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January 30, 2023

Qakbot Resurgence in the Cyber Landscape

Stay informed on the evolving threat Qakbot. Protect yourself from the Qakbot resurgence! Learn more from our Darktrace AI Cybersecurity experts!
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
Nahisha Nobregas
SOC Analyst
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30
Jan 2023

In June 2022, Darktrace observed a surge in Qakbot infections across its client base. The detected Qakbot infections, which in some cases led to the delivery of secondary payloads such as Cobalt Strike and Dark VNC, were initiated through novel delivery methods birthed from Microsoft’s default blocking of XL4 and VBA macros in early 2022 [1]/[2]/[3]/[4] and from the public disclosure in May 2022 [5] of the critical Follina vulnerability (CVE-2022-30190) in Microsoft Support Diagnostic Tool (MSDT). Despite the changes made to Qakbot’s delivery methods, Qakbot infections still inevitably resulted in unusual patterns of network activity. In this blog, we will provide details of these network activities, along with Darktrace/Network’s coverage of them. 

Qakbot Background 

Qakbot emerged in 2007 as a banking trojan designed to steal sensitive data such as banking credentials.  Since then, Qakbot has developed into a highly modular triple-threat powerhouse used to not only steal information, but to also drop malicious payloads and to serve as a backdoor. The malware is also versatile, with its delivery methods regularly changing in response to the changing threat landscape.  

Threat actors deliver Qakbot through email-based delivery methods. In the first half of 2022, Microsoft started rolling out versions of Office which block XL4 and VBA macros by default. Prior to this change, Qakbot email campaigns typically consisted in the spreading of deceitful emails with Office attachments containing malicious macros.  Opening these attachments and then enabling the macros within them would lead users’ devices to install Qakbot.  

Actors who deliver Qakbot onto users’ devices may either sell their access to other actors, or they may leverage Qakbot’s capabilities to pursue their own objectives [6]. A common objective of actors that use Qakbot is to drop Cobalt Strike beacons onto infected systems. Actors will then leverage the interactive access provided by Cobalt Strike to conduct extensive reconnaissance and lateral movement activities in preparation for widespread ransomware deployment. Qakbot’s close ties to ransomware activity, along with its modularity and versatility, make the malware a significant threat to organisations’ digital environments.

Activity Details and Qakbot Delivery Methods

During the month of June, variationsof the following pattern of network activity were observed in several client networks:

1.     User’s device contacts an email service such as outlook.office[.]com or mail.google[.]com

2.     User’s device makes an HTTP GET request to 185.234.247[.]119 with an Office user-agent string and a ‘/123.RES' target URI. The request is responded to with an HTML file containing a exploit for the Follina vulnerability (CVE-2022-30190)

3.     User’s device makes an HTTP GET request with a cURL User-Agent string and a target URI ending in ‘.dat’ to an unusual external endpoint. The request is responded to with a Qakbot DLL sample

4.     User’s device contacts Qakbot Command and Control servers over ports such as 443, 995, 2222, and 32101

In some cases, only steps 1 and 4 were seen, and in other cases, only steps 1, 3, and 4 were seen. The different variations of the pattern correspond to different Qakbot delivery methods.

Figure 1: Geographic distribution of Darktrace clients affected by Qakbot

Qakbot is known to be delivered via malicious email attachments [7]. The Qakbot infections observed across Darktrace’s client base during June were likely initiated through HTML smuggling — a method which consists in embedding malicious code into HTML attachments. Based on open-source reporting [8]-[14] and on observed patterns of network traffic, we assess with moderate to high confidence that the Qakbot infections observed across Darktrace’s client base during June 2022 were initiated via one of the following three methods:

  • User opens HTML attachment which drops a ZIP file on their device. ZIP file contains a LNK file, which when opened, causes the user's device to make an external HTTP GET request with a cURL User-Agent string and a '.dat' target URI. If successful, the HTTP GET request is responded to with a Qakbot DLL.
  • User opens HTML attachment which drops a ZIP file on their device. ZIP file contains a docx file, which when opened, causes the user's device to make an HTTP GET request to 185.234.247[.]119 with an Office user-agent string and a ‘/123.RES' target URI. If successful, the HTTP GET request is responded to with an HTML file containing a Follina exploit. The Follina exploit causes the user's device to make an external HTTP GET with a '.dat' target URI. If successful, the HTTP GET request is responded to with a Qakbot DL.
  • User opens HTML attachment which drops a ZIP file on their device. ZIP file contains a Qakbot DLL and a LNK file, which when opened, causes the DLL to run.

