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June 25, 2024

Following up on our Conversation: Detecting & Containing a LinkedIn Phishing Attack with Darktrace

Darktrace/Email detected a phishing attack that had originated from LinkedIn, where the attacker impersonated a well known construction company to conduct a credential harvesting attack on the target. Darktrace’s ActiveAI Security Platform played a critical role in investigating the activity and initiating real-time responses that were outside the physical capability of human security teams.
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Nicole Wong
Cyber Security Analyst
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25
Jun 2024

Note: Real organization, domain and user names have been modified and replaced with fictitious names to maintain anonymity.  

Social media cyber-attacks

Social media is a known breeding ground for cyber criminals to easily connect with a near limitless number of people and leverage the wealth of personal information shared on these platforms to defraud the general public.  Analysis suggests even the most tech savvy ‘digital natives’ are vulnerable to impersonation scams over social media, as criminals weaponize brands and trends, using the promise of greater returns to induce sensitive information sharing or fraudulent payments [1].

LinkedIn phishing

As the usage of a particular social media platform increases, cyber criminals will find ways to exploit the increasing user base, and this trend has been observed with the rise in LinkedIn scams in recent years [2].  LinkedIn is the dominant professional networking site, with a forecasted 84.1million users by 2027 [3].  This platform is data-driven, so users are encouraged to share information publicly, including personal life updates, to boost visibility and increase job prospects [4] [5].  While this helps legitimate recruiters to gain a good understanding of the user, an attacker could also leverage the same personal content to increase the sophistication and success of their social engineering attempts.  

Darktrace detection of LinkedIn phishing

Darktrace detected a Software-as-a-Service (SaaS) compromise affecting a construction company, where the attack vector originated from LinkedIn (outside the monitoring of corporate security tools), but then pivoted to corporate email where a credential harvesting payload was delivered, providing the attacker with credentials to access a corporate file storage platform.  

Because LinkedIn accounts are typically linked to an individual’s personal email and are most commonly accessed via the mobile application [6] on personal devices that are not monitored by security teams, it can represent an effective initial access point for attackers looking to establish an initial relationship with their target. Moreover, user behaviors to ignore unsolicited emails from new or unknown contacts are less frequently carried over to platforms like LinkedIn, where interactions with ‘weak ties’ as opposed to ‘strong ties’ are a better predictor of job mobility [7]. Had this attack been allowed to continue, the threat actor could have leveraged access to further information from the compromised business cloud account to compromise other high value accounts, exfiltrate sensitive data, or defraud the organization.

LinkedIn phishing attack details

Reconnaissance

The initial reconnaissance and social engineering occurred on LinkedIn and was thus outside the purview of corporate security tools, Darktrace included.

However, the email domain “hausconstruction[.]com” used by the attacker in subsequent communications appears to be a spoofed domain impersonating a legitimate construction company “haus[.]com”, suggesting the attacker may have also impersonated an employee of this construction company on LinkedIn.  In addition to spoofing the domain, the attacker seemingly went further to register “hausconstruction.com” on a commercial web hosting platform.  This is a technique used frequently not just to increase apparent legitimacy, but also to bypass traditional security tools since newly registered domains will have no prior threat intelligence, making them more likely to evade signature and rules-based detections [8].  In this instance, open-source intelligence (OSINT) sources report that the domain was created several months earlier, suggesting this may have been part of a targeted attack on construction companies.  

Initial Intrusion

It was likely that during the correspondence over LinkedIn, the target user was solicited into following up over email regarding a prospective construction project, using their corporate email account.  In a probable attempt to establish a precedent of bi-directional correspondence so that subsequent malicious emails would not be flagged by traditional security tools, the attacker did not initially include suspicious links, attachments or use solicitous or inducive language within their initial emails.

Example of bi-directional email correspondence between the target and the attacker impersonating a legitimate employee of the construction company haus.com.
Figure 1: Example of bi-directional email correspondence between the target and the attacker impersonating a legitimate employee of the construction company haus.com.
Cyber AI Analyst investigation into one of the initial emails the target received from the attacker.
Figure 2: Cyber AI Analyst investigation into one of the initial emails the target received from the attacker.  

To accomplish the next stage of their attack, the attacker shared a link, hidden behind the inducing text “VIEW ALL FILES”, to a malicious file using the Hightail cloud storage service. This is also a common method employed by attackers to evade detection, as this method of file sharing does not involve attachments that can be scanned by traditional security tools, and legitimate cloud storage services are less likely to be blocked.

OSINT analysis on the malicious link link shows the file hosted on Hightail was a HTML file with the associated message “Following up on our LinkedIn conversation”.  Further analysis suggests the file contained obfuscated Javascript that, once opened, would automatically redirect the user to a malicious domain impersonating a legitimate Microsoft login page for credential harvesting purposes.  

The malicious HTML file containing obfuscated Javascript, where the highlighted string references the malicious credential harvesting domain.
Figure 3: The malicious HTML file containing obfuscated Javascript, where the highlighted string references the malicious credential harvesting domain.
Screenshot of fraudulent Microsoft Sign In page hosted on the malicous credential harvesting domain.
Figure 4: Screenshot of fraudulent Microsoft Sign In page hosted on the malicious credential harvesting domain.

Although there was prior email correspondence with the attacker, this email was not automatically deemed safe by Darktrace and was further analyzed for unusual properties and unusual communications for the recipient and the recipient’s peer group.  

