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December 7, 2021

Conti Ransomware Strategies in Modern Cybercrime

Uncover the strategies behind the Conti ransomware gang's double extortion methods and what it means for businesses facing cyber threats.
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Justin Fier
SVP, Red Team Operations
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07
Dec 2021

In a previous blog, we outlined how the Ryuk ransomware strain developed by Russian hacking group ‘Wizard Spider’ has fallen into the hands of small-time cyber criminals.

Wizard Spider – who allegedly operate with support from the Russian government and remain under investigation by the FBI and Interpol – adopted Ryuk ransomware’s successor ‘Conti’ in 2020. Conti affects all Windows operating systems and has been involved in more than 400 incidents. Wizard Spider were soon rebranded in cyber press as the ‘Conti Ransomware Gang’, though the group does not necessarily see itself as a ‘gang’. It prefers to present itself as a business.

The ransomware bubble

Ransomware has become a multibillion-dollar industry – and the Conti Ransomware Gang reportedly made up 15% of it in 2020. With this scale of income, groups like Conti find themselves adopting some crude imitations of legitimate business practice. This corporate mimicry dictates that their victims be called ‘customers’, their extortion attempts ‘negotiations’ and their criminal peers ‘affiliates’. They even publish ‘press releases’ via a dedicated Dark Web site.

The gang’s Ransomware-as-a-Service ‘business model’ consists of employing affiliates, training them in Conti ransomware’s deployment and management, and then taking 30% of the profits themselves. With exact profits known only to the malware writers and not the affiliates, however, the percentage Conti takes is often much higher than the 30% they claim.

There may not be checks and regulations in place to address fraud in the cyber underworld, but one business complication which Conti have not been able to escape is that of the disgruntled employee.

Unhappy with the malpractice of their superiors, an underpaid affiliate leaked the Conti Ransomware Gang’s training materials and the IP addresses for their Cobalt Strike C2 servers in August 2021, declaring, “they recruit suckers and divide the money among themselves”.

Meanwhile, the US Government has also been taking action to try to disrupt the profit margins of groups like the Conti Ransomware Gang, going as far as to impose sanctions on cryptocurrency exchanges seen as facilitating ransomware transactions. However, leaks and legislation have proved far from fatal for Conti.

The reality is that these actions have not lost the Conti Ransomware Gang any of its so-called “customers”, and where there are customers there is profit. Any individual or organization entrusting their cyber security to conventional, rules-based measures is in their target market.

Darktrace’s AI recently detected a Conti attack conducted along the lines of one of the methods outlined in the August leak. The target organization – a US transportation company – was trialing Darktrace but, without Darktrace’s Autonomous Response set in active mode, the attack was allowed to go ahead. In examining how it progressed, however, it should become clear not only how threatening double extortion ransomware attacks like this one can be, but also how effectively they can be stopped by Darktrace at each stage of the attack.

Figure 1: Timeline of the attack

Conti Ransomware Gang diversifies the ransomware playbook

A single uninstalled Microsoft patch had left the target organization with dangerous ProxyShell vulnerabilities. Conti exploited these vulnerabilities, quickly gaining the rights to remotely execute Exchange PowerShell commands on the company’s server and steadily broadened its presence within the digital environment. This is a relatively new approach for the Conti Ransomware Gang, who previously relied upon phishing attacks and firewall exploits. By diversifying its approach, it stays ahead of patches and intelligence.

Two weeks after the initial breach, C2 connections were made to an unusual endpoint located in Finland using an SSL client which appeared innocuous but was 100% rare for the organization. Had Autonomous Response been set in active mode, Darktrace would have shut the connections down at this very early stage.

The IP address of this suspicious endpoint has since been identified as a Conti IoC (Indicator of Compromise), allowing it to be incorporated into rules-based security solutions. This would have done little good for the company in question, however, which was breached weeks before this intelligence was made available.

As Conti continued to conduct internal reconnaissance and move laterally through the company’s digital environment, Darktrace detected further unusual activity. The suspicious Finnish endpoint then employed new ‘Living off the Land’ techniques, installing the usually legitimate tools AnyDesk and Cobalt Strike onto various parts of the environment.

A series of SSL connections were made to AnyDesk endpoints and external hosts, one of which lasted 95 hours, indicating an active remote session conducted by one of Conti’s affiliates. At this stage, Darktrace had 10 distinct reasons to suspect an imminent attack.