The usage of these delivery methods illustrate how threat actors are adopting to a post-macro world [4], with their malware delivery techniques shifting from usage of macros-embedding Office documents to usage of container files, Windows Shortcut (LNK) files, and exploits for novel vulnerabilities. 

The Qakbot infections observed across Darktrace’s client base did not only vary in terms of their delivery methods — they also differed in terms of their follow-up activities. In some cases, no follow-up activities were observed. In other cases, however, actors were seen leveraging Qakbot to exfiltrate data and to deliver follow-up payloads such as Cobalt Strike and Dark VNC.  These follow-up activities were likely preparation for the deployment of ransomware. Darktrace’s early detection of Qakbot activity within client environments enabled security teams to take actions which likely prevented the deployment of ransomware. 

Darktrace Coverage 

Users’ interactions with malicious email attachments typically resulted in their devices making cURL HTTP GET requests with empty Host headers and target URIs ending in ‘.dat’ (such as as ‘/24736.dat’ and ‘/noFindThem.dat’) to rare, external endpoints. In cases where the Follina vulnerability is believed to have been exploited, users’ devices were seen making HTTP GET requests to 185.234.247[.]119 with a Microsoft Office User-Agent string before making cURL HTTP GET requests. The following Darktrace DETECT/Network models typically breached as a result of these HTTP activities:

  • Device / New User Agent
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Device / New User Agent and New IP
  • Anomalous File / EXE from Rare External Location
  • Anomalous File / Numeric Exe Download 

These DETECT models were able to capture the unusual usage of Office and cURL User-Agent strings on affected devices, as well as the downloads of the Qakbot DLL from rare external endpoints. These models look for unusual activity that falls outside a device’s usual pattern of behavior rather than for activity involving User-Agent strings, URIs, files, and external IPs which are known to be malicious.

When enabled, Darktrace RESPOND/Network autonomously intervened, taking actions such as ‘Enforce group pattern of life’ and ‘Block connections’ to quickly intercept connections to Qakbot infrastructure. 

Figure 2: This ‘New User Agent to IP Without Hostname’ model breach highlights an example of Darktrace’s detection of a device attempting to download a file containing a Follina exploit
Figure 3: This ‘New User Agent to IP Without Hostname’ model breach highlights an example of Darktrace’s detection of a device attempting to download Qakbot
Figure 4: The Event Log for an infected device highlights the moment a connection to the endpoint outlook.office365[.]com was made. This was followed by an executable file transfer detection and use of a new User-Agent, curl/7.9.1

After installing Qakbot, users’ devices started making connections to Command and Control (C2) endpoints over ports such as 443, 22, 990, 995, 1194, 2222, 2078, 32101. Cobalt Strike and Dark VNC may have been delivered over some of these C2 connections, as evidenced by subsequent connections to endpoints associated with Cobalt Strike and Dark VNC. These C2 activities typically caused the following Darktrace DETECT/Network models to breach: 

  • Anomalous Connection / Application Protocol on Uncommon Port
  • Anomalous Connection / Multiple Connections to New External TCP Port
  • Compromise / Suspicious Beaconing Behavior
  • Anomalous Connection / Multiple Failed Connections to Rare Endpoint
  • Compromise / Large Number of Suspicious Successful Connections
  • Compromise / Sustained SSL or HTTP Increase
  • Compromise / SSL or HTTP Beacon
  • Anomalous Connection / Rare External SSL Self-Signed
  • Anomalous Connection / Anomalous SSL without SNI to New External
  • Compromise / SSL Beaconing to Rare Destination
  • Compromise / Suspicious TLS Beaconing To Rare External
  • Compromise / Slow Beaconing Activity To External Rare
Figure 5: This Device Event Log illustrates the Command and Control activity displayed by a Qakbot-infected device

