July 18, 2023

How Darktrace SOC Thwarted a BEC Attack

Discover how Darktrace's SOC detected and stopped a Business Email Compromise in a customer's SaaS environment.

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

Nicole Wong

Cyber Security Analyst

Jul 2023

What is Business Email Compromise (BEC)?

Business Email Compromise (BEC) is the practice of tricking an organization into transferring funds or sensitive data to a malicious actor.

Although at face value this type of attack may not carry the same gravitas as the more blockbuster, cloak-and-dagger type of attack such as ransomware [1], the costs of BEC actually dwarf that of ransomware [2]. Moreover, among UK organizations that reported a cyber breach in 2023, attacks related to BEC – namely phishing attacks, email impersonation, attempted hacking of online back accounts, and account takeover – were reported as the most disruptive, ahead of ransomware and other types of cyber-attack [3].

What makes a BEC attack successful?

BEC attacks are so successful and damaging due to the difficulty of detection for traditional security systems, along with their ease of execution. BEC does not require much technical sophistication to accomplish; rather, it exploits humans’ natural trust in known correspondents, via a phishing email for example, to induce them to perform a certain action.

How does a BEC attack work?

BEC attacks typically begin with a phishing email to an employee of an organization. Traditional email gateways may be unable to block the initial phishing email, as the email often appear to have been sent by a known correspondent, or it may contain minimal payload content.

The recipient’s interaction with the initial phishing email will likely result in the attacker gaining access to the user’s identity. Once access is obtained, the attacker may abuse the identity of the compromised user to obtain details of the user’s financial relations to the rest of the organization or its customers, eventually using these details to conduct further malicious email activity, such as sending out emails containing fraudulent wire transfer requests. Today, the continued growth in adoption of services to support remote working, such as cloud file storage and sharing, means that the compromise of a single user’s email account can also grant access to a wide range of corporate sensitive information.

How to protect against BEC attacks

The rapid uptake of cloud-based infrastructure and software-as-a-service (SaaS) outpaces the adoption of skills and expertise required to secure it, meaning that security teams are often less prepared to detect and respond to cloud-based attacks.

Alongside the adoption of security measures that specialize in anomaly-based detection and autonomous response, like Darktrace DETECT™ and Darktrace RESPOND™, it is extremely beneficial for organizations to have an around the clock security operations center (SOC) in place to monitor and investigate ongoing suspicious activity as it emerges.

In June 2023, Darktrace’s SOC alerted a customer to an active BEC attack within their cloud environment, following the successful detection of suspicious activity by Darktrace’s AI, playing a fundamental role in thwarting the attack in its early stages.

Darktrace Mitigates BEC Attack

Figure 1: Screenshot of the SaaS Console showing location information for the compromised SaaS account. The ability to visualize the distance between these two locations enables a SOC Analyst to deduce that the simultaneous activity from London and Derby may represent impossible travel’.

It was suspected the attack began with a phishing email, as on the previous day the user had received a highly anomalous email from an external sender with which the organization had not previously communicated. However, the customer had configured Darktrace/Email™ in passive mode, which meant that Darktrace was not able to carry out any RESPOND actions on this anomalous email to prevent it from landing in the user’s inbox. Despite this, Darktrace/Apps was able to instantly detect the subsequent unusual login to the customer’s SaaS environment; its anomaly-based approach to threat detection allowed it to recognize the anomalous behavior even though the malicious email had successfully reached the user.

Following the anomalous ExpressVPN login, Darktrace detected further account anomalies originating from another ExpressVPN IP (45.92.229[.]195), as the attacker accessed files over SharePoint. Notably, Darktrace identified that the logins from ExpressVPN IPs were performed with the software Chrome 114, however, activity from the legitimate account owner prior to these unusual logins was performed using the software Chrome 102. It is unusual for a user to be using multiple browser versions simultaneously, therefore in addition to the observed impossible travel, this further implied the presence of different actors behind the simultaneous account activity.

Figure 2: Screenshot of the Event Log for the compromised SaaS account, showing simultaneous login and file access activity on the account from different browser versions, and thus likely from different devices.

