July 18, 2023

Understanding Email Security & the Psychology of Trust

We explore how psychological research into the nature of trust relates to our relationship with technology - and what that means for AI solutions.

Written by

Hanah Darley

Director of Threat Research

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

Hanah Darley

Director of Threat Research

Photo showing woman logging into her laptop with username and password

Jul 2023

When security teams discuss the possibility of phishing attacks targeting their organization, often the first reaction is to assume it is inevitable because of the users. Users are typically referenced in cyber security conversations as organizations’ greatest weaknesses, cited as the causes of many grave cyber-attacks because they click links, open attachments, or allow multi-factor authentication bypass without verifying the purpose.

While for many, the weakness of the user may feel like a fact rather than a theory, there is significant evidence to suggest that users are psychologically incapable of protecting themselves from exploitation by phishing attacks, with or without regular cyber awareness trainings. The psychology of trust and the nature of human reliance on technology make the preparation of users for the exploitation of that trust in technology very difficult – if not impossible.

This Darktrace long read will highlight principles of psychological and sociological research regarding the nature of trust, elements of the trust that relate to technology, and how the human brain is wired to rely on implicit trust. These principles all point to the outcome that humans cannot be relied upon to identify phishing. Email security driven by machine augmentation, such as AI anomaly detection, is the clearest solution to tackle that challenge.

What is the psychology of trust?

Psychological and sociological theories on trust largely centre around the importance of dependence and a two-party system: the trustor and the trustee. Most research has studied the impacts of trust decisions on interpersonal relationships, and the characteristics which make those relationships more or less likely to succeed. In behavioural terms, the elements most frequently referenced in trust decisions are emotional characteristics such as benevolence, integrity, competence, and predictability.¹

Most of the behavioural evaluations of trust decisions survey why someone chooses to trust another person, how they made that decision, and how quickly they arrived at their choice. However, these micro-choices about trust require the context that trust is essential to human survival. Trust decisions are rooted in many of the same survival instincts which require the brain to categorize information and determine possible dangers. More broadly, successful trust relationships are essential in maintaining the fabric of human society, critical to every element of human life.

Trust can be compared to dark matter (Rotenberg, 2018), which is the extensive but often difficult to observe material that binds planets and earthly matter. In the same way, trust is an integral but often a silent component of human life, connecting people and enabling social functioning.²

Defining implicit and routine trust

As briefly mentioned earlier, dependence is an essential element of the trusting relationship. Being able to build a routine of trust, based on the maintenance rather than establishment of trust, becomes implicit within everyday life. For example, speaking to a friend about personal issues and life developments is often a subconscious reaction to the events occurring, rather than an explicit choice to trust said friend each time one has new experiences.

Active and passive levels of cognition are important to recognize in decision-making, such as trust choices. Decision-making is often an active cognitive process requiring a lot of resource from the brain. However, many decisions occur passively, especially if they are not new choices e.g. habits or routines. The brain’s focus turns to immediate tasks while relegating habitual choices to subconscious thought processes, passive cognition. Passive cognition leaves the brain open to impacts from inattentional blindness, wherein the individual may be abstractly aware of the choice but it is not the focus of their thought processes or actively acknowledged as a decision. These levels of cognition are mostly referenced as “attention” within the brain’s cognition and processing.³

This idea is essentially a concept of implicit trust, meaning trust which is occurring as background thought processes rather than active decision-making. This implicit trust extends to multiple areas of human life, including interpersonal relationships, but also habitual choice and lifestyle. When combined with the dependence on people and services, this implicit trust creates a haze of cognition where trust is implied and assumed, rather than actively chosen across a myriad of scenarios.

Trust and technology

As researchers at the University of Cambridge highlight in their research into trust and technology, ‘In a fundamental sense, all technology depends on trust.’ The same implicit trust systems which allow us to navigate social interactions by subconsciously choosing to trust, are also true of interactions with technology. The implied trust in technology and services is perhaps most easily explained by a metaphor.

