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August 2, 2024

Safelink Smuggling: Enhancing Resilience Against Malicious Links

Gain insights into safelink smuggling tactics and learn strategies to protect your organization from the dangers posed by malicious links.
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
Carlos Gray
Senior Product Marketing Manager, Email
Written by
Stephen Pickman
Senior Vice President, Engineering
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02
Aug 2024

Darktrace security members and researchers have recently seen a rise in what we are calling Safelink Smuggling. Safelinks are URLs rewritten by security solutions to enable additional analysis when the URL is clicked. Once analyzed, they may prompt a user, redirect the browser back to the original URL, or block further access if deemed necessary.

What is Safelink Smuggling?

Safelink Smuggling is a technique that involves an attacker purposely getting their malicious payload rewritten by a security solution’s Safelink capability to then propagate the rewritten URL to others. This technique is a way for attackers to not only avoid detection by traditional email security and other solutions, but also to instill mistrust in all email security solutions. As a result, Safelinks from a range of popular email security providers are often seen in phishing or supply chain attacks. In fact, Darktrace has observed over 300,000 cases of Safelinks being included in unexpected and suspicious contexts over the last 3 months.

How does Safelink Smuggling work?

Safelink Smuggling has two key stages: Getting a malicious link rewritten by an email security solution, then propagating that rewritten link to other victims.

Step one:

Obfuscated a malicious payload through a Safelink capability rewriting the link; Darktrace has seen this attempted through two methods – Compromised Account or Reply-Chain.

  • Method 1: Compromised Account

If an attacker can gain access to a compromised account – whether that’s through brute force, malware or credential theft – they can infiltrate it with malicious links, and then exfiltrate the Safelinks created as the email passes through security filtering. In other words, attackers will send a malicious payload to the compromised inbox, with the intent that the malicious URL gets rewritten. Unlike a normal phishing email where the threat actor wants to avoid having their email blocked, in this case the objective is for the email to get through to the inbox with the link rewritten. As observed by Darktrace, attackers often send the link in isolation as any additional components (i.e., body text or other content in the email) could cause a more severe action such as the email security solution holding the message.

  • Method 2: Reply-Chain

With this method, the attacker sends a malicious link to an email security vendor’s customer in an attempt to solicit a reply from an internal user. This allows them to grab the re-written URL within the reply chain. However, this is a risky tactic which can fail at several points. The attacker has to be confident the initial email won't be blocked outright; they also risk alerting security vendors to the address and the URL intended to be used for the main campaign. They also must be confident that the checks made when the re-written URL is clicked will not lead to a block at the final destination.
Regardless of the method used, the end result will appear as follows:

For example, the original malicious URL may look like this,

faceldu[.]org/Invoice112.zip

(negative surface indicators: recently registered domain, file extension)

And after being rewritten,

securityvevndor[.]com/safe?q=aNDF80dfaAkAH930adbd

(positive surface indicators: established domain, positive reputation, associated with safe content)

Step Two:

Now that the attacker has access to a malicious URL that has been obfuscated by a safe rewrite, attackers can forward or craft an email leveraging that same link. In fact, we have even seen multiple layers of Safelink Smuggling being used to mask a payload further.

The Challenge of Link Rewriting

Traditional email security solutions rewrite all links sent to an organization, but there is an inherent risk to this methodology. Rewriting every link, whether harmless or harmful, leads employees to lose context and creates a false sense of security when interacting with rewritten links in emails. Furthermore, it provides attackers with many opportunities to exploit Safelinks. As demonstrated in Method 2 above, if an email security solution does not rewrite every link, executing such attacks would be significantly more challenging.

Traditionally, rewriting every link made sense from a security perspective, as it allowed servers to thoroughly analyze links for known attack patterns and signatures. However, this approach relies on identifying previously recognized threats. Conversely, Darktrace / EMAIL gathers sufficient information about a link without needing to rewrite it, by analyzing the context and content of the email and the link itself.

In fact, Darktrace is the pioneer in applying selective rewriting to URLs based on suspicious properties or context, a method that other solutions have since adopted. While traditional solutions rewrite links to assess them only after they are clicked, Darktrace / EMAIL takes immediate action to neutralize threats before they reach the inbox.

