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December 14, 2021

Log4Shell Vulnerability Detection & Response With Darktrace

Learn how Darktrace's AI detects and responds to Log4Shell attacks. Explore real-world examples and see how Darktrace identified and mitigated cyber threats.
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
Max Heinemeyer
Global Field CISO
Written by
Justin Fier
SVP, Red Team Operations
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14
Dec 2021

In this blog, we’ll take a look at the Log4Shell vulnerability and provide real-world examples of how Darktrace detects and responds to attacks attempting to leverage Log4Shell in the wild.

Log4Shell is now the well-known name for CVE-2021-44228 – a severity 10 zero-day exploiting a well-known Java logging utility known as Log4j. Vulnerabilities are discovered daily, and some are more severe than others, but the fact that this open source utility is nested into nearly everything, including the Mars Ingenuity drone, makes this that much more menacing. Details and further updates about Log4Shell are still emerging at the publication date of this blog.

Typically, zero-days with the power to reach this many systems are held close to the chest and only used by nation states for high value targets or operations. This one, however, was first discovered being used against Minecraft gaming servers, shared in chat amongst gamers.

While all steps should be taken to deploy mitigations to the Log4Shell vulnerability, these can take time. As evidenced here, behavioral detection can be used to look for signs of post-exploitation activity such as scanning, coin mining, lateral movement, and other activities.

Darktrace initially detected the Log4Shell vulnerability targeting one of our customers’ Internet-facing servers, as you will see in detail in an actual anonymized threat investigation below. This was highlighted and reported using Cyber AI Analyst, unpacked here by our SOC team. Please take note that this was using pre-existing algorithms without retraining classifiers or adjusting response mechanisms in reaction to Log4Shell cyber-attacks.

How Log4Shell works

The vulnerability works by taking advantage of improper input validation by the Java Naming and Directory Interface (JNDI). A command comes in from an HTTP user-agent, encrypted HTTPS connection, or even a chat room message, and the JNDI sends that to the target system in which it gets executed. Most libraries and applications have checks and protections in place to prevent this from happening, but as seen here, they get missed at times.

Various threat actors have started to leverage the vulnerability in attacks, ranging from indiscriminate crypto-mining campaigns to targeted, more sophisticated attacks.

Real-world example 1: Log4Shell exploited on CVE ID release date

Darktrace saw this first example on December 10, the same day the CVE ID was released. We often see publicly documented vulnerabilities being weaponized within days by threat actors. This attack hit an Internet-facing device in an organization’s demilitarized zone (DMZ). Darktrace had automatically classified the server as an Internet-facing device based on its behavior.

The organization had deployed Darktrace in the on-prem network as one of many coverage areas that include cloud, email and SaaS. In this deployment, Darktrace had good visibility of the DMZ traffic. Antigena was not active in this environment, and Darktrace was in detection-mode only. Despite this fact, the client in question was able to identify and remediate this incident within hours of the initial alert. The attack was automated and had the goal of deploying a crypto-miner known as Kinsing.

In this attack, the attacker made it harder to detect the compromise by encrypting the initial command injection using HTTPS over the more common HTTP seen in the wild. Despite this method being able to bypass traditional rules and signature-based systems Darktrace was able to spot multiple unusual behaviors seconds after the initial connection.

Initial compromise details

Through peer analysis Darktrace had previously learned what this specific DMZ device and its peer group normally do in the environment. During the initial exploitation, Darktrace detected various subtle anomalies that taken together made the attack obvious.

  1. 15:45:32 Inbound HTTPS connection to DMZ server from rare Russian IP — 45.155.205[.]233;
  2. 15:45:38 DMZ server makes new outbound connection to the same rare Russian IP using two new user agents: Java user agent and curl over a port that is unusual to serve HTTP compared to previous behavior;
  3. 15:45:39 DMZ server uses an HTTP connection with another new curl user agent (‘curl/7.47.0’) to the same Russian IP. The URI contains reconnaissance information from the DMZ server.

All this activity was detected not because Darktrace had seen it before, but because it strongly deviated from the regular ‘pattern of life’ for this and similar servers in this specific organization.

This server never reached out to rare IP addresses on the Internet, using user agents it never used before, over protocol and port combinations it never uses. Every point-in-time anomaly itself may have presented slightly unusual behavior – but taken together and analyzed in the context of this particular device and environment, the detections clearly tell a bigger story of an ongoing cyber-attack.