The Darktrace DETECT/Network models which detected these C2 activities do not look for devices making connections to known, malicious endpoints. Rather, they look for devices deviating from their ordinary patterns of activity, making connections to external endpoints which internal devices do not usually connect to, over ports which devices do not normally connect over. 

In some cases, actors were seen exfiltrating data from Qakbot-infected systems and dropping Cobalt Strike in order to conduct extensive discovery. These exfiltration activities typically caused the following models to breach:

  • Anomalous Connection / Data Sent to Rare Domain
  • Unusual Activity / Enhanced Unusual External Data Transfer
  • Anomalous Connection / Uncommon 1 GiB Outbound
  • Anomalous Connection / Low and Slow Exfiltration to IP
  • Unusual Activity / Unusual External Data to New Endpoints

The reconnaissance and brute-force activities carried out by actors typically resulted in breaches of the following models:

  • Device / ICMP Address Scan
  • Device / Network Scan
  • Anomalous Connection / SMB Enumeration
  • Device / New or Uncommon WMI Activity
  •  Unusual Activity / Possible RPC Recon Activity
  • Device / Possible SMB/NTLM Reconnaissance
  •  Device / SMB Lateral Movement
  •  Device / Increase in New RPC Services
  •  Device / Spike in LDAP Activity
  • Device / Possible SMB/NTLM Brute Force
  • Device / SMB Session Brute Force (Non-Admin)
  • Device / SMB Session Brute Force (Admin)
  • Device / Anomalous NTLM Brute Force

Conclusion

June 2022 saw Qakbot swiftly mould itself in response to Microsoft's default blocking of macros and the public disclosure of the Follina vulnerability. The evolution of the threat landscape in the first half of 2022 caused Qakbot to undergo changes in its delivery methods, shifting from delivery via macros-based methods to delivery via HTML smuggling methods. The effectiveness of these novel delivery methods where highlighted in Darktrace's client base, where large volumes of Qakbot infections were seen during June 2022. Leveraging Self-Learning AI, Darktrace DETECT/Network was able to detect the unusual network behaviors which inevitably resulted from these novel Qakbot infections. Given that the actors behind these Qakbot infections were likely seeking to deploy ransomware, these detections, along with Darktrace RESPOND/Network’s autonomous interventions, ultimately helped to protect affected Darktrace clients from significant business disruption.  

Appendices

List of IOCs

References

[1] https://techcommunity.microsoft.com/t5/excel-blog/excel-4-0-xlm-macros-now-restricted-by-default-for-customer/ba-p/3057905

[2] https://techcommunity.microsoft.com/t5/microsoft-365-blog/helping-users-stay-safe-blocking-internet-macros-by-default-in/ba-p/3071805

[3] https://learn.microsoft.com/en-us/deployoffice/security/internet-macros-blocked

[4] https://www.proofpoint.com/uk/blog/threat-insight/how-threat-actors-are-adapting-post-macro-world

[5] https://twitter.com/nao_sec/status/1530196847679401984

[6] https://www.microsoft.com/security/blog/2021/12/09/a-closer-look-at-qakbots-latest-building-blocks-and-how-to-knock-them-down/

[7] https://www.zscaler.com/blogs/security-research/rise-qakbot-attacks-traced-evolving-threat-techniques

[8] https://www.esentire.com/blog/resurgence-in-qakbot-malware-activity

[9] https://www.fortinet.com/blog/threat-research/new-variant-of-qakbot-spread-by-phishing-emails