Darktrace achieves high success rates in detecting malicious links and emails on the first encounter using Self-Learning AI. By understanding 'normal' behavior in email communications, Darktrace identifies subtle deviations indicative of cyber threats and selectively rewrites only those links deemed suspicious, ensuring a targeted, proportionate, and non-disruptive response.

Why do traditional email security solutions miss Safelink attacks?

Traditional security solutions that focus on learning attack patterns will miss Safelink threats as they are often utilized in attacks that have a variety of layers which help the email seem legitimate. Leveraging all the classic techniques seen in a supply chain attack to disguise the sender's intent, taking advantage of the users' inherent trust in familiar sources, the user is more likely to lower their defenses.

For more information: https://darktrace.com/products/email/use-cases/supply-chain-attack

In terms of the URL, if the payload is malicious, why is it difficult for email security solutions to catch it? Primarily, other security vendors will focus on the payload in isolation, attempting to find known attack patterns or signatures such as a domain name or IP with a bad reputation. Unfortunately, with this technique, if the URL has a legitimate domain, it will return a clean track record. Common obfuscation techniques such as captchas, short-links, and click throughs can all be deployed to add layers of complexity to the analysis.

Safelink Smuggling relies heavily on link redirects, which means that web analysis tools will falter as they will only analyze the first redirect. Consequently, when more in-depth analysis on the link itself is performed, the first place the URL takes the user is not the malicious site but rather the default on-click analysis of the vendor in question. Therefore, any traditional browser or link analysis will also return a negative result.

Finally, the context itself is important. In contrast to traditional email security solutions, Darktrace / EMAIL asks who, what, when, where, and why for every single email, and compares it to the pattern of life of both the internal recipient and the external sender, rather than attempting to match patterns with historical threat data. When analyzing an email from an inbound perspective, Darktrace reveals potential deviations from normal, that, when considered sufficiently anomalous, will result in taking a proportional action to the threat assessed.

To illustrate the above, let’s take a look at an example email that Darktrace recently caught.

The following is an email a Darktrace customer received, which Darktrace / EMAIL held before it reached the inbox. In this case, the smuggled Safelink was further obfuscated behind a QR Code. The accompanying document also presented some anomalies in terms of its intent, perceived as a potential social engineering attempt. Finally, the lack of association and low mailing history meant there was no prior context for this email.  

Example of a Safelink Smuggling attack using a popular email security solution’s safelink.
Fig 1: Example of a Safelink Smuggling attack using a popular email security solution’s safelink.

How to mitigate against Safelink Smuggling?

It's difficult for email security vendors to do anything about their links being reused, and reuse should almost be expected by popular operators in the email security space. Therefore, the presence of links from a vendor’s domain in a suspicious email communication rarely indicates a compromise of the link rewrite infrastructure or a compromise of the third-party vendor.

Email security vendors can improve their defense-in-depth, especially around their email provider accounts to avoid Method 1 (Compromised Account attacks) and become more selective with their rewrites to curtail Method 2 (Reply Chain attacks).

Primary protection against Safelink Smuggling should be offered by the email security vendor responsible for inbound email analysis. They need to ensure that techniques such as Safelink Smuggling are not evaded by their detection mechanisms.

Darktrace has long been working on the betterment of security within the email community and innovating our link analysis infrastructure to mitigate against this attack methodology (read more about our major update in 6.2 here), regardless of whether the receiving organization are Darktrace customers.

How does Darktrace deal with Safelink Smuggling today?

Darktrace has been dealing with Safelink Smuggling since launch and has a standardized recommendation for customers who are looking to defend against this threat.

Customers want to avoid being 1) the propagators of this threat and potentially damaging their brand reputation, and 2) being victims of the supply chain attack thereafter.

The principal recommendation to protect customer accounts and consequently their brands is to ensure defense-in-depth. As accounts establish themselves as the crown jewels of any modern enterprise, organizations should vigilantly monitor their account activity with the same rigor they would analyze their network activity. Whether that is through the base account takeover protection offered by Darktrace / EMAIL, or the expanded defense offered by Darktrace / IDENTITY, it is crucial that the accounts themselves have a robust security solution in place.

Secondly, to avoid falling victim to the supply chain attack that leverages a third-party vendor’s link rewrite, it is imperative to use a solution that does not rely on static threat intelligence and link reputation analysis. Rather than chasing attackers by updating rules and signatures, Darktrace leverages Self-Learning AI to learn the communication patterns of both internal and external messages to reveal deviations in both content and context.