Conti News: Closing the deal with double extortion ransomware

Double extortion has become the Conti Ransomware Gang’s new favourite sales tactic. If you refuse to pay its ransom, Conti will not only take your most important files from you, but also exfiltrate and publish them using its dedicated ‘Conti News’ website, or sell them directly to your competitors.

Having expanded their reach across the transport company’s network, the Conti affiliate began rapidly exfiltrating large quantities of company data to Conti’s preferred cloud storage site, MEGA. Over four days, more than 3TB of data was uploaded, and then encrypted.

To avoid detection by a human security team, encryption was launched at close to midnight – Conti’s ‘business’ does not respect business hours. When the company’s security team returned to work the next day, they were met with a ransom note.

This attack was able to progress because Darktrace was only being trialed at this stage and was therefore allowed to detect threats but not to take action against them. With Autonomous Response employed in active mode, this ransomware attack would have ended in the very early stages, when Darktrace detected its first suspicious connections.

Nonetheless, the Cyber AI Analyst was able to investigate and connect the dots of the attack automatically, making the organization’s remediation efforts drastically quicker and easier than they would have been without even this partial Darktrace deployment.

Figure 2: Cyber AI Analyst generated this incident report following the initiation of data exfiltration

How the Conti Ransomware Gang evades cyber intelligence

Security systems that rely on human intelligence to detect threats fit Conti’s ideal customer profile perfectly. By adapting and diversifying their approach, moving from Ryuk to Conti, and from spear phishing and firewall exploits to this new ProxyShell approach, Conti stay ahead of regulations and hold on to their vulnerable customer base.

Even if the Conti Ransomware Gang is brought down by leaks or legislation, other groups will rise to fill the gap in the market, eager for their own cut of the illicit gains. If these groups are to be truly stopped, they must be made unprofitable.

The US government has tried to do this by imposing fines upon ransom payers, but companies still often consider the losses involved in not recovering their data too great. As I have argued previously, ‘to pay or not to pay,’ is not the question we should be asking.

If you’re deciding whether to pay or not to pay, you’re already too far down the line. Darktrace stops groups like Conti at the first encounter. As this case has shown, Darktrace’s Self-Learning AI is able to identify threats weeks before human analysts and threat intelligence can do the same, and neutralize them at every stage of an attack with Autonomous Response.

Thanks to Darktrace analyst Sam Lister for his insights on the above threat find.

Darktrace model detections:

  • Device / Long Agent Connection to New Endpoint
  • Device / ICMP Address Scan
  • Anomalous Connection / SMB Enumeration
  • Anomalous Server Activity / Outgoing from Server
  • Compromise / Beacon to Young Endpoint
  • Anomalous Server Activity / Rare External from Server
  • Compromise / Fast Beaconing to DGA
  • Compromise / SSL or HTTP Beacon
  • Compromise / Sustained SSL or HTTP Increase
  • Compromise / Beacon for 4 Days
  • Anomalous Connection / Multiple HTTP POSTs to Rare Hostname
  • Unusual Activity / Enhanced Unusual External Data Transfer
  • Anomalous Connection / Data Sent to Rare Domain
  • Anomalous Connection / Uncommon 1 GiB Outbound
  • Compliance / SMB Drive Write
  • Anomalous File / Internal / Additional Extension Appended to SMB File
  • Anomalous Connection / Suspicious Read Write Ratio
  • Anomalous Connection / Suspicious Read Write Ratio and Unusual SMB
  • Anomalous Connection / Sustained MIME Type Conversion
  • Unusual Activity / Anomalous SMB Move & Write
  • Unusual Activity / Unusual Internal Data Volume as Client or Server
  • Device / Suspicious File Writes to Multiple Hidden SMB Shares
  • Compromise / Ransomware / Suspicious SMB Activity
  • Anomalous File / Internal / Unusual SMB Script Write
  • Anomalous File / Internal / Masqueraded Executable SMB Write
  • Device / SMB Lateral Movement
  • Device / Multiple Lateral Movement Model Breaches

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
Justin Fier
SVP, Red Team Operations

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February 3, 2026

Darktrace Malware Analysis: Unpacking SnappyBee

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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.

The DllMain function
Figure 1: The DllMain function

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.

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

The memory decryption routine.
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

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Nathaniel Bill
Malware Research Engineer

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February 4, 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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