[10] https://twitter.com/pr0xylife/status/1539320429281615872

[11] https://twitter.com/max_mal_/status/1534220832242819072

[12] https://twitter.com/1zrr4h/status/1534259727059787783?lang=en

[13] https://isc.sans.edu/diary/rss/28728

[14] https://www.fortiguard.com/threat-signal-report/4616/qakbot-delivered-through-cve-2022-30190-follina

Credit to:  Hanah Darley, Cambridge Analyst Team Lead and Head of Threat Research and Sam Lister, Senior Cyber Analyst

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
Nahisha Nobregas
SOC Analyst

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February 3, 2026

The State of AI Cybersecurity 2026: Unveiling insights from over 1,500 security leaders

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2025 was the year enterprise AI went mainstream. In 2026, it’s made its way into every facet of the organizational structure – transforming workflows, revolutionizing productivity, and creating new value streams. In short, it’s opened up a whole new attack surface.  

At the same time, AI has accelerated the pace of cybersecurity arms race on both sides: adversaries are innovating using the latest AI technologies at their disposal while defenders scramble to outmaneuver them and stay ahead of AI-powered threats.  

That’s why Darktrace publishes this research every year. The State of AI Cybersecurity 2026 provides an annual snapshot of how the AI threat landscape is shifting, where organizations are adopting AI to maximum advantage, and how they are securing AI in the enterprise.

What is the State of AI Cybersecurity 2026?

We surveyed over 1,500 CISOs, IT leaders, administrators, and practitioners from a range of industries and different countries to uncover their attitudes, understanding, and priorities when it comes to AI threats, agents, tools, and operations in 2026. ​

The results show a fast-changing picture, as security leaders race to navigate the challenges and opportunities at play. Since last year, there has been enormous progress towards maturity in areas like AI literacy and confidence in AI-powered defense, while issues around AI governance remain inconclusive.

Let’s look at some of the key findings for 2026.

What’s the impact of AI on the attack surface?

Security leaders are seeing the adoption of AI agents across the workforce, and are increasingly concerned about the security implications.

  • 44% are extremely or very concerned with the security implications of third-party LLMs (like Copilot or ChatGPT)
  • 92% are concerned about the use of AI agents across the workforce and their impact on security

The rapid expansion of generative AI across the enterprise is outpacing the security frameworks designed to govern it. AI systems behave in ways that traditional defenses are not designed to monitor, introducing new risks around data exposure, unauthorized actions, and opaque decision-making as employees embed generative AI and autonomous agents into everyday workflows.  

Their top concerns? Sensitive data exposure ranks top (61%), while regulatory compliance violations are a close second (56%). These risks tend to have the fastest and most material fallout – ranging from fines to reputational harm – and are more likely to materialize in environments where AI governance is still evolving.

What’s the impact of AI on the cyber threat landscape?

AI is now being used to expedite every stage of the attack kill chain – from initial intrusion to privilege escalation and data exfiltration. 

“73% say that AI-powered threats are already having a significant impact on their organization.”

With AI, attackers can launch novel attacks at scale, and this is significantly increasing the number of threats requiring attention by the security team – often to the point of overwhelm.  

Traditional security solutions relying on historical attack data were never designed to handle an environment where attacks continuously evolve, multiply, and optimize at machine speed, so it’s no surprise that 92% agree that AI-powered cyber-threats are forcing them to significantly upgrade their defenses.

How is AI reshaping cybersecurity operations?

Cybersecurity workflows are still in flux as security leaders get used to the integration of AI agents into everyday operations.  

“Generative AI is now playing a role in 77% of security stacks.” But only 35% are using unsupervised machine learning.

AI technologies are diverse, ranging from LLMs to NLP systems, GANs, and unsupervised machine learning, with each type offering specific capabilities and facing particular limitations. The lack of familiarity with the different types of AI used within the security stack may be holding some practitioners back from using these new technologies to their best advantage.  

It also creates a lack of trust between humans and AI systems: only 14% of security professionals allow AI to take independent remediation actions in the SOC with no human in the loop.