Darktrace identified that the files observed during this anomalous activity referenced financial information and personnel schedules, suggesting that the attacker was performing internal reconnaissance to gather information about sensitive internal company procedures, in preparation for further fraudulent financial activity.

Although the actions taken by the attacker were mostly passive, Darktrace/Apps chained together the multiple anomalies to understand that this pattern of activity was indicative of movement along the cyber kill chain. The multiple model breaches generated by the ongoing unusual activity triggered an Enhanced Monitoring model breach that was escalated to Darktrace’s SOC as the customer had subscribed to Darktrace’s Proactive Threat Notification (PTN) service. Enhanced Monitoring models detect activities that are more likely to be indicative of compromise.

Subsequently, Darktrace’s SOC triaged the activity detected on the SaaS account and sent a PTN alert to the customer, advising urgent follow up action. The encrypted alert contained relevant technical details of the incident that were summarized by an expert Darktrace Analyst, along with recommendations to the customer’s internal SOC team to take immediate action. Upon receipt and validation of the alert, the customer used Darktrace RESPOND to perform a manual force logout and block access from the external ExpressVPN IP.

Had Darktrace RESPOND been enabled in autonomous response mode, it would have immediately taken action to disable the account after ongoing anomalies were detected from it. However, as the customer only had RESPOND configured in the manual human confirmation model, the expertise of Darktrace’s SOC team was critical in enabling the customer to react and prevent further escalation of post-compromise activity. Evidence of further attempts to access the compromised account were observed hours after RESPOND actions were taken, including failed login attempts from another rare external IP, this time associated with the VPN service NordVPN.

Figure 3: Timeline of attack and response actions from Darktrace SOC and Darktrace RESPOND.

Because the customer had subscribed to Darktrace’s PTN service, they were able to further leverage the expertise of Darktrace’s global team of cyber analysts and request further analysis of which files were accessed by the legitimate account owner versus the attacker. This information was shared securely within the same Customer Portal ticket that was automatically opened on behalf of the customer when the PTN was alerted, allowing the customer’s security team to submit further queries and feedback, and request assistance to further investigate this alert within Darktrace. A similar service called Ask the Expert (ATE) exists for customers to draw from the expertise of Darktrace’s analysts at any time, not just when PTNs are alerted.

Conclusion

The growing prevalence and impact of BEC attacks amid the shift to cloud-based infrastructure means that already stretched internal security teams may not have the sufficient human capacity to detect and respond to these threats.

Darktrace’s round-the-clock SOC thwarted a BEC attack that had the potential to result in significant financial and reputational damage to the legal services company, by alerting the customer to high priority activity during the early stages of the attack and sharing actionable insights that the customer could use to prevent further escalation. Following the confirmed compromise, the support and in-depth analysis provided by Darktrace’s SOC on the files accessed by the attacker enabled the customer to effectively report this breach to the Information Commissioner’s Office, to maintain compliance with UK data protection regulations. [4].

Although the attacker used IP addresses that were local to the customer’s country of operations and did not perform overtly noisy actions during reconnaissance, Darktrace was able to identify that this activity deviated from the legitimate user’s typical pattern of life, triggering model breaches at each stage of the attack as it progressed from initial access to internal reconnaissance. While Darktrace RESPOND triggered an action that would have prevented the attack autonomously, the customer’s configuration meant that Darktrace’s SOC had an even more significant role in alerting the customer directly to take manual action.

Credit to: Sam Lister, Senior Analyst, for his contributions to this blog.