Most people have a favourite brand of soda. People will routinely purchase that soda and drink it without testing it for chemicals or bacteria and without reading reviews to ensure the companies that produce it have not changed their quality standards. This is a helpful, representative example of routine trust, wherein the trust choice is implicit through habitual action and does not mean the person is actively thinking about the ramifications of continuing to use a product and trust it.

The principle of dependence is especially important in trust and technology discussions, because the modern human is entirely reliant on technology and so has no way to avoid trusting it.⁵ Specifically important in workplace scenarios, employees are given a mandatory set of technologies, from programs to devices and services, which they must interact with on a daily basis. Over time, the same implicit trust that would form between two people forms between the user and the technology. The key difference between interpersonal trust and technological trust is that deception is often much more difficult to identify.

The implicit trust in workplace technology

To provide a bit of workplace-specific context, organizations rely on technology providers for the operation (and often the security) of their devices. The organizations also rely on the employees (users) to use those technologies within the accepted policies and operational guidelines. The employees rely on the organization to determine which products and services are safe or unsafe.

Within this context, implicit trust is occurring at every layer of the organization and its technological holdings, but often the trust choice is only made annually by a small security team rather than continually evaluated. Systems and programs remain in place for years and are used because “that’s the way it’s always been done. Within that context, the exploitation of that trust by threat actors impersonating or compromising those technologies or services is extremely difficult to identify as a human.

For example, many organizations utilize email communications to promote software updates for employees. Typically, it would consist of email prompting employees to update versions from the vendors directly or from public marketplaces, such as App Store on Mac or Microsoft Store for Windows. If that kind of email were to be impersonated, spoofing an update and including a malicious link or attachment, there would be no reason for the employee to question that email, given the explicit trust enforced through habitual use of that service and program.

Inattentional blindness: How the brain ignores change

Users are psychologically predisposed to trust routinely used technologies and services, with most of those trust choices continuing subconsciously. Changes to these technologies would often be subject to inattentional blindness, a psychological phenomenon wherein the brain either overwrites sensory information with what the brain expects to see rather than what is actually perceived.

A great example of inattentional blindness⁶ is the following experiment, which asks individuals to count the number of times a ball is passed between multiple people. While that is occurring, something else is going on in the background, which, statistically, those tested will not see. The shocking part of this experiment comes after, when the researcher reveals that the event occurring in the background not seen by participants was a person in a gorilla suit moving back and forth between the group. This highlights how significant details can be overlooked by the brain and “overwritten” with other sensory information. When applied to technology, inattentional blindness and implicit trust makes spotting changes in behaviour, or indicators that a trusted technology or service has been compromised, nearly impossible for most humans to detect.

With all this in mind, how can you prepare users to correctly anticipate or identify a violation of that trust when their brains subconsciously make trust decisions and unintentionally ignore cues to suggest a change in behaviour? The short answer is, it’s difficult, if not impossible.

How threats exploit our implicit trust in technology

Most cyber threats are built around the idea of exploiting the implicit trust humans place in technology. Whether it’s techniques like “living off the land”, wherein programs normally associated with expected activities are leveraged to execute an attack, or through more overt psychological manipulation like phishing campaigns or scams, many cyber threats are predicated on the exploitation of human trust, rather than simply avoiding technological safeguards and building backdoors into programs.

In the case of phishing, it is easy to identify the attempts to leverage the trust of users in technology and services. The most common example of this would be spoofing, which is one of the most common tactics observed by Darktrace/Email. Spoofing is mimicking a trusted user or service, and can be accomplished through a variety of mechanisms, be it the creation of a fake domain meant to mirror a trusted link type, or the creation of an email account which appears to be a Human Resources, Internal Technology or Security service.

In the case of a falsified internal service, often dubbed a “Fake Support Spoof”, the user is exploited by following instructions from an accepted organizational authority figure and service provider, whose actions should normally be adhered to. These cases are often difficult to spot when studying the sender’s address or text of the email alone, but are made even more difficult to detect if an account from one of those services is compromised and the sender’s address is legitimate and expected for correspondence. Especially given the context of implicit trust, detecting deception in these cases would be extremely difficult.