Darktrace detected this activity with various models, for example:

  • Anomalous Connection / New User Agent to IP Without Hostname
  • Anomalous Connection / Callback on Web Facing Device

Further tooling and crypto-miner download

Less than 90 minutes after the initial compromise, the infected server started downloading malicious scripts and executables from a rare Ukrainian IP 80.71.158[.]12.

The following payloads were subsequently downloaded from the Ukrainian IP in order:

  • hXXp://80.71.158[.]12//lh.sh
  • hXXp://80.71.158[.]12/Expl[REDACTED].class
  • hXXp://80.71.158[.]12/kinsing
  • hXXp://80.71.158[.]12//libsystem.so
  • hXXp://80.71.158[.]12/Expl[REDACTED].class

Using no threat intelligence or detections based on static indicators of compromise (IoC) such as IPs, domain names or file hashes, Darktrace detected this next step in the attack in real time.

The DMZ server in question never communicated with this Ukrainian IP address in the past over these uncommon ports. It is also highly unusual for this device and its peers to download scripts or executable files from this type of external destination, in this fashion. Shortly after these downloads, the DMZ server started to conduct crypto-mining.

Darktrace detected this activity with various models, for example:

  • Anomalous File / Script from Rare External Location
  • Anomalous File / Internet Facing System File Download
  • Device / Internet Facing System with High Priority Alert

Surfacing the Log4Shell incident immediately

In addition to Darktrace detecting each individual step of this attack in real time, Darktrace Cyber AI Analyst also surfaced the overarching security incident, containing a cohesive narrative for the overall attack, as the most high-priority incident within a week’s worth of incidents and alerts in Darktrace. This means that this incident was the most obvious and immediate item highlighted to human security teams as it unfolded. Darktrace’s Cyber AI Analyst found each stage of this incident and asked the very questions you would expect of your human SOC analysts. From the natural language report generated by the Cyber AI Analyst, a summary of each stage of the incident followed by the vital data points human analysts need, is presented in an easy to digest format. Each tab signifies a different part of this incident outlining the actual steps taken during each investigative process.

The result of this is no sifting through low-level alerts, no need to triage point-in-time detections, no putting the detections into a bigger incident context, no need to write a report. All of this was automatically completed by the AI Analyst saving human teams valuable time.

The below incident report was automatically created and could be downloaded as a PDF in various languages.

Figure 1: Darktrace’s Cyber AI Analyst surfaces multiple stages of the attack and explains its investigation process

Real-world example 2: Responding to a different attack using Log4Shell

On December 12, another organization’s Internet-facing server was initially compromised via Log4Shell. While the details of the compromise are different – other IoCs are involved – Darktrace detected and surfaced the attack similarly to the first example.

Interestingly, this organization had Darktrace Antigena in autonomous mode on their server, meaning the AI can take autonomous actions to respond to ongoing cyber-attacks. These responses can be delivered via a variety of mechanisms, for instance, API interactions with firewalls, other security tools, or native responses issued by Darktrace.

In this attack the rare external IP 164.52.212[.]196 was used for command and control (C2) communication and malware delivery, using HTTP over port 88, which was highly unusual for this device, peer group and organization.

Antigena reacted in real time in this organization, based on the specific context of the attack, without any human in the loop. Antigena interacted with the organization’s firewall in this case to block any connections to or from the malicious IP address – in this case 164.52.212[.]196 – over port 88 for 2 hours with the option of escalating the block and duration if the attack appears to persist. This is seen in the illustration below:

Figure 2: Antigena’s response

Here comes the trick: thanks to Self-Learning AI, Darktrace knows exactly what the Internet-facing server usually does and does not do, down to each individual data point. Based on the various anomalies, Darktrace is certain that this represents a major cyber-attack.

Antigena now steps in and enforces the regular pattern of life for this server in the DMZ. This means the server can continue doing whatever it normally does – but all the highly anomalous actions are interrupted as they occur in real time, such as speaking to a rare external IP over port 88 serving HTTP to download executables.

Of course the human can change or lift the block at any given time. Antigena can also be configured to be in human confirmation mode, having the human in the loop at certain times during the day (e.g. office hours) or at all times, depending on an organization’s needs and requirements.

Conclusion

This blog illustrates further aspects of cyber-attacks leveraging the Log4Shell vulnerability. It also demonstrates how Darktrace detects and responds to zero-day attacks if Darktrace has visibility of the attacked entities.

