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Defending Tokyo Olympics: AI Neutralizes IoT Attack

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19
Sep 2021
19
Sep 2021
Learn how Darktrace autonomously thwarted a cyber-attack on a national sporting body before the Tokyo Olympics in this detailed breakdown.

One of the greatest issues in security is how to deal with high-stress scenarios when there is a significant breach, and there is too much to do in too little time. The nightmare scenario for any CISO is when this happens during a critical moment for the organization: an important acquisition, a crucial news announcement, or in this case, a global sporting event attracting an audience of millions.

Threat actors often exploit the pressure of these events to cause disruption or extract hefty sums. Sporting occasions, especially Formula 1 races, the Super Bowl, and the Olympics, attract a great deal of criminal interest.

The games begin

There have been several recorded attacks and data breaches at the Olympics this year, including an incident when a volleyball commentator asked his colleague for his computer password – not realizing he was still on air.

In a more nefarious case discovered by Darktrace, a Raspberry Pi device was covertly implanted into a national sporting body directly involved in the Olympics, in an attempt to exfiltrate sensitive data. The events took place one week before the start of the Games, and a data breach at this time would have had significant ramifications for the reputation of the organization, the confidentiality of their plans, and potentially the safety of their athletes.

Darktrace AI recognized this activity as malicious given its evolving understanding of ‘self’ for the organization, and Antigena – Darktrace’s autonomous response capability – took action at machine speed to interrupt the threat, affording the human security team the critical time they needed to catch up and neutralize the attack.

In what follows, we break down the attack.

Figure 1: The overall dwell time was three days.

Breaking down the attack

July 15, 14:09 — Initial intrusion

An unauthorized Raspberry Pi device connected to the organization’s digital environment – disguised and named in a way which mimicked the corporate naming convention. As a small IoT device, Raspberry Pis can be easily hidden and are difficult to locate physically in large environments. They have been used in various high-profile hacks in the past including the 2018 NASA breach.

IoT devices – from printers to fish tanks – pose a serious risk to security, as they can be exploited to gather information, move laterally, and escalate privileges.

July 15, 15:25 — External VPN activity

Anomalous UDP connections were made to an external endpoint over port 1194 (Open VPN activity). URIs showed that the device downloaded data potentially associated with Open VPN configuration files. This could represent an attempt to establish a secure channel for malicious activity such as data exfiltration.

By establishing an outgoing VPN, the attacker obfuscated their activity and bypassed the organization’s signature-based security, which could not detect the encrypted traffic. Antigena immediately blocked the suspicious connectivity, regardless of the encryption, identifying that the activity was a deviation from the ‘pattern of life’ for new devices.

July 15, 16:04 — Possible C2 activity

The Raspberry Pi soon began making repeated HTTP connections to a new external endpoint and downloaded octet streams — arbitrary binary data. It seems the activity was initiated by a standalone software process as opposed to a web browser.

Darktrace revealed that the device was performing an unusual external data transfer to the same endpoint, uploading 7.5 MB which likely contained call home data about the new location and name of the device.

July 15, 16:41 — Internal reconnaissance

The device engaged in TCP scanning across three unique internal IP addresses over a wide range of ports. Although the network scan only targeted three internal servers, the activity was identified by Darktrace as a suspicious increase in internal connections and failed internal connections.

Antigena instantly stopped the Raspberry Pi from making internal connections over the ports involved in the scanning activity, as well as enforcing the device’s ‘pattern of life’.

Figure 2: Device event log showing the components which enable Darktrace to detect network scanning.

July 15, 18:14 — Multiple internal reconnaissance tactics

The Raspberry Pi then scanned a large number of devices on SMB port 445 and engaged in suspicious use of the outdated SMB version 1 protocol, suggesting more in-depth reconnaissance to find exploitable vulnerabilities.

Reacting to the scanning activity alongside the insecure protocol SMBv1, Antigena blocked connections from the source device to the destination IPs for one hour.

Four minutes later, the device engaged in connections to the open-source vulnerability scanner, Nmap. Nmap can be used legitimately for vulnerability scanning and so often is not alerted to by traditional security tools. However, Darktrace’s AI detected that the use of the tool was highly anomalous, and so blocked all outgoing traffic for ten minutes.

July 15, 22:03 — Final reconnaissance

Three hours later, the Raspberry Pi initiated another network scan across six unique external IPs – this was in preparation for the final data exfiltration. Antigena responded with instant, specific blocks to the external IPs which the device was attempting to connect to – before any data could be exfiltrated.