How email security solutions can solve the problem of implicit trust

How can an organization prepare for this exploitation? How can it mitigate threats which are designed to exploit implicit trust? The answer is by using email security solutions that leverage behavioural analysis via anomaly detection, rather than traditional email gateways.

Expecting humans to identify the exploitation of their own trust is a high-risk low-reward endeavour, especially when it takes different forms, affects different users or portions of the organization differently, and doesn’t always have obvious red flags to identify it as suspicious. Cue email security using anomaly detection as the key answer to this evolving problem.

Anomaly detection enabled by machine learning and artificial intelligence (AI) removes the inattentional blindness that plagues human users and security teams and enables the identification of departures from the norm, even those designed to mimic expected activity. Using anomaly detection mitigates multiple human cognitive biases which might prevent teams from identifying evolving threats, and also guarantees that all malicious behaviour will be detected. Of course, anomaly detection means that security teams may be alerted to benign anomalous activity, but still guarantees that no threat, no matter how novel or cleverly packaged, won’t be identified and raised to the human security team.

Utilizing machine learning, especially unsupervised machine learning, mimics the benefits of human decision making and enables the identification of patterns and categorization of information without the framing and biases which allow trust to be leveraged and exploited.

For example, say a cleverly written email is sent from an address which appears to be a Microsoft affiliate, suggesting to the user that they need to patch their software due to the discovery of a new vulnerability. The sender’s address appears legitimate and there are news stories circulating on major media providers that a new Microsoft vulnerability is causing organizations a lot of problems. The link, if clicked, forwards the user to a login page to verify their Microsoft credentials before downloading the new version of the software. After logging in, the program is available for download, and only requires a few minutes to install. Whether this email was created by a service like ChatGPT (generative AI) or written by a person, if acted upon it would give the threat actor(s) access to the user’s credential and password as well as activate malware on the device and possibly broader network if the software is downloaded.

If we are relying on users to identify this as unusual, there are a lot of evidence points that enforce their implicit trust in Microsoft services that make them want to comply with the email rather than question it. Comparatively, anomaly detection-driven email security would flag the unusualness of the source, as it would likely not be coming from a Microsoft-owned IP address and the sender would be unusual for the organization, which does not normally receive mail from the sender. The language might indicate solicitation, an attempt to entice the user to act, and the link could be flagged as it contains a hidden redirect or tailored information which the user cannot see, whether it is hidden beneath text like “Click Here” or due to link shortening. All of this information is present and discoverable in the phishing email, but often invisible to human users due to the trust decisions made months or even years ago for known products and services.

AI-driven Email Security: The Way Forward

Email security solutions employing anomaly detection are critical weapons for security teams in the fight to stay ahead of evolving threats and varied kill chains, which are growing more complex year on year. The intertwining nature of technology, coupled with massive social reliance on technology, guarantees that implicit trust will be exploited more and more, giving threat actors a variety of avenues to penetrate an organization. The changing nature of phishing and social engineering made possible by generative AI is just a drop in the ocean of the possible threats organizations face, and most will involve a trusted product or service being leveraged as an access point or attack vector. Anomaly detection and AI-driven email security are the most practical solution for security teams aiming to prevent, detect, and mitigate user and technology targeting using the exploitation of trust.

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References

¹https://www.kellogg.northwestern.edu/trust-project/videos/waytz-ep-1.aspx

²Rotenberg, K.J. (2018). The Psychology of Trust. Routledge.

³https://www.cognifit.com/gb/attention

⁴https://www.trusttech.cam.ac.uk/perspectives/technology-humanity-society-democracy/what-trust-technology-conceptual-bases-common

⁵Tyler, T.R. and Kramer, R.M. (2001). Trust in organizations : frontiers of theory and research. Thousand Oaks U.A.: Sage Publ, pp.39–49.

⁶https://link.springer.com/article/10.1007/s00426-006-0072-4

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Written by

Hanah Darley

Director of Threat Research

Inside the SOC

Written by

Hanah Darley

Director of Threat Research

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 3, 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.

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References

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

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