Another new trend for this year is a strong preference (85%) for relying on Managed Security Service Providers (MSSPs) for SOC services instead of in-house teams, as organizations aim to secure expert, always-on support without the cost and operational burden of running an internal operation.

What impact is AI having on cybersecurity tools?

“96% of cybersecurity professionals agree that AI can significantly improve the speed and efficiency with which they work.”

The capacity of AI for augmenting security efforts is undisputed. But as vendor AI claims become far-reaching, it falls to security leaders to clarify which AI tools offer true value and can help solve their specific security challenges.  

Security professionals are aligned on the biggest area of impact: 72% agree that AI excels at detecting anomalies thanks to its advanced pattern recognition. This enables it to identify unusual behavior that may signal a threat, even when the specific attack has never been encountered or recorded in existing datasets.  

“When purchasing new security capabilities, 93% prefer ones that are part of a broader platform over individual point products.”

Like last year, the drive towards platform consolidation remains strong. Fewer vendors can mean tighter integrations, less console switching, streamlined management, and stronger cross-domain threat insights. The challenge is finding vendors that perform well across the board.

See the full report for more statistics and insights into how security leaders are responding to the AI landscape in 2026.

Learn more about securing AI in your enterprise.

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February 3, 2026

Introducing Darktrace / SECURE AI: Complete AI Security Across Your Enterprise

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Why securing AI can’t wait

AI is entering the enterprise faster than IT and security teams can keep up, appearing in SaaS tools, embedded in core platforms, and spun up by teams eager to move faster.  

As this adoption accelerates, it introduces unpredictable behaviors and expands the attack surface in ways existing security tools can’t see or control, startup or platform, they all lack one trait. These new types of risks command the attention of security teams and boardrooms, touching everything from business integrity to regulatory exposure.

Securing AI demands a fundamentally different approach, one that understands how AI behaves, how it interacts with data and users, and how risk emerges in real time. That shift is at the core of how organizations should be thinking about securing AI across the enterprise.

What is the current state of securing AI?

In Darktrace’s latest State of AI in Cybersecurity Report research across 1,500 cybersecurity professionals shows that the percentage of organizations without an AI adoption policy grew from 55% last year to 63% this year.

More troubling, the percentage of organizations without any plan to create an AI policy nearly tripled from 3% to 8%. Without clear policies, businesses are effectively accelerating blindfolded.

When we analyzed activity across our own customer base, we saw the same patterns playing out in their environments. Last October alone, we saw a 39% month-over-month increase in anomalous data uploads to generative AI services, with the average upload being 75MB. Given the size and frequency of these uploads, it's almost certain that much of this data should never be leaving the enterprise.

Many security teams still lack visibility into how AI is being used across their business; how it’s behaving, what it’s accessing, and most importantly, whether it’s operating safely. This unsanctioned usage quietly expands, creating pockets of AI activity that fall completely outside established security controls. The result is real organizational exposure with almost no visibility, underscoring just how widespread AI use has already become desipite the existence of formal policies.

This challenge doesn’t stop internally. Shadow AI extends into third-party tools, vendor platforms, and partner systems, where AI features are embedded without clear oversight.

Meanwhile, attackers are now learning to exploit AI’s unique characteristics, compounding the risks organizations are already struggling to manage.

The leader in AI cybersecurity now secures AI

Darktrace brings more than a decade of behavioral AI expertise built on an enterprise‑wide platform designed to operate in the complex, ambiguous environments where today’s AI now lives.  

Other cybersecurity technologies try to predict each new attack based on historical attacks. The problem is AI operates like humans do. Every action introduces new information that changes how AI behaves, its unpredictable, and historical attack tactics are now only a small part of the equation, forcing vendors to retrofit unproven acquisitions to secure AI.  

Darktrace is fundamentally different. Our Self‑Learning AI learns what “normal” looks like for your unique business: how your users, systems, applications, and now AI agents behave, how they communicate, and how data flows. This allows us to spot even the smallest shifts when something changes in meaningful ways. Long before AI agents were introduced, our technology was already interpreting nuance, detecting drift, uncovering hidden relationships, and making sense of ambiguous activity across networks, cloud, SaaS, email, OT, identities, and endpoints.