After 30 minutes, Darktrace detected bruteforcing activity from the Raspberry Pi using the SMB and NTLM authentication protocols. The device made a large number of failed login attempts to a single internal device using over 100 unique user accounts. Antigena blocked the activity, successfully stopping another wave of attempted SMB lateral movement.

By this stage, Antigena had bought the security team enough time to respond. The team applied an Antigena quarantine rule (the most severe action Antigena can take) to the Raspberry Pi, until they were able to find the physical location of the device and unplug it from the network.

How AI Analyst stitched together the incident

Cyber AI Analyst autonomously reported on three key moments of the attack:

  • Unusual External Data Transfer
  • Possible HTTP Command and Control
  • TCP Scanning of Multiple Devices (the attempted data exfiltration)

It tied together activities over the span of multiple days, which could have been easily missed by human analysis. The AI provided crucial pieces of information, including the extent of the scanning activity. Such insights are time-consuming to calculate manually.

Figure 3: A screenshot from Cyber AI Analyst summarizing potential C2 activity.

Autonomous Response

Antigena took targeted action throughout to neutralize the suspicious behavior, while allowing normal business operations to continue unhindered.

Rather than widespread blocking, Antigena implemented a range of nuanced responses depending on the situation, always taking the smallest action necessary to deal with the threat.

Figure 4: Darktrace’s UI reveals the attempted network reconnaissance, and Antigena actions a targeted response. All IP addresses have been randomized.

Raspberry Pi: IoT threats

In an event involving 206 countries and 11,000 athletes, facing attacks from hacktivists, criminal groups, and nation states, with many broadcasters working remotely and millions watching from home, organizations involved in the Olympics needed a security solution which could rise to the occasion.

Even with the largest affairs, threats can come from the smallest places. The ability to detect unauthorized IoT devices and maintain visibility over all activity in your digital estate is essential.

Autonomous Response protects against the unexpected, stopping malicious activity at machine speed without any user input. This is necessary for rapid response and remediation, especially for resource-stretched internal security teams. When it comes to defending systems and outpacing attackers, AI always wins the race.

Thanks to Darktrace analysts Emma Foulger and Greg Chapman for their insights on the above threat find.

Learn how two rogue Raspberry Pi devices infected a healthcare provider

Darktrace model detections:

  • Compromise / Ransomware / Suspicious SMB Activity
  • Tags / New Raspberry Pi Device
  • Device / Network Scan
  • Unusual Activity / Unusual Raspberry Pi Activity
  • Antigena / Network / Insider Threat / Antigena Network Scan Block
  • Device / Suspicious Network Scan Activity
  • Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block
  • Antigena / Network / Significant Anomaly / Antigena Controlled and Model Breach
  • Device / Suspicious SMB Scanning Activity
  • Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block
  • Device / Attack and Recon Tools
  • Device / New Device with Attack Tools
  • Device / Anomalous Nmap Activity
  • Device / External Network Scan
  • Device / SMB Session Bruteforce
  • Antigena / Network / Manual / Block All Outgoing Connections
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.
AUTHOR
ABOUT ThE AUTHOR
Oakley Cox
Director of Product

Oakley is a Product Manager within the Darktrace R&D team. He collaborates with global customers, including all critical infrastructure sectors and Government agencies, to ensure Darktrace/OT remains the first in class solution for OT Cyber Security. He draws on 7 years’ experience as a Cyber Security Consultant to organizations across EMEA, APAC and ANZ. His research into cyber-physical security has been published by Cyber Security journals and by CISA. Oakley has a Doctorate (PhD) from the University of Oxford.

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Inside the SOC

Disarming the WarmCookie Backdoor: Darktrace’s Oven-Ready Solution

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26
Jul 2024

What is WarmCookie malware?

WarmCookie, also known as BadSpace [2], is a two-stage backdoor tool that provides functionality for threat actors to retrieve victim information and launch additional payloads. The malware is primarily distributed via phishing campaigns according to multiple open-source intelligence (OSINT) providers.

Backdoor malware: A backdoor tool is a piece of software used by attackers to gain and maintain unauthorized access to a system. It bypasses standard authentication and security mechanisms, allowing the attacker to control the system remotely.

Two-stage backdoor malware: This means the backdoor operates in two distinct phases:

1. Initial Stage: The first stage involves the initial infection and establishment of a foothold within the victim's system. This stage is often designed to be small and stealthy to avoid detection.