Appendices

Darktrace DETECT/Apps Models Breached:

SaaS / Access / Unusual External Source for SaaS Credential Use
SaaS / Compromise / Login From Rare Endpoint While User Is Active
SaaS / Unusual Activity / Activity from Multiple Unusual IPs
SaaS / Unusual Activity / Multiple Unusual SaaS Activities
SaaS / Access / Suspicious Login Attempt
SaaS / Compromise / SaaS Anomaly Following Anomalous Login (Enhanced Monitoring Model)

Darktrace RESPOND/Apps Models Breached:

Antigena / SaaS / Antigena Unusual Activity Block
Antigena / SaaS / Antigena Suspicious SaaS Activity Block

MITRE ATT&CK Mapping

Tactic	Techniques
Reconnaissance	• T1598 – Phishing for Information
Initial Access	• T1078.004 – Valid Accounts: Cloud Accounts
Collection	• T1213.002 – Data from Information Repositories: Sharepoint

References

[1] Rand, D. (2022, November 10). Why Business Email Compromise Costs Companies More Than Ransomware Attacks. Retrieved from Tanium: https://www.tanium.com/blog/whybusiness-email-compromise-costs-companies-more-than-ransomware-attacks/

[2] Federal Bureau of Investigation. (2022). 2022 IC3 Report. Retrieved from IC3.gov: https://www.ic3.gov/Media/PDF/AnnualReport/2022_IC3Report.pdf

[3] Department for Science, Innovation & Technology. (2023, April 19). Cyber security breaches survey 2023. Retrieved from gov.uk: https://www.gov.uk/government/statistics/cyber-security-breaches-survey-2023/cybersecurity-breaches-survey-2023

[4] ICO. (2023). Personal data breaches: a guide. Retrieved from Information Commissioner's Office: https://ico.org.uk/for-organisations/report-a-breach/personal-data-breach/personal-data-breaches-a-guide/#whatbreachesdo

執筆者

Nicole Wong

Cyber Security Analyst

Inside the SOC

Written by

Nicole Wong

Cyber Security Analyst

Malware Research Engineer

•

February 3, 2026

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February 3, 2026

Darktrace Malware Analysis: Unpacking SnappyBee

Introduction

The aim of this blog is to be an educational resource, documenting how an analyst can perform malware analysis techniques such as unpacking. This blog will demonstrate the malware analysis process against well-known malware, in this case SnappyBee.

SnappyBee (also known as Deed RAT) is a modular backdoor that has been previously attributed to China-linked cyber espionage group Salt Typhoon, also known as Earth Estries [1] [2]. The malware was first publicly documented by TrendMicro in November 2024 as part of their investigation into long running campaigns targeting various industries and governments by China-linked threat groups.

In these campaigns, SnappyBee is deployed post-compromise, after the attacker has already obtained access to a customer's system, and is used to establish long-term persistence as well as deploying further malware such as Cobalt Strike and the Demodex rootkit.

To decrease the chance of detection, SnappyBee uses a custom packing routine. Packing is a common technique used by malware to obscure its true payload by hiding it and then stealthily loading and executing it at runtime. This hinders analysis and helps the malware evade detection, especially during static analysis by both human analysts and anti-malware services.

This blog is a practical guide on how an analyst can unpack and analyze SnappyBee, while also learning the necessary skills to triage other malware samples from advanced threat groups.

First principles

Packing is not a new technique, and threat actors have generally converged on a standard approach. Packed binaries typically feature two main components: the packed data and an unpacking stub, also called a loader, to unpack and run the data.

Typically, malware developers insert a large blob of unreadable data inside an executable, such as in the .rodata section. This data blob is the true payload of the malware, but it has been put through a process such as encryption, compression, or another form of manipulation to render it unreadable. Sometimes, this data blob is instead shipped in a different file, such as a .dat file, or a fake image. When this happens, the main loader has to read this using a syscall, which can be useful for analysis as syscalls can be easily identified, even in heavily obfuscated binaries.

In the main executable, malware developers will typically include an unpacking stub that takes the data blob, performs one or more operations on it, and then triggers its execution. In most samples, the decoded payload data is loaded into a newly allocated memory region, which will then be marked as executable and executed. In other cases, the decoded data is instead dropped into a new executable on disk and run, but this is less common as it increases the likelihood of detection.

Finding the unpacking routine

The first stage of analysis is uncovering the unpacking routine so it can be reverse engineered. There are several ways to approach this, but it is traditionally first triaged via static analysis on the initial stages available to the analyst.