As AI introduces new behaviors, unstructured interactions, invisible pathways, and the rise of Shadow AI, these challenges have only intensified. But this is exactly the environment our platform was built for. Securing AI isn’t a new direction for Darktrace — it’s the natural evolution of the behavioral intelligence we’ve delivered to thousands of organizations worldwide.

Introducing Darktrace / SECURE AI – Complete AI security across your enterprise

We are proud to introduce Darktrace / SECURE AI, the newest product in the Darktrace ActiveAI Security Platform designed to secure AI across the whole enterprise.

This marks the next chapter in our mission to secure organizations from cyber threats and emerging risks. By combining full visibility, intelligent behavioral oversight, and real-time control, Darktrace is enabling enterprises to safely adopt, manage, and build AI within their business. This ensures that AI usage, data access, and behavior remain aligned to security baselines, compliance, and business goals.

Darktrace / SECURE AI can bring every AI interaction into a single view, helping teams understand intent, assess risk, protect sensitive data, and enforce policy across both human and AI Agent activity. Now organizations can embrace AI with confidence, with visibility to ensure it is operating safely, responsibly, and in alignment with their security and compliance needs.  

Because securing AI spans multiple areas and layers of complexity, Darktrace / SECURE AI is built around four foundational use cases that ensure your whole enterprise and every AI use affecting your business, whether owned or through third parties, is protected, they are:

  • Monitoring the prompts driving GenAI agents and assistants
  • Securing business AI agent identities in real time
  • Evaluating AI risks in development and deployment
  • Discovering and controlling Shadow AI

Monitoring the prompts driving GenAI agents and assistants

For AI systems, prompts are one of the most active and sensitive points of interaction—spanning human‑AI exchanges where users express intent and AI‑AI interactions where agents generate internal prompts to reason and coordinate. Because prompt language effectively is behavior, and because it relies on natural language rather than a fixed, finite syntax, the attack surface is open‑ended. This makes prompt‑driven risks far more complex than traditional API‑based vulnerabilities tied to CVEs.

Whether an attacker is probing for weaknesses, an employee inadvertently exposes sensitive data, or agents generate their own sub‑tasks to drive complex workflows, security teams must understand how prompt behavior shapes model behavior—and where that behavior can go wrong. Without that behavioral understanding, organizations face heightened risks of exploitation, drift, and cascading failures within their AI systems.

Darktrace / SECURE AI brings together all prompt activity across enterprise AI systems, including Microsoft Copilot and ChatGPT Enterprise, low‑code environments like Microsoft Copilot Studio, SaaS providers like Salesforce and Microsoft 365, and high‑code platforms such as AWS Bedrock and SageMaker, into a single, unified layer of visibility.  

Beyond visibility, Darktrace applies behavioral analytics to understand whether a prompt is unusual or risky in the context of the user, their peers, and the broader organization. Because AI attacks are far more complex and conversational than traditional exploits against fixed APIs – sharing more in common with email and Teams/Slack interactions, —this behavioral understanding is essential. By treating prompts as behavioral signals, Darktrace can detect conversational attacks, malicious chaining, and subtle prompt‑injection attempts, and where integrations allow, intervene in real time to block unsafe prompts or prevent harmful model actions as they occur.

Securing business AI agent identities in real time

As organizations adopt more AI‑driven workflows, we’re seeing a rapid rise in autonomous and semi‑autonomous agents operating across the business. These agents operate within existing identities, with the capability to access systems, read and write data, and trigger actions across cloud platforms, internal infrastructure, applications, APIs, and third‑party services. Some identities are controlled, like users, others like the ones mentioned, can appear anywhere, with organizations having limited visibility into how they’re configured or how their permissions evolve over time.  