2. Secondary Stage: Once the initial stage has successfully compromised the system, it retrieves or activates the second stage payload. This stage provides more advanced functionalities for the attacker, such as extensive data exfiltration, deeper system control, or the deployment of additional malicious payloads.

How does WarmCookie malware work?

Reported attack patterns include emails attempting to impersonate recruitment firms such as PageGroup, Michael Page, and Hays. These emails likely represented social engineering tactics, with attackers attempting to manipulate jobseekers into engaging with the emails and following malicious links embedded within [3].

This backdoor tool also adopts stealth and evasion tactics to avoid the detection of traditional security tools. Reported evasion tactics included custom string decryption algorithms, as well as dynamic API loading to prevent researchers from analyzing and identifying the core functionalities of WarmCookie [1].

Before this backdoor makes an outbound network request, it is known to capture details from the target machine, which can be used for fingerprinting and identification [1], this includes:

- Computer name

- Username

- DNS domain of the machine

- Volume serial number

WarmCookie samples investigated by external researchers were observed communicating communicated over HTTP to a hardcoded IP address using a combination of RC4 and Base64 to protect its network traffic [1]. Ultimately, threat actors could use this backdoor to deploy further malicious payloads on targeted networks, such as ransomware.

Darktrace Coverage of WarmCookie

Between April and June 2024, Darktrace’s Threat Research team investigated suspicious activity across multiple customer networks indicating that threat actors were utilizing the WarmCookie backdoor tool. Observed cases across customer environments all included the download of unusual executable (.exe) files and suspicious outbound connectivity.

Affected devices were all observed making external HTTP requests to the German-based external IP, 185.49.69[.]41, and the URI, /data/2849d40ade47af8edfd4e08352dd2cc8.

The first investigated instance occurred between April 23 and April 24, when Darktrace detected a a series of unusual file download and outbound connectivity on a customer network, indicating successful WarmCookie exploitation. As mentioned by Elastic labs, "The PowerShell script abuses the Background Intelligent Transfer Service (BITS) to download WarmCookie and run the DLL with the Start export" [1].

Less than a minute later, the same device was observed making HTTP requests to the rare external IP address: 185.49.69[.]41, which had never previously been observed on the network, for the URI /data/b834116823f01aeceed215e592dfcba7. The device then proceeded to download masqueraded executable file from this endpoint. Darktrace recognized that these connections to an unknown endpoint, coupled with the download of a masqueraded file, likely represented malicious activity.

Following this download, the device began beaconing back to the same IP, 185.49.69[.]41, with a large number of external connections observed over port 80.  This beaconing related behavior could further indicate malicious software communicating with command-and-control (C2) servers.

Darktrace’s model alert coverage included the following details:

[Model Alert: Device / Unusual BITS Activity]

- Associated device type: desktop

- Time of alert: 2024-04-23T14:10:23 UTC

- ASN: AS28753 Leaseweb Deutschland GmbH

- User agent: Microsoft BITS/7.8

[Model Alert: Anomalous File / EXE from Rare External Location]

[Model Alert: Anomalous File / Masqueraded File Transfer]

- Associated device type: desktop

- Time of alert: 2024-04-23T14:11:18 UTC

- Destination IP: 185.49.69[.]41

- Destination port: 80

- Protocol: TCP

- Application protocol: HTTP

- ASN: AS28753 Leaseweb Deutschland GmbH

- User agent: Mozilla / 4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1;.NET CLR 1.0.3705)

- Event details: File: http[:]//185.49.69[.]41/data/b834116823f01aeceed215e592dfcba7, total seen size: 144384B, direction: Incoming

- SHA1 file hash: 4ddf0d9c750bfeaebdacc14152319e21305443ff

- MD5 file hash: b09beb0b584deee198ecd66976e96237

[Model Alert: Compromise / Beaconing Activity To External Rare]

- Associated device type: desktop

- Time of alert: 2024-04-23T14:15:24 UTC

- Destination IP: 185.49.69[.]41

- Destination port: 80

- Protocol: TCP

- Application protocol: HTTP

- ASN: AS28753 Leaseweb Deutschland GmbH  

- User agent: Mozilla / 4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1;.NET CLR 1.0.3705)

Between May 7 and June 4, Darktrace identified a wide range of suspicious external connectivity on another customer’s environment. Darktrace’s Threat Research team further investigated this activity and assessed it was likely indicative of WarmCookie exploitation on customer devices.