While Log4Shell is dominating the IT and security news, similar vulnerabilities have surfaced in the past and will appear in the future. We’ve spoken about our approach to detecting and responding to similar vulnerabilities and surrounding cyber-attacks before, for instance:

As always, companies should aim for a defense-in-depth strategy combining preventative security controls with detection and response mechanisms, as well as strong patch management.

Thanks to Brianna Leddy (Darktrace’s Director of Analysis) for her insights on the above threat find.

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
Max Heinemeyer
Global Field CISO
Written by
Justin Fier
SVP, Red Team Operations

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March 26, 2026

Phantom Footprints: Tracking GhostSocks Malware

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Why are attackers using residential proxies?

In today's threat landscape, blending in to normal activity is the key to success for attackers and the growing reliance on residential proxies shows a significant shift in how threat actors are attempting to bypass IP detection tools.

The increasing dependency on residential proxies has exposed how prevalent proxy services are and how reliant a diverse range of threat actors are on them. From cybercriminal groups to state‑sponsored actors, the need to bypass IP detection tools is fundamental to the success of these groups. One malware that has quietly become notorious for its ability to avoid anomaly detection is GhostSocks, a malware that turns compromised devices into residential proxies.

What is GhostSocks?

Originally marketed on the Russian underground forum xss[.]is as a Malware‑as‑a‑Service (MaaS), GhostSocks enables threat actors to turn compromised devices into residential proxies, leveraging the victim's internet bandwidth to route malicious traffic through it.

How does Ghostsocks malware work? 

The malware offers the threat actor a “clean” IP address, making it look like it is coming from a household user. This enables the bypassing of geographic restrictions and IP detection tools, a perfect tool for avoiding anomaly detection. It wasn’t until 2024, when a partnership was announced with the infamous information stealer Lumma Stealer, that GhostSocks surged into widespread adoption and alluded to who may be the author of the proxy malware.

Written in GoLang, GhostSocks utilizes the SOCKS5 proxy protocol, creating a SOCKS5 connection on infected devices. It uses a relay‑based C2 implementation, where an intermediary server sits in between the real command-and-control (C2) server and the infected device.

How does Ghostsocks malware evade detection?

To further increase evasion, the Ghostsocks malware wraps its SOCKS5 tunnels in TLS encryption, allowing its malicious traffic to blend into normal network traffic.

Early variants of GhostSocks do not implement a persistence mechanism; however, later versions achieve persistence via registry run keys, ensuring sustained proxy operational time [1].

While proxying is its primary purpose, GhostSocks also incorporates backdoor functionality, enabling malicious actors to run arbitrary commands and download and deploy additional malicious payloads. This was evident with the well‑known ransomware group Black Basta, which reportedly used GhostSocks as a way of maintaining long‑term access to victims’ networks [1].

Darktrace’s detection of GhostSocks Malware

Darktrace observed a steady increase in GhostSocks activity across its customer base from late 2025, with its Threat Research team identifying multiple incidents involving the malware. In one notable case from December 2025, Darktrace detected GhostSocks operating alongside Lumma Stealer, reinforcing that the partnership between Lumma and GhostSocks remains active despite recent attempts to disrupt Lumma’s infrastructure.

Darktrace’s first detection of GhostSocks‑related activity came when a device on the network of a customer in the education sector began making connections to an endpoint with a suspicious self‑signed certificate that had never been seen on the network before.

The endpoint in question, 159.89.46[.]92 with the hostname retreaw[.]click, has been flagged by multiple open‑source intelligence (OSINT) sources as being associated with Lumma Stealer’s C2 infrastructure [2], indicating its likely role in the delivery of malicious payloads.

Darktrace’s detection of suspicious SSL connections to retreaw[.]click, indicating an attempted link to Lumma C2 infrastructure.
Figure 1: Darktrace’s detection of suspicious SSL connections to retreaw[.]click, indicating an attempted link to Lumma C2 infrastructure.

Less than two minutes later, Darktrace observed the same device downloading the executable (.exe) file “Renewable.exe” from the IP 86.54.24[.]29, which Darktrace recognized as 100% rare for this network.

Darktrace’s detection of a device downloading the unusual executable file “Renewable.exe”.
Figure 2: Darktrace’s detection of a device downloading the unusual executable file “Renewable.exe”.