Finally, for those customers that already leverage Darktrace / EMAIL we recommend ensuring that lock links are enabled, and that the default warning page is displayed every time a link is rewritten, no matter the perceived severity of the link. This will allow any potential user that clicks on a rewritten Darktrace / EMAIL link to be alerted to the potential nature of the site they are trying to access.

Safelink smuggling example caught by Darktrace

While most cases involve other vendors, analysts recently saw a case where Darktrace's own links were used in this type of attack. A small number of links were leveraged in a campaign targeting both Darktrace and non-Darktrace customers alike. Thankfully, these attempts were all appropriately actioned by those customers that had Darktrace / EMAIL deployed.

In the example below, you will see how Darktrace Cyber AI Analyst describes the example at hand under the Anomaly Indicators section.

Example of Safelink Smuggling attack on Darktrace using the Darktrace Safelink Infrastructure.
Fig 2: Example of Safelink Smuggling attack on Darktrace using the Darktrace Safelink Infrastructure.

First, the display name mismatch can be interpreted as an indicator of social engineering, attempting to deceive the recipient with an IT policy change.

Second, the link itself, which in this case is a hidden redirect to an unusual host for this environment.

Finally, there is a suspected account takeover due to the origin of the email being a long-standing, validated domain that contains a wide variety of suspicious elements.

Darktrace / EMAIL would have held this email from being delivered.

Conclusion

By investigating Safelink Smuggling, Darktrace wants to shine a light on the technique for security teams and help raise awareness of how it can be used to dupe users into lowering their defenses. Challenge your email security vendor on how it deals with link analysis, particularly from trusted senders and applications.

Interested in Darktrace’s approach to defense-in-depth? Check out Darktrace / EMAIL

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
Carlos Gray
Senior Product Marketing Manager, Email
Written by
Stephen Pickman
Senior Vice President, Engineering

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June 24, 2026

From Click to Command: Behavioral Detection of AppleScript-Led MacOS Intrusions

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Introduction

Darktrace’s Threat Research team is publishing this analysis to help defenders understand an active pattern of macOS tradecraft observed in multiple customer environments. This post summarizes the behaviors observed, how they were assessed, and what defenders can do now.

Across multiple environments, Darktrace observed a consistent MacOS intrusion pattern beginning with ClickFix-style user-assisted “update” execution and transitioning into AppleScript-driven post-compromise activity and sustained outbound signaling.

While individual indicators were low-confidence, the repeated convergence of weak behavioral signals — including HTTP POST beaconing, rare or IP-only destinations, SSL anomalies, and abnormal client characteristics — provided a defensible indication of command-and-control establishment Darktrace detection and response in these cases was driven by behavior over artifacts. In the highest-confidence instances, automated containment disrupted outbound signaling before sustained tasking could occur.

Background

ClickFix-style activity typically relies on user-assisted execution and plausible “update” pretexting, followed by post-execution use of native tools to keep the footprint light. In MacOS environments, AppleScript and other built-in scripting mechanisms enable flexible post-compromise workflows while minimizing stable file-based indicators.

Following execution, affected devices exhibited a consistent behavioral pattern. AppleScript or equivalent native scripting activity was observed initiating follow-on workflows, after which outbound communications began to establish a structured rhythm.

These communications were characterized by repeated HTTP POST requests to low-prevalence or IP-only endpoints, often combined with unusual SSL properties and client identifiers that diverged from baseline device behavior. Individually, these signals were weak. When correlated across time and devices, they formed a pattern consistent with control establishment rather than benign software activity.

In higher-confidence cases, Autonomous Response actions were able to reduce or halt outbound signaling, interrupting the attacker’s ability to maintain control.

Detection Timeline

In representative cases, the sequence unfolded as follows:

Stage 1 – Initial Execution

Initial activity began with suspicious or masqueraded execution on a MacOS endpoint, consistent with ClickFix-style user deception.

Stage 2 – Post-Execution Scripting

This was followed closely by native scripting activity, most commonly AppleScript, indicating the transition into post-execution workflow.

Stage 3 – Outbound Communications

Outbound communications then emerged, initially sporadic but quickly forming a consistent cadence of HTTP POST requests to rare external endpoints.