SnappyBee consists of two components that can be analyzed:

A Dynamic-link Library (DLL) that acts as a loader, responsible for unpacking the malicious code
A data file shipped alongside the DLL, which contains the encrypted malicious code

Additionally, SnappyBee includes a legitimate signed executable that is vulnerable to DLL side-loading. This means that when the executable is run, it will inadvertently load SnappyBee’s DLL instead of the legitimate one it expects. This allows SnappyBee to appear more legitimate to antivirus solutions.

The first stage of analysis is performing static analysis of the DLL. This can be done by opening the DLL within a disassembler such as IDA Pro. Upon opening the DLL, IDA will display the DllMain function, which is the malware’s initial entry point and the first code executed when the DLL is loaded.

First, the function checks if the variable fdwReason is set to 1, and exits if it is not. This variable is set by Windows to indicate why the DLL was loaded. According to Microsoft Developer Network (MSDN), a value of 1 corresponds to DLL_PROCESS_ATTACH, meaning “The DLL is being loaded into the virtual address space of the current process as a result of the process starting up or as a result of a call to LoadLibrary” [3]. Since SnappyBee is known to use DLL sideloading for execution, DLL_PROCESS_ATTACH is the expected value when the legitimate executable loads the malicious DLL.

SnappyBee then uses the GetModule and GetProcAddress to dynamically resolve the address of the VirtualProtect in kernel32 and StartServiceCtrlDispatcherW in advapi32. Resolving these dynamically at runtime prevents them from showing up as a static import for the module, which can help evade detection by anti-malware solutions. Different regions of memory have different permissions to control what they can be used for, with the main ones being read, write, and execute. VirtualProtect is a function that changes the permissions of a given memory region.

SnappyBee then uses VirtualProtect to set the memory region containing the code for the StartServiceCtrlDispatcherW function as writable. It then inserts a jump instruction at the start of this function, redirecting the control flow to one of the SnappyBee DLL’s other functions, and then restores the old permissions.

In practice, this means when the legitimate executable calls StartServiceCtrlDispatcherW, it will immediately hand execution back to SnappyBee. Meanwhile, the call stack now appears more legitimate to outside observers such as antimalware solutions.

The hooked-in function then reads the data file that is shipped with SnappyBee and loads it into a new memory allocation. This pattern of loading the file into memory likely means it is responsible for unpacking the next stage.

Figure 2: The start of the unpacking routine that reads in dbindex.dat.

SnappyBee then proceeds to decrypt the memory allocation and execute the code.

Figure 3: The memory decryption routine.

This section may look complex, however it is fairly straight forward. Firstly, it uses memset to zero out a stack variable, which will be used to store the decryption key. It then uses the first 16 bytes of the data file as a decryption key to initialize the context from.

SnappyBee then calls the mbed_tls_arc4_crypt function, which is a function from the mbedtls library. Documentation for this function can be found online and can be referenced to better understand what each of the arguments mean [4].

The documentation for mbedtls_arc4_crypt. — Figure 4: The documentation for mbedtls_arc4_ crypt.

Comparing the decompilation with the documentation, the arguments SnappyBee passes to the function can be decoded as:

The context derived from 16-byte key at the start of the data is passed in as the context in the first parameter
The file size minus 16 bytes (to account for the key at the start of the file) is the length of the data to be decrypted
A pointer to the file contents in memory, plus 16 bytes to skip the key, is used as the input
A pointer to a new memory allocation obtained from VirtualAlloc is used as the output

So, putting it all together, it can be concluded that SnappyBee uses the first 16 bytes as the key to decrypt the data that follows , writing the output into the allocated memory region.

SnappyBee then calls VirtualProtect to set the decrypted memory region as Read + Execute, and subsequently executes the code at the memory pointer. This is clearly where the unpacked code containing the next stage will be placed.

Unpacking the malware

Understanding how the unpacking routine works is the first step. The next step is obtaining the actual code, which cannot be achieved through static analysis alone.

There are two viable methods to retrieve the next stage. The first method is implementing the unpacking routine from scratch in a language like Python and running it against the data file.