Similar to the initial use case, BITS activity was observed on affected devices, which is utilized to download WarmCookie [1]. This initial behavior was observed with the device after triggering the model: Device / Unusual BITS Activity on May 7.

Just moments later, the same device was observed making HTTP requests to the aforementioned German IP address, 185.49.69[.]41 using the same URI /data/2849d40ade47af8edfd4e08352dd2cc8, before downloading a suspicious executable file.

Just like the first use case, this device followed up this suspicious download with a series of beaconing connections to 185.49.69[.]41, again with a large number of connections via port 80.

Similar outgoing connections to 185.49.69[.]41 and model alerts were observed on additional devices during the same timeframe, indicating that numerous customer devices had been compromised.

Darktrace’s model alert coverage included the following details:

[Model Alert: Device / Unusual BITS Activity]

- Associated device type: desktop

- Time of alert: 2024-05-07T09:03:23 UTC

- ASN: AS28753 Leaseweb Deutschland GmbH

- User agent: Microsoft BITS/7.8

[Model Alert: Anomalous File / EXE from Rare External Location]

[Model Alert: Anomalous File / Masqueraded File Transfer]

- Associated device type: desktop

- Time of alert: 2024-05-07T09:03:35 UTC  

- Destination IP: 185.49.69[.]41

- Protocol: TCP

- ASN: AS28753 Leaseweb Deutschland GmbH

- Event details: File: http[:]//185.49.69[.]41/data/2849d40ade47af8edfd4e08352dd2cc8, total seen size: 72704B, direction: Incoming

- SHA1 file hash: 5b0a35c574ee40c4bccb9b0b942f9a9084216816

- MD5 file hash: aa9a73083184e1309431b3c7a3e44427  

[Model Alert: Anomalous Connection / New User Agent to IP Without Hostname]

- Associated device type: desktop

- Time of alert: 2024-05-07T09:04:14 UTC  

- Destination IP: 185.49.69[.]41  

- Application protocol: HTTP  

- URI: /data/2849d40ade47af8edfd4e08352dd2cc8

- User agent: Microsoft BITS/7.8  

[Model Alert: Compromise / HTTP Beaconing to New Endpoint]

- Associated device type: desktop

- Time of alert: 2024-05-07T09:08:47 UTC

- Destination IP: 185.49.69[.]41

- Protocol: TCP

- Application protocol: HTTP  

- ASN: AS28753 Leaseweb Deutschland GmbH  

- URI: /  

- User agent: Mozilla / 4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1;.NET CLR 1.0.3705) \

Cyber AI Analyst Coverage Details around the external destination, ‘185.49.69[.]41’.
Figure 1: Cyber AI Analyst Coverage Details around the external destination, ‘185.49.69[.]41’.
External Sites Summary verifying the geographical location of the external IP, 185.49.69[.]41’.
Figure 2: External Sites Summary verifying the geographical location of the external IP, 185.49.69[.]41’.

Fortunately, this particular customer was subscribed to Darktrace’s Proactive Threat Notification (PTN) service and the Darktrace Security Operation Center (SOC) promptly investigated the activity and alerted the customer. This allowed their security team to address the activity and begin their own remediation process.

In this instance, Darktrace’s Autonomous Response capability was configured in Human Confirmation mode, meaning any mitigative actions required manual application by the customer’s security team.

Despite this, Darktrace recommended two actions to contain the activity: blocking connections to the suspicious IP address 185.49.69[.]41 and any IP addresses ending with '69[.]41', as well as the ‘Enforce Pattern of Life’ action. By enforcing a pattern of life, Darktrace can restrict a device (or devices) to its learned behavior, allowing it to continue regular business activities uninterrupted while blocking any deviations from expected activity.

Actions suggested by Darktrace to contain the emerging activity, including blocking connections to the suspicious endpoint and restricting the device to its ‘pattern of life’.
Figure 3: Actions suggested by Darktrace to contain the emerging activity, including blocking connections to the suspicious endpoint and restricting the device to its ‘pattern of life’.

Conclusion

Backdoor tools like WarmCookie enable threat actors to gather and leverage information from target systems to deploy additional malicious payloads, escalating their cyber attacks. Given that WarmCookie’s primary distribution method seems to be through phishing campaigns masquerading as trusted recruitments firms, it has the potential to affect a large number of organziations.

