Cyber Analyst

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July 6, 2026

NIST Just Proved It: AI Security Can’t Be Solved With Rules

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Static AI guardrails are inherently limited

As organizations adopt generative AI, many still assume that the right set of guardrails will be enough. The problem is you can’t anticipate every way these systems might be misused, abused or attacked. What NIST has done is put a mathematical foundation under that intuition.

In recent research building on Gödel’s incompleteness theorems, which showed that any system built on a fixed set of rules will always have gaps, NIST demonstrates that there is no finite set of guardrails that can be universally robust against adversarial prompts. In plain terms, if your defense is based on a fixed set of rules, there will always be inputs that bypass them. Not because the rules are badly written, but because the problem space is bigger than static rules can ever cover.

This is not new in cybersecurity - detection rules have always had to live with this trade-off. What is different with GenAI is the scale and shape of that problem. These systems are built on human language, and human language is not bounded. It is fluid, contextual and deliberately ambiguous. The number of ways intent can be hidden is effectively limitless. You are not defending against a defined protocol or a fixed exploit chain. You are defending against the entire expressive capacity of people.

So attempting to create a complete set of rules is the wrong starting point. It assumes the problem can be deterministically described. NIST’s work shows that it cannot. Organizations still need a way to manage AI risk, but the traditional approach of defining allowed and disallowed patterns is always going to lag behind what is actually happening. The same input can be benign in one context and risky in another, and static rules struggle to capture that distinction.

The question then is what fills that gap?

AI security must shift from rules to behavior

What's required is a shift in what you are trying to understand. Rules try to describe what should and shouldn't happen. Behavior shows you what is happening. Or to put it another way, if inputs are unbounded and adversaries adapt, the only stable signal is behavior.

In a GenAI context, that means analyzing how an AI model is being used, how prompts evolve over time, how outputs are shaped, and where AI agent interactions start to drift from what is expected. It means moving from static definitions of bad to a more dynamic understanding of intent.

Instead of trying to predict every bad prompt, you focus on identifying when behavior starts to move outside expected norms. Instead of asking whether a single input matches a rule, you ask whether the overall pattern of activity makes sense for the system and how it’s being used.

Guardrails remain important but they are only one layer

This does not eliminate the need for guardrails. They still play a role. But they will never address the entire problem space and are simply one part of your defense in depth approach.

NIST’s proof is useful because it makes this explicit. It removes the assumption that with enough effort, a complete rule set is achievable. It isn’t.

Once you accept that, the shift becomes unavoidable. This is no longer a problem of writing better rules, but of understanding behavior in a space where the possible inputs are effectively unbounded.

For security leaders, that changes the nature of the problem. It is less about defining what should be allowed, and more about recognizing when something is no longer consistent with expected behavior.

That does not remove the need for guardrails, but it does change their role. They set boundaries, but they do not define understanding. The gap between the two is where risk now sits.

In the end, this is what “can’t be solved with rules” really means. Rules will always leave gaps, and those gaps are not theoretical. They show up in how systems actually behave Not what we expect them to do, or what we intended them to do, but what they are doing in practice. That is where the signal is, and increasingly, that is where the security problem sits.

References:

https://www.nist.gov/news-events/news/2026/06/nist-mathematical-proof-supports-transition-continuous-monitor-and-update

https://ieeexplore.ieee.org/document/11475847

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About the author
Andrew Hollister
Principal Solutions Engineer, Cyber Technician

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July 1, 2026

5 Ways AI is changing traditional security models according to modern CISOs

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The Reality of Securing AI in Motion

Traditional security tools were built for environments defined by fixed rules and predictable workflows. But AI behavior is non-deterministic. The same prompt can produce different outcomes, and risk often emerges gradually as AI behavior adapts, and permissions drift over time. This creates a constantly shifting environment where security teams are working to define control in a system that resists stability. “In AI security, yesterday's priorities can become tomorrow's blind spots. The landscape shifts that fast,” warned the SVP and Head of Technology and Cybersecurity of a real estate investment trust. Conventional approaches, which rely on establishing and maintaining a steady baseline, struggle to keep up with that level of change.

At the same time, AI adoption is accelerating across organizations, often faster than security teams can implement the controls needed to manage it. “The car is being built while it’s already on the road,” explained the CISO of a global private fund administrator. “The threats we're securing against today won't be the threats we're facing tomorrow. What kept us up three months ago looks nothing like what we're dealing with today.”

As businesses move quickly to unlock value from AI, security teams are left closing gaps in real time, while also facing adversaries who are using AI to make their attacks more scalable, adaptive, and difficult to detect. In this recent roundtable discussion of CISOs and security leaders, five themes emerged around AI cyber risk.  

1. AI agents with human access but no human judgment

In Darktrace’s 2026 State of AI Cybersecurity report, 96% of the surveyed security professionals agree that AI significantly improves the speed and efficiency with which they work. Yet, 92% admitted that they’re concerned with the security implications of the use of AI agents across their workforce.

AI agents now operate with human-level permissions across systems, acting at machine speed, orchestrating actions across platforms, and making decisions without the judgment or caution a person would apply. Unlike human users, they cannot be expected to pause and question whether a given action is appropriate.

Their identities are also difficult to inventory, govern, and audit. As agents become easier to deploy than legacy IT systems ever were, organizations are quickly losing track of what is running, what it has access to, and what it is doing. This creates a growing class of highly privileged, autonomous actors operating without the visibility or oversight that traditional identity and access controls were designed to provide.“While AI adoption is critical to running a modern business, AI alone can’t solve all our cybersecurity challenges,” said a global financial sector CISO. “We still need think critically and use human judgement. Those are two things AI can’t do.”