This is straightforward in this case, as the unpacking routine in relatively simple and would not require much effort to re-implement. However, many unpacking routines are far more complex, which leads to the second method: allowing the malware to unpack itself by debugging it and then capturing the result. This is the approach many analysts take to unpacking, and the following will document this method to unpack SnappyBee.

As SnappyBee is 32-bit Windows malware, debugging can be performed using x86dbg in a Windows sandbox environment to debug SnappyBee. It is essential this sandbox is configured correctly, because any mistake during debugging could result in executing malicious code, which could have serious consequences.

Before debugging, it is necessary to disable the DYNAMIC_BASE flag on the DLL using a tool such as setdllcharacteristics. This will stop ASLR from randomizing the memory addresses each time the malware runs and ensures that it matches the addresses observed during static analysis.

The first place to set a breakpoint is DllMain, as this is the start of the malicious code and the logical place to pause before proceeding. Using IDA, the functions address can be determined; in this case, it is at offset 10002DB0. This can be used in the Goto (CTRL+G) dialog to jump to the offset and place a breakpoint. Note that the “Run to user code” button may need to be pressed if the DLL has not yet been loaded by x32dbg, as it spawns a small process to load the DLL as DLLs cannot be executed directly.

The program can then run until the breakpoint, at which point the program will pause and code recognizable from static analysis can be observed.

Figure 5: The x32dbg dissassembly listing forDllMain.

In the previous section, this function was noted as responsible for setting up a hook, and in the disassembly listing the hook address can be seen being loaded at offset 10002E1C. It is not necessary to go through the whole hooking process, because only the function that gets hooked in needs to be run. This function will not be naturally invoked as the DLL is being loaded directly rather than via sideloading as it expects. To work around this, the Extended Instruction Pointer (EIP) register can be manipulated to point to the start of the hook function instead, which will cause it to run instead of the DllMain function.

To update EIP, the CRTL+G dialog can again be used to jump to the hook function address (10002B50), and then the EIP register can be set to this address by right clicking the first instruction and selecting “Set EIP here”. This will make the hook function code run next.

Figure 6: The start of the hookedin-in function

Once in this function, there are a few addresses where breakpoints should be set in order to inspect the state of the program at critical points in the unpacking process. These are:

- 10002C93, which allocates the memory for the data file and final code

- 10002D2D, which decrypts the memory

- 10002D81, which runs the unpacked code

Setting these can be done by pressing the dot next to the instruction listing, or via the CTRL+G Goto menu.

At the first breakpoint, the call to VirtualAlloc will be executed. The function returns the memory address of the created memory region, which is stored in the EAX register. In this case, the region was allocated at address 00700000.

Figure 7: The result of the VirtualAlloc call.

It is possible to right click the address and press “Follow in dump” to pin the contents of the memory to the lower pane, which makes it easy to monitor the region as the unpacking process continues.

*Figure 8: The allocated memory region shown in x32dbg’s dump.*

Single-stepping through the application from this point eventually reaches the call to ReadFile, which loads the file into the memory region.

*Figure 9: The allocated memory region after the file is read into it, showing high entropy data.*

The program can then be allowed to run until the next breakpoint, which after single-stepping will execute the call to mbedtls_arc4_crypt to decrypt the memory. At this point, the data in the dump will have changed.

*Figure 10: The same memory region after the decryption is run, showing lower entropy data.*

Right-clicking in the dump and selecting "Disassembly” will disassemble the data. This yields valid shell code, indicating that the unpacking succeeded, whereas corrupt or random data would be expected if the unpacking had failed.

*Figure 11: The disassembly view of the allocated memory.*

Right-clicking and selecting “Follow in memory map” will show the memory allocation under the memory map view. Right-clicking this then provides an option to dump the entire memory block to file.

*Figure 12: Saving the allocated memory region.*

This dump can then be opened in IDA, enabling further static analysis of the shellcode. Reviewing the shellcode, it becomes clear that it performs another layer of unpacking.