Both the file MD5 hash and the executable itself have been identified by multiple OSINT vendors as being associated with the GhostSocks malware [3], with the executable likely the backdoor component of the GhostSocks malware, facilitating the distribution of additional malicious payloads [4].

Following this detection, Darktrace’s Autonomous Response capability recommended a blocking action for the device in an early attempt to stop the malicious file download. In this instance, Darktrace was configured in Human Confirmation Mode, meaning the customer’s security team was required to manually apply any mitigative response actions. Had Autonomous Response been fully enabled at the time of the attack, the connections to 86.54.24[.]29 would have been blocked, rendering the malware ineffective at reaching its C2 infrastructure and halting any further malicious communication.

 Darktrace’s Autonomous Response capability suggesting blocking the suspicious connections to the unusual endpoint from which the malicious executable was downloaded.
Figure 3: Darktrace’s Autonomous Response capability suggesting blocking the suspicious connections to the unusual endpoint from which the malicious executable was downloaded.

As the attack was able to progress, two days later the device was detected downloading additional payloads from the endpoint www.lbfs[.]site (23.106.58[.]48), including “Setup.exe”, “,.exe”, and “/vp6c63yoz.exe”.

Darktrace’s detection of a malicious payload being downloaded from the endpoint www.lbfs[.]site.
Figure 4: Darktrace’s detection of a malicious payload being downloaded from the endpoint www.lbfs[.]site.

Once again, Darktrace recognized the anomalous nature of these downloads and suggested that a “group pattern of life” be enforced on the offending device in an attempt to contain the activity. By enforcing a pattern of life on a device, Darktrace restricts its activity to connections and behaviors similar to those performed by peer devices within the same group, while still allowing it to carry out its expected activity, effectively preventing deviations indicative of compromise while minimizing disruption. As mentioned earlier, these mitigative actions required manual implementation, so the activity was able to continue. Darktrace proceeded to suggest further actions to contain subsequent malicious downloads, including an attempt to block all outbound traffic to stop the attack from progressing.

An overview of download activity and the Autonomous Response actions recommended by Darktrace to block the downloads.
Figure 5: An overview of download activity and the Autonomous Response actions recommended by Darktrace to block the downloads.

Around the same time, a third executable download was detected, this time from the hostname hxxp[://]d2ihv8ymzp14lr.cloudfront.net/2021-08-19/udppump[.]exe, along with the file “udppump.exe”.While GhostSocks may have been present only to facilitate the delivery of additional payloads, there is no indication that these CloudFront endpoints or files are functionally linked to GhostSocks. Rather, the evidence points to broader malicious file‑download activity.

Shortly after the multiple executable files had been downloaded, Darktrace observed the device initiating a series of repeated successful connections to several rare external endpoints, behavior consistent with early-stage C2 beaconing activity.

Cyber AI Analyst’s investigation

Darktrace’s detection of additional malicious file downloads from malicious CloudFront endpoints.
Figure 7: Darktrace’s detection of additional malicious file downloads from malicious CloudFront endpoints.

Throughout the course of this attack, Darktrace’s Cyber AI Analyst carried out its own autonomous investigation, piecing together seemingly separate events into one wider incident encompassing the first suspicious downloads beginning on December 4, the unusual connectivity to many suspicious IPs that followed, and the successful beaconing activity observed two days later. By analyzing these events in real-time and viewing them as part of the bigger picture, Cyber AI Analyst was able to construct an in‑depth breakdown of the attack to aid the customer’s investigation and remediation efforts.

Cyber AI Analyst investigation detailing the sequence of events on the compromised device, highlighting its extensive connectivity to rare endpoints, the related malicious file‑download activity, and finally the emergence of C2 beaconing behavior.
Figure 8: Cyber AI Analyst investigation detailing the sequence of events on the compromised device, highlighting its extensive connectivity to rare endpoints, the related malicious file‑download activity, and finally the emergence of C2 beaconing behavior.

Conclusion

The versatility offered by GhostSocks is far from new, but its ability to convert compromised devices into residential proxy nodes, while enabling long‑term, covert network access—illustrates how threat actors continue to maximise the value of their victims’ infrastructure. Its growing popularity, coupled with its ongoing partnership with Lumma, demonstrates that infrastructure takedowns alone are insufficient; as long as threat actors remain committed to maintaining anonymity and can rapidly rebuild their ecosystems, related malware activity is likely to persist in some form.