This lack of human judgment becomes especially risky as new architectures, such as Model Context Protocol (MCP), can expand how agents connect to data, tools, and external systems. By design, MCP enables agents to dynamically discover and interact with new resources, increasing flexibility but also introducing new pathways for unintended access, data exposure, or abuse if not properly governed.

The CISO of a fund administrator highlighted one emerging vector as an example: rogue MCP servers. “Our developers want to move quickly and bring value to the business, but technologies like these can unintentionally expose sensitive data in ways that would never have happened before.”

2. Increased digital complexity and expanded attack surface

AI activity rarely stays contained. A single prompt can trigger a chain of actions across networks, email, cloud infrastructure, SaaS platforms, endpoints, identity systems, and development environments, spanning systems that were never designed to be secured as a single, connected flow. This expands both the scale and complexity of what security teams need to monitor and defend.

Yet no single control has visibility across that entire chain. “You can’t defend effectively what you can’t see,” cautioned the private fund administrator CISO. As AI-driven activity moves fluidly across environments, gaps in coverage become inevitable, creating blind spots that attackers can exploit.

Threat actors are already capitalizing on this lack of visibility. “Threat actors have advanced their use of generative AI to launch more convincing phishing campaigns, automate social engineering, and scale attacks with greater precision down to the individual level,” said the SVP of Technology and Cybersecurity for the real estate investment trust. What was once manual and targeted can now be automated and personalized at scale, making attacks harder to detect and easier to execute.

At the same time, the pace of exploitation is accelerating. As a global CISO operating across 40+ countries described it: “Zero-day vulnerabilities are no longer zero day; it’s minus one day. By the time you get to it and address it, it’s already a problem.” By the time risk is identified, it has often already been realized.

The result is a rapidly expanding and increasingly interconnected attack surface that challenges security teams to maintain visibility, context, and control across AI-driven activity.

3. Shadow AI is already everywhere

76% of organizations now cite shadow AI as a problem, one that is spreading through organizations in ways that are hard to track and even harder to control.

Employees are experimenting with publicly available Gen AI tools. Teams are spinning up low-code automations on their own. SaaS providers are quietly embedding AI into existing products. Developers are plugging AI services directly into workflows, often without pausing to consider what that exposure means.

The result is a lack of visibility into:

  • What AI tools are being used
  • What data those tools can access
  • Where prompts and outputs are going
  • Which AI agents are interacting with enterprise systems

The SVP of Cybersecurity at a real estate investment trust described the shift: “Before, I was worried about someone sending data erroneously to their personal email. Now we have all these agents online that people are utilizing, and we’re looking at those vectors as well.” For security teams, this means operating without a complete view of how AI is being used, what it can access, and where risk may already be emerging.

4. Built-in guardrails are not enough

Organizations often assume that native AI guardrails or provider-level controls are sufficient to manage AI risk. But securing AI requires ongoing visibility, oversight, and governance, not just controls configured at deployment. "It’s a misconception that adopting AI is going to solve all your problems,” warns a global financial services CISO.

Security leaders are increasingly recognizing the limitations of these controls as:

  • Fragmented and difficult to enforce consistently across multiple AI systems, workflows, and environments
  • Ambiguous in terms of accountability due to shared responsibility for AI governance between IT, security, developers, business teams, and third-party providers
  • Limited in end-to-end oversight, leaving gaps that stretch from the initial prompt all the way through to the downstream impact of an agent's actions

Securing AI demands more than simple prompt filtering or static policy enforcement. It requires understanding intent, behavior, and context across both human and AI activity.

The next phase of cybersecurity: securing AI

To safely and responsibly adopt AI at scale, organizations need a new operational model for cybersecurity that’s capable of:

• Understanding AI behavior

• Identifying risk in real time

• Maintaining governance without slowing innovation

The CSO of a $10 billion municipal utility organization described the challenge with precision: “We have to move at the speed of innovation and risk, because both are accelerating faster than ever.”

Embrace AI with confidence with Darktrace / SECURE AI

Darktrace has introduced Darktrace / SECURE AI™, a new product within the Darktrace ActiveAI Security Platform™  ,designed to provide enterprise-wide security for AI by applying industry leading behavioral analysis to how prompts, agents, and AI systems are used.

Darktrace / SECURE AITM delivers real-time visibility and control across Enterprise and SaaS GenAI prompts, AI agent identities, development and production environments, and Shadow AI - detecting even subtle misuse, misconfiguration, and drift that traditional, rule-based controls simply do not understand. By interpreting context and intent across humans and machines, Darktrace enables organizations to adopt AI at scale without introducing unmanaged risk

What makes this possible is Darktrace’s decade-long maturity and expertise in behavioral understanding and AI-native cybersecurity. Achieved with Self-Learning AI that has been proven across more than 10,000 organizations, Darktrace understands what “normal” looks like for a business, across its users, systems, and now AI, so that meaningful deviations can be detected and acted on before they become incidents.

With one CISO describing Darktrace’s Self-Learning AI as “a leap forward compared to other tools” and another as a “force multiplier,” the technology can interpret ambiguous interactions, understand how access accumulates over time, and recognize when behavior, human or machine, begins to drift.

“Strategically, we’re looking to gain more visibility into how AI is operating across the environment and achieve greater control over what AI should be allowed to access and do,” shared the CISO at a private fund administrator.  

“What I’ve seen from Darktrace / SECURE AI is extremely promising. I have tremendous confidence in Darktrace’s vision for where this is headed and its ability to execute on this new solution.”

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