Stage 4 – Anomaly Convergence

As activity persisted, additional anomalies became visible — unusual SSL characteristics, abnormal user agents, and connections to infrastructure with no prior network prevalence.

Stage 5 – Autonomous Response

In the most mature stages of the activity, automated containment actions disrupted outbound communications on affected devices, limiting the attacker’s ability to continue tasking while investigations progressed.

Darktrace coverage and detections

The following use-case highlights systems likely affected by malicious macOS intrusion activity linked by Microsoft to the Democratic People’s Republic of Korea (DPRK) [1], with indications of suspicious behavior observed between March 1 and May 3, 2026. The activity overlaps with patterns described in recent reporting on DPRK-nexus MacOS intrusions [1], though attribution confidence in this case remains moderate and based on behavioral alignment rather than solely infrastructure linkage.

Analyst confidence emerged through the correlation of multiple weak signals across time and devices. This included model coverage for rare external communications, sustained beaconing patterns, repeated HTTP POSTs, and anomalous client characteristics. Where enabled, Autonomous Response actions disrupted the most active outbound paths to reduce the attacker’s ability to maintain control while Darktrace’s investigation continued.

Notably, this highly anomalous behavior included:

  • Outbound connections to the rare external endpoint, zoom[.]uswebob[.]us associated with IP address, 148.72.73[.]98 [2][3] over port 443
  • Outbound connections to the rare external endpoint, check02id[.]com associated with IP address, 83.136.210[.]180 [4] over port 7365
  • Outbound connections to the rare external endpoints, 104.145.210[.]107 [5] over port 8443 and 83.136.208[.]48 [6] over port 443
  • Outbound connections to the rare external endpoint, 83.136.208[.]246 [7] over port 6783 with observed URI `/api/daemon` and a PowerShell user agent

Darktrace’s detection initially highlighted a desktop device (running MacOS) engaging in anomalous behavior as early as March 12, 2026. Starting on March 12, the source device triggered a ‘Possible Doppelganger Attack’ alert including connectivity to the hostname "zoom[.]uswebob[.]us · 148.72.73[.]98" over port 443 (TCP, HTTPS, H2). This model highlights a device connecting to a location that is rare but masquerades as legitimate software, such as Zoom in this case, a commonly used technique to blend into expected traffic [2] [3].

 Initial connectivity observed to the rare external hostname, zoom[.]uswebob[.]us · 148.72.73[.]98, over port 443.
Figure 1: Initial connectivity observed to the rare external hostname, zoom[.]uswebob[.]us · 148.72.73[.]98, over port 443.

This was followed roughly seven later by a connection to 104.145.210[.]107 over port 8443, during which approximately 250 KiB of data of inbound data and 30 MiB of outbound data was observed, triggering the ‘Unusual Activity / Unusual External Data to New Endpoint’ in Darktrace.

Quickly after this connection, Darktrace’s Autonomous Response intervened, blocking the device’s access to the unusual external location and halting the data exfiltration attempt.

Figure 2: Darktrace’s detection of unusual data exfiltration, shortly followed by an Autonomous Response action to block it.

The device continued to consistently trigger model alerts relating to unusual external connectivity, including 'Posting HTTP to IP Without Hostname', 'Anomalous Connection / Rare External SSL Self-Signed' alerts, until well after 3 PM that day.

Figure 3: Additional external connectivity to new IP without a hostname, including connectivity to 83.136.208[.]246, alongside an anomalous ‘curl/8.7.1’ user agent and ‘/api/daemon’ URI.
Figure 4: Continued external SSL connectivity to IP 83.136.208[.]48, including connectivity to 83.136.208[.]246, alongside an anomalous ‘curl/8.7.1’ user agent and ‘/api/daemon’ URI.
Figure 5: Continued external HTTP connectivity to hostname, check02id[.]com · 83.136.210[.]180, alongside an anomalous ‘Go-http-client/1,1’ user agent.

From March 13 to March 28, the device continued exhibit unusual connectivity to various endpoints (e.g., 83.136.208[.]48, 83.136.208[.]246, check02id[.]com · 83.136.210[.]180), with the 'Multiple HTTP POSTs to Rare Hostname' model consistently triggering.

Windows OS Case

Pivoting over to an additional device, this time running Windows OS, anomalous behavior was also observed between March 30 and April 20. Notably, on March 30, the device was observed making a large number of suspicious external connection attempts to 83.136.208[.]246 over port 6783, all of which failed.