Credit to Isabel Evans (Cyber Analyst), Gernice Lee (Associate Principal Analyst & Regional Consultancy Lead – APJ)
Edited by Ryan Traill (Content Manager)

Appendices

References

1.    https://bloo.io/research/malware/ghostsocks

2.    https://www.virustotal.com/gui/domain/retreaw.click/community

3.    https://synthient.com/blog/ghostsocks-from-initial-access-to-residential-proxy

4.    https://www.joesandbox.com/analysis/1810568/0/html

5. https://www.virustotal.com/gui/url/fab6525bf6e77249b74736cb74501a9491109dc7950688b3ae898354eb920413

Darktrace Model Detections

Real-time Detection Models

Anomalous Connection / Suspicious Self-Signed SSL

Anomalous Connection / Rare External SSL Self-Signed

Anomalous File / EXE from Rare External Location

Anomalous File / Multiple EXE from Rare External Locations

Compromise / Possible Fast Flux C2 Activity

Compromise / Large Number of Suspicious Successful Connections

Compromise / Large Number of Suspicious Failed Connections

Compromise / Sustained SSL or HTTP Increase

Autonomous Response Models

Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block

Antigena / Network / External Threat / Antigena Suspicious File Block

Antigena / Network / Significant Anomaly / Antigena Controlled and Model Alert

Antigena / Network / External Threat / Antigena File then New Outbound Block

Antigena / Network / Significant Anomaly / Antigena Alerts Over Time Block

Antigena / Network / External Threat / Antigena Suspicious Activity Block

MITRE ATT&CK Mapping

Tactic – Technique – Sub-Technique

Resource Development – T1588 - Malware

Initial Access - T1189 - Drive-by Compromise

Persistence – T1112 – Modify Registry

Command and Control – T1071 – Application Layer Protocol

Command and Control – T1095 – Non-application Layer Protocol

Command and Control – T1071 – Web Protocols

Command and Control – T1571 – Non-Standard Port

Command and Control – T1102 – One-Way Communication

List of Indicators of Compromise (IoCs)

86.54.24[.]29 - IP - Likely GhostSocks C2

http[://]86.54.24[.]29/Renewable[.]exe - Hostname - GhostSocks Distribution Endpoint

http[://]d2ihv8ymzp14lr.cloudfront[.]net/2021-08-19/udppump[.]exe - CDN - Payload Distribution Endpoint

www.lbfs[.]site - Hostname - Likely C2 Endpoint

retreaw[.]click - Hostname - Lumma C2 Endpoint

alltipi[.]com - Hostname - Possible C2 Endpoint

w2.bruggebogeyed[.]site - Hostname - Possible C2 Endpoint

9b90c62299d4bed2e0752e2e1fc777ac50308534 - SHA1 file hash – Likely GhostSocks payload

3d9d7a7905e46a3e39a45405cb010c1baa735f9e - SHA1 file hash - Likely follow-up payload

10f928e00a1ed0181992a1e4771673566a02f4e3 - SHA1 file hash - Likely follow-up payload

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Gernice Lee
Associate Principal Analyst & Regional Consultancy Lead

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March 27, 2026

State of AI Cybersecurity 2026: 92% of security professionals concerned about the impact of AI agents

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The findings in this blog are taken from Darktrace's annual State of AI Cybersecurity Report 2026.

AI is already embedded in day-to-day enterprise activity, with 78% of participants in one recent survey reporting that their organizations are using generative AI in at least one business function. Generative AI now acts as an always-on assistant, researcher, creator, and coach across an expanding array of departments and functions. Autonomous agents are performing multi-step operational workflows from end to end. AI features have been layered on top of every SaaS application. And vibe coding is making it possible for employees without deep technical expertise to build their own AI-powered automations.

According to Gartner, more than 80% of enterprises will have deployed GenAI models, applications, or APIs in production environments by the end of this year, up from less than 5% in 2023. Companies report a 130% increase in spending on AI over the same period, with 72% of business leaders using AI tools at least weekly. The outsized efficiency and productivity gains that were once a future vision are quickly becoming everyday reality.

AI is currently driving business growth and innovation, and organizations risk falling behind peers if they don’t keep up with the pace of adoption, but it is also quietly expanding the enterprise attack surface. The modern CISO is challenged to both enable innovation and protect the business from these emerging threats.