In the face of such threats, Darktrace’s behavioral analysis provides organizations with full visibility over anomalous activity on their digital estates, regardless of whether the threat bypasses by human security teams or email security tools. While threat actors seemingly managed to evade customers’ native email security and gain access to their networks in these cases, Darktrace identified the suspicious behavior associated with WarmCookie and swiftly notified customer security teams.

Had Darktrace’s Autonomous Response capability been fully enabled in these cases, it could have blocked any suspicious connections and subsequent activity in real-time, without the need of human intervention, effectively containing the attacks in the first instance.

Credit to Justin Torres, Cyber Security Analyst and Dylan Hinz, Senior Cyber Security Analyst

Appendices

Darktrace Model Detections

- Anomalous File / EXE from Rare External Location

- Anomalous File / Masqueraded File Transfer  

- Compromise / Beacon to Young Endpoint  

- Compromise / Beaconing Activity To External Rare  

- Compromise / HTTP Beaconing to New Endpoint  

- Compromise / HTTP Beaconing to Rare Destination

- Compromise / High Volume of Connections with Beacon Score

- Compromise / Large Number of Suspicious Successful Connections

- Compromise / Quick and Regular Windows HTTP Beaconing

- Compromise / SSL or HTTP Beacon

- Compromise / Slow Beaconing Activity To External Rare

- Compromise / Sustained SSL or HTTP Increase

- Compromise / Sustained TCP Beaconing Activity To Rare Endpoint

- Anomalous Connection / Multiple Failed Connections to Rare Endpoint

- Anomalous Connection / New User Agent to IP Without Hostname

- Compromise / Sustained SSL or HTTP Increase

AI Analyst Incident Coverage:

- Unusual Repeated Connections

- Possible SSL Command and Control to Multiple Endpoints

- Possible HTTP Command and Control

- Suspicious File Download

Darktrace RESPOND Model Detections:

- Antigena / Network / External Threat / Antigena Suspicious File Block

- Antigena / Network / External Threat / Antigena Suspicious File Pattern of Life Block

List of IoCs

IoC - Type - Description + Confidence

185.49.69[.]41 – IP Address – WarmCookie C2 Endpoint

/data/2849d40ade47af8edfd4e08352dd2cc8 – URI – Likely WarmCookie URI

/data/b834116823f01aeceed215e592dfcba7 – URI – Likely WarmCookie URI

4ddf0d9c750bfeaebdacc14152319e21305443ff  - SHA1 Hash  – Possible Malicious File

5b0a35c574ee40c4bccb9b0b942f9a9084216816  - SHA1 Hash – Possiblem Malicious File

MITRE ATT&CK Mapping

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

Drive-by Compromise - INITIAL ACCESS - T1189

Ingress Tool Transfer - COMMAND AND CONTROL - T1105

Malware - RESOURCE DEVELOPMENT - T1588.001 - T1588

Lateral Tool Transfer - LATERAL MOVEMENT - T1570

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

Web Services - RESOURCE DEVELOPMENT - T1583.006 - T1583

Browser Extensions - PERSISTENCE - T1176

Application Layer Protocol - COMMAND AND CONTROL - T1071

Fallback Channels - COMMAND AND CONTROL - T1008

Multi-Stage Channels - COMMAND AND CONTROL - T1104

Non-Standard Port - COMMAND AND CONTROL - T1571

One-Way Communication - COMMAND AND CONTROL - T1102.003 - T1102

Encrypted Channel - COMMAND AND CONTROL - T1573

External Proxy - COMMAND AND CONTROL - T1090.002 - T1090

Non-Application Layer Protocol - COMMAND AND CONTROL - T1095

References

[1] https://www.elastic.co/security-labs/dipping-into-danger

[2] https://www.gdatasoftware.com/blog/2024/06/37947-badspace-backdoor

[3] https://thehackernews.com/2024/06/new-phishing-campaign-deploys.html

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Justin Torres
Cyber Analyst

Blog

Thought Leadership

The State of AI in Cybersecurity: Understanding AI Technologies

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24
Jul 2024

About the State of AI Cybersecurity Report

Darktrace surveyed 1,800 CISOs, security leaders, administrators, and practitioners from industries around the globe. Our research was conducted to understand how the adoption of new AI-powered offensive and defensive cybersecurity technologies are being managed by organizations.