Darktrace / SECURE AI gives organizations a real‑time, identity‑centric understanding of what their AI agents are doing, not just what they were designed to do. It automatically discovers live agent identities operating across SaaS, cloud, network, endpoints, OT, and email, including those running inside third‑party environments.  

The platform maps how each agent is configured, what systems it accesses, and how it communicates, including activity such as MCP usage or interactions with storage services where sensitive data may reside.  

By continuously observing agent behavior across all domains, Darktrace / SECURE AI highlights when unnecessary or risky permissions are granted, when activity patterns deviate, or when agents begin chaining together actions in unintended ways. This real‑time audit trail allows organizations to evaluate whether agent actions align with intended operational parameters and catch anomalous or risky behavior early.    

Evaluating AI risks in development and deployment

In the build phase, new identities are created, entitlements accumulate, components are stitched together across SaaS, cloud, and internal environments, and logic starts taking shape through prompts and configurations.  

It’s a highly dynamic and often fragmented process, and even small missteps here, such as a misconfiguration in a created agent identity, can become major security issues once the system is deployed. This is why evaluating AI risk during development and deployment is critical.

Darktrace / SECURE AI brings clarity and control across this entire lifecycle — from the moment an AI system starts taking shape to the moment it goes live. It allows you to gain visibility into created identities and their access across hyperscalers, low‑code SaaS, and internal labs, supported by AI security posture management that surfaces misconfigurations, over‑entitlement, and anomalous building events. Darktrace/ SECURE AI then connects these development insights directly to prompt oversight, connecting how AI is being built to how it will behave once deployed.  The result is a safer, more predictable AI lifecycle where risks are discovered early, guardrails are applied consistently, and innovations move forward with confidence rather than guesswork.

Discovering and controlling Shadow AI

Shadow AI has now appeared across every corner of the enterprise. It’s not just an employee pasting internal data into an external chatbot; it includes unsanctioned agent builders, hidden MCP servers, rogue model deployments, and AI‑driven workflows running on devices or services no one expected to be using AI.  

Darktrace / SECURE AI brings this frontier into view by continuously analyzing interactions across cloud, networks, endpoints, OT, and SASE environments. It surfaces unapproved AI usage wherever it appears and distinguishes legitimate activity in sanctioned tools from misuse or high‑risk behavior. The system identifies hidden AI components and rogue agents, reveals unauthorized deployments and unexpected connections to external AI systems, and highlights risky data flows that deviate from business norms.

When the behavior warrants a response, Darktrace / SECURE AI enables policy enforcement that guides users back toward sanctioned options while containing unsafe or ungoverned adoption. This closes one of the fastest‑expanding security gaps in modern enterprises and significantly reduces the attack surface created by shadow AI.

Conclusion

What’s needed now along with policies and frameworks for AI adoption is the right tooling to detect threats based on AI behavior across shadow use, prompt risks, identity misuse, and AI development.  

Darktrace is uniquely positioned to secure AI, we’ve spent over a decade building AI that learns your business – understanding subtle behavior across the entire enterprise long before AI agents arrived. With over 10,000 customers relying on Darktrace as the last line of defense to capture threats others cannot, Securing AI isn’t a pivot for us, it's not an acquisition; it’s the natural extension of the behavioral expertise and enterprise‑wide intelligence our platform was built on from the start.  

To learn more about how to secure AI at your organization we curated a readiness program that brings together IT and security leaders navigating this responsibility, providing a forum to prepare for high-impact decisions, explore guardrails, and guide the business amid growing uncertainty and pressure.

Sign up for the Secure AI Readiness Program here: This gives you exclusive access to the latest news on the latest AI threats, updates on emerging approaches shaping AI security, and insights into the latest innovations, including Darktrace’s ongoing work in this area.

Ready to talk with a Darktrace expert on securing AI? Register here to receive practical guidance on the AI risks that matter most to your business, paired with clarity on where to focus first across governance, visibility, risk reduction, and long-term readiness.  

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About the author
Brittany Woodsmall
Product Marketing Manager, AI
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