AI agents introduce new risks and vulnerabilities

AI agents are playing growing roles in enterprise production environments. In many cases, these agents act with broad permissions across multiple software systems and platforms. This means they’re granted far-reaching access – to sensitive data, business-critical applications, tokens and APIs, and IT and security tools. With this access comes risk for security leaders – 92% are concerned about the use of AI agents across the workforce and their impact on security.

These agents must be governed as identities, with least-privilege access and ongoing monitoring. They can’t be thought of as invisible aspects of the application estate. Understanding how AI agents behave, and how to manage their permissions, control their behavior, and limit their data access will be a top security priority throughout 2026.

Generative AI prompts: The next frontier

Prompts are how users – both human and agentic – interact with AI systems, and they’re where natural language gets translated into model behavior. Natural language is infinite in its potential combinations and permutations, making this aspect of the attack surface open-ended and far more complex than traditional CVEs. With carefully crafted prompts, bad actors may be able to coax models into disclosing sensitive data, bypassing guardrails, or initiating undesirable actions.

Among security leaders, the biggest worries about AI usage in their environments all involve ways that systems might be manipulated to bypass traditional controls.

  • 61% are most concerned about the exposure of sensitive data
  • 56% are most concerned about potential data security and policy violations
  • 51% are most concerned about the misuse or abuse of AI tools

The more employees rely on AI in their day-to-day workflows, the more critical it becomes for security teams to understand how prompt behavior determines model behavior – and where that behavior could go wrong.

What does “securing AI” mean in practice?

AI adoption opens new security risks that blur the boundaries between traditional security disciplines. A single malicious interaction with an AI model could involve identity misuse, sensitive data exposure, application logic abuse, and supply chain risk – all within a single workflow. Protecting this dynamic and rapidly evolving attack surface requires an approach that spans identity security, cloud security, application security, data security, software development security, and more.

The task for security leaders is to implement the tools, policies, and frameworks to mitigate these novel, expansive, and cross-disciplinary risks.

However, within most enterprises, AI policy creation remains in its infancy. Just 37% of security leaders report that their organization has a formal AI policy, representing a small but worrisome decrease from last year. Conversations about AI abound: in 52% of organizations, there’s discussion about an AI policy. Still, talk is cheap, and leaders will need to take action if they’re to successfully enable secure AI innovation.

To govern and protect their AI systems, organizations must take a multi-pronged approach. This requires building out policies, but it also demands that they are able to:

  • Monitor the prompts driving GenAI assistants and agents in real time. Organizations must be able to inspect prompts, sessions, and responses across enterprise GenAI tools, low- and high-code environments, and SaaS and SASE so that they can detect clever conversational prompt attacks and malicious chaining.
  • Secure all business AI agent identities. Security teams need to identify all the agents acting within their environment and supply chain, map their connections and interactions via MCP and services like Amazon S3, and audit their behavior across the cloud, SaaS environments, and on the network and endpoint devices.
  • Maintain centralized, comprehensive visibility. Understanding intent, assessing risks, and enforcing policies all require that security teams have a single view that spans AI interactions across the entire business.
  • Discover and control shadow AI. Teams need to be able to identify unsanctioned AI activities, distinguish the misuse of legitimate tools from their appropriate use, and apply policies to protect data, while guiding users towards approved solutions.

Scaling AI safely and responsibly

The approach that most cybersecurity vendors have taken – using historical patterns to predict future threats – doesn’t work well for AI systems. Because AI changes its behavior in response to the information it encounters while taking action, previous patterns don’t indicate what it will do next. Looking at past attacks can’t tell you how complex models will behave in your individual business.

Securing AI requires interpreting ambiguous interactions, uncovering subtleties that reveal intent within extended conversations, understanding how access accumulates over time, and recognizing when behavior – both human and machine – begins to drift towards areas of risk. To do this, you need to understand what “normal” looks like in each unique organization: how users, systems, applications, and AI agents behave, how they communicate, and how data flows between them.

Darktrace has spent more than a decade designing AI-powered solutions that can understand and adapt to evolving behavior in complex environments. This technology learns directly from the environment it protects, identifying malicious actions that deviate from normal operations, so that it can stop AI-related threats on the very first encounter.

As AI adoption reshapes enterprise operations, humans and machines will collaborate more and more often. This collaboration might dramatically expand the attack surface, but it also has the potential to be a force multiplier for defenders.

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

Learn more about securing AI in your enterprise.

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