This blog continues the conversation from “The State of AI in Cybersecurity: Unveiling Global Insights from 1,800 Security Practitioners”. This blog will focus on security professionals’ understanding of AI technologies in cybersecurity tools.

To access download the full report, click here.

How familiar are security professionals with supervised machine learning

Just 31% of security professionals report that they are “very familiar” with supervised machine learning.

Many participants admitted unfamiliarity with various AI types. Less than one-third felt "very familiar" with the technologies surveyed: only 31% with supervised machine learning and 28% with natural language processing (NLP).

Most participants were "somewhat" familiar, ranging from 46% for supervised machine learning to 36% for generative adversarial networks (GANs). Executives and those in larger organizations reported the highest familiarity.

Combining "very" and "somewhat" familiar responses, 77% had familiarity with supervised machine learning, 74% generative AI, and 73% NLP. With generative AI getting so much media attention, and NLP being the broader area of AI that encompasses generative AI, these results may indicate that stakeholders are understanding the topic on the basis of buzz, not hands-on work with the technologies.  

If defenders hope to get ahead of attackers, they will need to go beyond supervised learning algorithms trained on known attack patterns and generative AI. Instead, they’ll need to adopt a comprehensive toolkit comprised of multiple, varied AI approaches—including unsupervised algorithms that continuously learn from an organization’s specific data rather than relying on big data generalizations.  

Different types of AI

Different types of AI have different strengths and use cases in cyber security. It’s important to choose the right technique for what you’re trying to achieve.  

Supervised machine learning: Applied more often than any other type of AI in cyber security. Trained on human attack patterns and historical threat intelligence.  

Large language models (LLMs): Applies deep learning models trained on extremely large data sets to understand, summarize, and generate new content. Used in generative AI tools.  

Natural language processing (NLP): Applies computational techniques to process and understand human language.  

Unsupervised machine learning: Continuously learns from raw, unstructured data to identify deviations that represent true anomalies.  

What impact will generative AI have on the cybersecurity field?

More than half of security professionals (57%) believe that generative AI will have a bigger impact on their field over the next few years than other types of AI.

Chart showing the types of AI expected to impact security the most
Figure 1: Chart from Darktrace's State of AI in Cybersecurity Report

Security stakeholders are highly aware of generative AI and LLMs, viewing them as pivotal to the field's future. Generative AI excels at abstracting information, automating tasks, and facilitating human-computer interaction. However, LLMs can "hallucinate" due to training data errors and are vulnerable to prompt injection attacks. Despite improvements in securing LLMs, the best cyber defenses use a mix of AI types for enhanced accuracy and capability.

AI education is crucial as industry expectations for generative AI grow. Leaders and practitioners need to understand where and how to use AI while managing risks. As they learn more, there will be a shift from generative AI to broader AI applications.

Do security professionals fully understand the different types of AI in security products?

Only 26% of security professionals report a full understanding of the different types of AI in use within security products.

Confusion is prevalent in today’s marketplace. Our survey found that only 26% of respondents fully understand the AI types in their security stack, while 31% are unsure or confused by vendor claims. Nearly 65% believe generative AI is mainly used in cybersecurity, though it’s only useful for identifying phishing emails. This highlights a gap between user expectations and vendor delivery, with too much focus on generative AI.

Key findings include:

  • Executives and managers report higher understanding than practitioners.
  • Larger organizations have better understanding due to greater specialization.

As AI evolves, vendors are rapidly introducing new solutions faster than practitioners can learn to use them. There's a strong need for greater vendor transparency and more education for users to maximize the technology's value.

To help ease confusion around AI technologies in cybersecurity, Darktrace has released the CISO’s Guide to Cyber AI. A comprehensive white paper that categorizes the different applications of AI in cybersecurity. Download the White Paper here.  

Do security professionals believe generative AI alone is enough to stop zero-day threats?

No! 86% of survey participants believe generative AI alone is NOT enough to stop zero-day threats

This consensus spans all geographies, organization sizes, and roles, though executives are slightly less likely to agree. Asia-Pacific participants agree more, while U.S. participants agree less.

Despite expecting generative AI to have the most impact, respondents recognize its limited security use cases and its need to work alongside other AI types. This highlights the necessity for vendor transparency and varied AI approaches for effective security across threat prevention, detection, and response.

Stakeholders must understand how AI solutions work to ensure they offer advanced, rather than outdated, threat detection methods. The survey shows awareness that old methods are insufficient.

To access the full report, click here.

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