A further indicator was observed on April 1 with PowerShell connectivity to the same rare endpoint (83.136.208[.]246, port 6783), using the URI '/api/daemon' and the user agent 'Mozilla/5.0 (Windows NT; Windows NT 10.0; fr-FR) WindowsPowerShell/5.1.26100.7920'.  Additional alerts included 'New User Agent to IP Without Hostname' and 'Anomalous Github Download', alongside activity involving the same endpoint.

Figure 6 : ‘Anomalous Powershell to Rare External Destination’ and ‘Github Download’ model alerts. This behavior involved connectivity with the endpoints ‘83.136.208[.]246’ and ‘github[.]com’.

The device continued triggering 'Posting HTTP to IP Without Hostname' & 'PowerShell to External Rare' alerts between April 4 and April 20 across multiple related endpoints (i.e., 83.136.208[.]48, 83.136.208[.]246, check02id[.]com · 83.136.210[.]180).

Darktrace’s Autonomous Response capability was able to block suspicious PowerShell attempts to unusual external locations, as shown below in an example from April 20.

Figure 7:  Autonomous Response intervening to block an unusual PowerShell connection to an external destination.

Cyber AI Analyst investigations

In higher-confidence instances, Darktrace’s Cyber AI Analyst investigations helped connect otherwise separate model alerts into a single incident narrative, highlighting the attacker’s progression from post-execution scripting into sustained outbound signaling. This contextual stitching is particularly valuable in macOS scenarios where static artefacts are limited, and behavioral sequencing defines the intrusion.

Cyber AI Analyst investigations highlighted alerts on March 12, including unusual repeated connections and possible SSL command-and-control (C2) to multiple endpoints:

Figure 8: Cyber AI Analyst investigation linking events into a unified incident.

Autonomous Response

In addition to the containment actions detailed earlier, Autonomous Response implemented multiple additional measures to contain suspicious activity throughout the course of this attack. Whenever unusual external connectivity was detected, Darktrace blocked it, closing down potential C2 channels. Likewise, when data exfiltration attempts were identified, these connections were stopped to prevent the potential loss of sensitive data.

Figure 9: Autonomous Response actions implemented by Darktrace in response to suspicious connectivity in mid-March.

Furthermore, in cases where a device was deemed to have carried out a significant number of anomalous activities, Darktrace enforced a “pattern of life” on the device, preventing it from deviating from its expected behavior while allowing legitimate business operations to continue uninterrupted.

Figure 10: Autonomous Response actions implemented by Darktrace in response to suspicious connectivity in April, including the “Enforce Pattern of Life” action.

Conclusion

macOS intrusion tradecraft continues to shift toward native tooling and lightweight control channels designed to evade signature-led controls.

The repeated convergence of rare destinations, POST-based signaling, and anomalous client behavior — observed across time and across devices — provided sufficient evidence to act early and with confidence.

As macOS tradecraft continues to evolve, the defender advantage increasingly lies not in signatures, but in the ability to reason from behavior.

Credit to Justin Torres (Senior Cyber Analyst), Nathaniel Jones (VP, Security & AI Strategy, FCISO)

Edited by Ryan Traill (Content Manager)

Appendices

Darktrace Model Alert Coverage:

/ NETWORK-based model alerts:

·       Anomalous Connection::Multiple HTTP POSTs to Rare Hostname

·       Anomalous Connection::Rare External SSL Self-Signed

·       Anomalous Connection::Powershell to Rare External

·       Anomalous Connection::New User Agent to IP Without Hostname

·       Anomalous Connection::Posting HTTP to IP Without Hostname

·       Compromise::Fast Beaconing to DGA

·       Compromise::Large Number of Suspicious Failed Connections

·       Device::Anomalous Github Download

·       Device::New PowerShell User Agent

·       Unusual Activity::Unusual External Data to New Endpoint

/ NETWORK-based Autonomous Response model alerts:

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

·       Antigena / Network::Significant Anomaly::Antigena Controlled and Model Breach

·       Antigena / Network::Significant Anomaly::Antigena Breaches Over Time Block

Indicators of Compromise (IoCs)

IP/Hostname:

·       zoom[.]uswebob[.]us · 148.72.73[.]98

·       83.136.208[.]246

·       check02id[.]com · 83.136.210[.]180

·       83.136.208[.]48

·       104.145.210[.]107

URIs:

·       /api/daemon

Destination Port Usage:

·       6783

·       5202

·       443

·       7365

·       8443

ASN:

·       AS400897 PETROSKY

·       AS398256 AS-ULTAHOST

User agents:

·       Mozilla/5.0 (Windows NT; Windows NT 10.0; fr-FR) WindowsPowerShell/5.1.26100.7920

·       Go-http-client/1.1

·       curl/8.7.1

MITRE ATT&CK Mapping

(Technique Name - Tactic - ID - Sub-Technique of)

·       Browser Session Hijacking - COLLECTION - T1185

·       Web Protocols - COMMAND AND CONTROL - T1071.001 - T1071

·       Install Digital Certificate - RESOURCE DEVELOPMENT - T1608.003 - T1608

·       PowerShell - EXECUTION - T1059.001 - T1059

·       Domain Generation Algorithms - COMMAND AND CONTROL - T1568.002 - T1568

·       Non-Standard Port - COMMAND AND CONTROL - T1571

·       Malware - RESOURCE DEVELOPMENT - T1588.001 - T1588

·       Web Service - COMMAND AND CONTROL - T1102

·       Code Repositories - COLLECTION - T1213.003 - T1213

·       Exploitation of Remote Services - LATERAL MOVEMENT - T1210

·       Exfiltration Over C2 Channel - EXFILTRATION - T1041

·       Exfiltration to Cloud Storage - EXFILTRATION - T1567.002 - T1567

References:

[1] https://www.microsoft.com/en-us/security/blog/2026/04/16/dissecting-sapphire-sleets-macos-intrusion-from-lure-to-compromise/

[2] https://radar.securityalliance.org/advisory-on-dprk-unc1069-fake-microsoft-teams-and-zoom-calls/

[3] https://www.virustotal.com/gui/domain/uswebob.us

[4] https://www.virustotal.com/gui/ip-address/83.136.210.180/community

[5] https://www.virustotal.com/gui/ip-address/104.145.210.107/community

[6] https://www.virustotal.com/gui/ip-address/83.136.208.48/community

[7] https://www.virustotal.com/gui/ip-address/83.136.208.246/community

[8] https://www.darktrace.com/blog/applescript-abuse-unpacking-a-macos-phishing-campaign

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About the author
Justin Torres
Cyber Analyst

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June 24, 2026

A New Security Challenge: The Curious Case of Prompt Language Analysis

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Why prompt analysis is emerging as a key AI security challenge

If securing AI has been one of the defining cybersecurity conversations of the past year, prompt analysis is quickly becoming one of its most interesting frontiers.

Security leaders are under pressure to understand how AI is being used across the business. In some organizations, that means governing employee use of chatbots. In others, it means overseeing copilots embedded into SaaS platforms, monitoring coding assistants, or assessing the growing footprint of autonomous agents. However different these use cases may appear on the surface, they share a common factor: humans and machines are usually interacting with enterprise systems through language.  

How prompt language differs from traditional security telemetry

For years, defenders have become used to working with familiar forms of telemetry: email traffic, network connections, API calls, endpoint processes, authentication events. Prompt language is different. It is not simply another log source. It is an expression of intent, instruction, curiosity, urgency, and sometimes manipulation. It reflects the end-goal of a user or agent, but not always with enough surrounding context to interpret the risk correctly.

Why existing security approaches only partially explain prompt risk

A growing number of vendors are approaching the task of securing AI from the angle they know best. Perimeter vendors are extending web or browser controls into AI usage. Identity vendors are emphasizing agent permissions and access governance. Data security and DLP providers are focusing on content inspection and exfiltration risk. All of these perspectives matter, but individually can’t fully explain the problem.

The challenge with securing AI is not just that a new application category has emerged. It is that language has become a new operating layer in the enterprise.

Employees now use prompts to summarize documents, generate code, analyze spreadsheets, query internal knowledge, and trigger multi-step actions through agents. In each case, prompt language acts as the interface between human intent and machine execution. That makes prompts incredibly valuable from a security perspective as they can hint at misuse, policy violations, data exposure, or attempts to circumvent controls. However, they can also be deeply ambiguous when viewed in isolation. That ambiguity is the heart of the issue.