As the debugger is already running, the sample can be allowed to execute up to the final breakpoint that was set on the call to the unpacked shellcode. Stepping into this call will then allow debugging of the new shellcode.

The simplest way to proceed is to single-step through the code, pausing on each call instruction to consider its purpose. Eventually, a call instruction that points to one of the memory regions that were assigned will be reached, which will contain the next layer of unpacked code. Using the same disassembly technique as before, it can be confirmed that this is more unpacked shellcode.

Figure 13: The unpacked shellcode’s call to RDI, which points to more unpacked shellcode. Note this screenshot depicts the 64-bit variant of SnappyBee instead of 32-bit, however the theory is the same.

Once again, this can be dumped out and analyzed further in IDA. In this case, it is the final payload used by the SnappyBee malware.

Conclusion

Unpacking remains one of the most common anti-analysis techniques and is a feature of most sophisticated malware from threat groups. This technique of in-memory decryption reduces the forensic “surface area” of the malware, helping it to evade detection from anti-malware solutions. This blog walks through one such example and provides practical knowledge on how to unpack malware for deeper analysis.

In addition, this blog has detailed several other techniques used by threat actors to evade analysis, such as DLL sideloading to execute code without arising suspicion, dynamic API resolving to bypass static heuristics, and multiple nested stages to make analysis challenging.

Malware such as SnappyBee demonstrates a continued shift towards highly modular and low-friction malware toolkits that can be reused across many intrusions and campaigns. It remains vital for security teams to maintain the ability to combat the techniques seen in these toolkits when responding to infections.

While the technical details of these techniques are primarily important to analysts, the outcomes of this work directly affect how a Security Operations Centre (SOC) operates at scale. Without the technical capability to reliably unpack and observe these samples, organizations are forced to respond without the full picture.

The techniques demonstrated here help close that gap. This enables security teams to reduce dwell time by understanding the exact mechanisms of a sample earlier, improve detection quality with behavior-based indicators rather than relying on hash-based detections, and increase confidence in response decisions when determining impact.

Credit to Nathaniel Bill (Malware Research Engineer)
Edited by Ryan Traill (Analyst Content Lead)

Indicators of Compromise (IoCs)

SnappyBee Loader 1 - 25b9fdef3061c7dfea744830774ca0e289dba7c14be85f0d4695d382763b409b

SnappyBee Loader 2 - b2b617e62353a672626c13cc7ad81b27f23f91282aad7a3a0db471d84852a9ac

SnappyBee Payload - 1a38303fb392ccc5a88d236b4f97ed404a89c1617f34b96ed826e7bb7257e296

References

[1] https://www.trendmicro.com/en_gb/research/24/k/earth-estries.html

[2] https://www.darktrace.com/blog/salty-much-darktraces-view-on-a-recent-salt-typhoon-intrusion

[3] https://learn.microsoft.com/en-us/windows/win32/dlls/dllmain#parameters

[4] https://mbed-tls.readthedocs.io/projects/api/en/v2.28.4/api/file/arc4_8h/#_CPPv418mbedtls_arc4_cryptP20mbedtls_arc4_context6size_tPKhPh

About the author

Nathaniel Bill

Malware Research Engineer

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AI

February 4, 2026

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

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.

About the author

The Darktrace Community

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How Darktrace SOC Thwarted a BEC Attack

What is Business Email Compromise (BEC)?

What makes a BEC attack successful?

How does a BEC attack work?

How to protect against BEC attacks

Darktrace Mitigates BEC Attack

Conclusion

Appendices

MITRE ATT&CK Mapping

References

Darktrace Malware Analysis: Unpacking SnappyBee

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

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More in this series

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Blog

Network

February 3, 2026

Darktrace Malware Analysis: Unpacking SnappyBee

Introduction

First principles

Finding the unpacking routine

Conclusion

Indicators of Compromise (IoCs)

Blog

AI

February 4, 2026

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

What is the State of AI Cybersecurity 2026?

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

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

How is AI reshaping cybersecurity operations?

What impact is AI having on cybersecurity tools?