Prompts as behavioral signals, not just text to classify

A prompt by itself tells you what was asked. It does not necessarily tell you whether the request is expected, risky, accidental, or entirely legitimate in context. Two nearly identical prompts can carry very different meanings depending on the role and function of who issued them, what systems they can access, and what actions followed. In other words, prompts are not just text to classify. They are behavioral signals to interpret.

Example: How context changes prompt risk entirely

Consider a common enterprise scenario. An employee is pulled into a new project with an aggressive deadline. Almost overnight, their use of AI tools spikes. They begin prompting more frequently, working across unfamiliar documents, querying new data sources, and interacting with more systems than usual to accelerate delivery. Viewed narrowly, this may look suspicious. Prompt volume increases, file access patterns change, API and SaaS activity rise. From some vantage points, it may resemble insider risk or unmanaged AI usage.

But now add context. Imagine that, earlier that day, the employee received instructions from a senior leader asking them to support a time-sensitive initiative. Their communication history shows that this leader is a legitimate reporting-line superior. Their recent collaboration patterns align with the new project team. Their subsequent activity, while unusual for that individual’s baseline, is consistent with the business task they were assigned.

What initially looked like a risk event may actually be a normal response to business pressure. Without the surrounding context of communication, organizational relationships, and broader behavioral patterns, prompt activity alone could generate more noise than insight.

The reverse is also true. A prompt may appear benign on the surface while the context around it suggests elevated risk. A request that seems routine could originate from a compromised user, a newly connected external agent, a shadow AI workflow, or a user acting outside their normal role. The language itself may not contain anything obviously malicious, but the surrounding conditions may tell a very different story.

What security teams need to analyze prompts effectively

The future of prompt analysis is not just about understanding language. It is about understanding language in context.

To do that well, security teams need more than prompt inspection. They need to understand:

  • Who is issuing the prompt, whether human or agent
  • How that identity normally behaves across the enterprise
  • What systems, data, and workflows are connected to the interaction
  • Which relationships and communications explain the surrounding activity
  • Whether the downstream actions align with expected business behavior

When those layers are absent, prompt analysis can become another isolated control surface: useful in theory, but limited in practice. Security teams may detect unusual wording but miss the operational function behind it, overreact to benign changes in behavior, or miss subtle misuse because the prompt itself did not appear dangerous.

How organizations should think about prompt analysis going forward

Security teams have seen this pattern before. In the cloud, posture without runtime context left important gaps. In identity, access control without behavioral understanding missed misuse that looked legitimate on paper. In data security, content inspection without business context often created friction without resolving risk. AI is exposing the same lesson again: controls are strongest when they are coordinated, not isolated. As organizations work to secure AI and identify gaps across their security operations, prompt analysis will become an increasingly important source of insight, but only as part of a broader strategy.

Prompt analysis will undoubtedly become more common, as prompts are one of the clearest windows into how people and agents are using AI systems. However, what matters most is not simply collecting prompts or filtering dangerous phrases, but being able to place that language inside a wider behavioral and operational picture.

Organizations that already have a broader understanding of how work gets done across the enterprise will be better positioned to make sense of prompt language as this category matures. They will be better able to distinguish urgency from abuse, experimentation from exfiltration, and productive AI adoption from hidden risk.

Figure 1: Darktrace / SECURE AI reconstructs the full sequence of events, showing every user and agent interaction in context, with risky prompts highlighted and categorized, including PII, sensitive data, and other policy violations.

At Darktrace, this is the key lesson emerging from the market: prompt language does matter, but it does not stand alone. It is most valuable when treated as a new behavioral input that can enrich understanding across the enterprise, not as a self-contained source of truth.

Why prompts become less useful when analyzed in isolation

The curious case of prompt language analysis, then, is this: the more important prompts become, the less useful they are in a vacuum.

The real opportunity is not just to see what was asked. It is to understand why it was asked, what it meant in that moment, and what happened next.

For a deeper look at how organizations are approaching this challenge from the strengths of prompt analysis to its limitations in isolation see Prompt Security in Enterprise AI: Strengths, Weaknesses, and Common Approaches, which expands on the role prompt-level controls play within a broader, context-driven security strategy.

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About the author
Nabil Zoldjalali
VP, Field CISO
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