Darktrace determined that:

  • It was unusual for this file storage solution to be referenced in communications to the user and the wider network
  • Textual properties of the email body suggested a high level of inducement from the sender, with a high level of focus on the phishing link.
  • The full link contained suspicious properties suggesting it is high risk.
Darktrace’s analysis of the phishing email, presenting key information about the unusual characteristics of this email, information on highlighted content, and an overview of actions that were initially applied.
Figure 5: Darktrace’s analysis of the phishing email, presenting key information about the unusual characteristics of this email, information on highlighted content, and an overview of actions that were initially applied.  

Based on these anomalies, Darktrace initially moved the phishing email to the junk folder and locked the link, preventing the user from directly accessing the malicious file hosted on Hightail.  However, the customer’s security team released the email, likely upon end-user request, allowing the target user to access the file and ultimately enter their credentials into that credential harvesting domain.

Darktrace alerts triggered by the malicious phishing email and the corresponding Autonomous Response actions.
Figure 6: Darktrace alerts triggered by the malicious phishing email and the corresponding Autonomous Response actions.

Lateral Movement

Correspondence between the attacker and target continued for two days after the credential harvesting payload was delivered.  Five days later, Darktrace detected an unusual login using multi-factor authentication (MFA) from a rare external IP and ASN that coincided with Darktrace/Email logs showing access to the credential harvesting link.

This attempt to bypass MFA, known as an Office365 Shell WCSS attack, was likely achieved by inducing the target to enter their credentials and legitimate MFA token into the fake Microsoft login page. This was then relayed to Microsoft by the attacker and used to obtain a legitimate session. The attacker then reused the legitimate token to log into Exchange Online from a different IP and registered their own device for MFA.

Screenshot within Darktrace/Email of the phishing email that was released by the security team, showing the recipient clicked the link to file storage where the malicious payload was stored.
Figure 7: Screenshot within Darktrace/Email of the phishing email that was released by the security team, showing the recipient clicked the link to file storage where the malicious payload was stored.

Event Log showing a malicious login and MFA bypass at 17:57:16, shortly after the link was clicked.  Highlighted in green is activity from the legitimate user prior to the malicious login, using Edge.
Figure 8: Event Log showing a malicious login and MFA bypass at 17:57:16, shortly after the link was clicked.  Highlighted in green is activity from the legitimate user prior to the malicious login, using Edge. Highlighted in orange and red is the malicious activity using Chrome.

The IP addresses used by the attacker appear to be part of anonymization infrastructure, but are not associated with any known indicators of compromise (IoCs) that signature-based detections would identify [9] [10].

In addition to  logins being observed within half an hour of each other from multiple geographically impossible locations (San Francisco and Phoenix), the unexpected usage of Chrome browser, compared to Edge browser previously used, provided Darktrace with further evidence that this activity was unlikely to originate from the legitimate user.  Although the user was a salesperson who frequently travelled for their role, Darktrace’s Self-Learning AI understood that the multiple logins from these locations was highly unusual at the user and group level, and coupled with the subsequent unexpected account modification, was a likely indicator of account compromise.  

Accomplish mission

Although the email had been manually released by the security team, allowing the attack to propagate, additional layers of defense were triggered as Darktrace's Autonomous Response initiated “Disable User” actions upon detection of the multiple unusual logins and the unauthorized registration of security information.  

However, the customer had configured Autonomous Response to require human confirmation, therefore no actions were taken until the security team manually approved them over two hours later. In that time, access to mail items and other SharePoint files from the unusual IP address was detected, suggesting a potential loss of confidentiality to business data.

Advanced Search query showing several FilePreviewed and MailItemsAccessed events from either the IPs used by the attacker, or using the software Chrome.  Note some of the activity originated from Microsoft IPs which may be whitelisted by traditional security tools.
Figure 9: Advanced Search query showing several FilePreviewed and MailItemsAccessed events from either the IPs used by the attacker, or using the software Chrome.  Note some of the activity originated from Microsoft IPs which may be whitelisted by traditional security tools.

However, it appears that the attacker was able to maintain access to the compromised account, as login and mail access events from 199.231.85[.]153 continued to be observed until the afternoon of the next day.  

Conclusion

This incident demonstrates the necessity of AI to security teams, with Darktrace’s ActiveAI Security Platform detecting a sophisticated phishing attack where human judgement fell short and initiated a real-time response when security teams could not physically respond as fast.  

Security teams are very familiar with social engineering and impersonation attempts, but these attacks remain highly prevalent due to the widespread adoption of technologies that enable these techniques to be deployed with great sophistication and ease.  In particular, the popularity of information-rich platforms like LinkedIn that are geared towards connecting with unknown people make it an attractive initial access point for malicious attackers.

In the second half of 2023 alone, over 200 thousand fake profiles were reported by members on LinkedIn [11].  Fake profiles can be highly sophisticated, use professional images, contain compelling descriptions, reference legitimate company listings and present believable credentials.  

It is unrealistic to expect end users to defend themselves against such sophisticated impersonation attempts. Moreover, it is extremely difficult for human defenders to recognize every fraudulent interaction amidst a sea of fake profiles. Instead, defenders should leverage AI, which can conduct autonomous investigations without human biases and limitations. AI-driven security can ensure successful detection of fraudulent or malicious activity by learning what real users and devices look like and identifying deviations from their learned behaviors that may indicate an emerging threat.

Appendices

Darktrace Model Detections

DETECT/ Apps

SaaS / Compromise / SaaS Anomaly Following Anomalous Login

SaaS / Compromise / Unusual Login and Account Update

SaaS / Unusual Activity / Multiple Unusual External Sources For SaaS Credential

SaaS / Access / Unusual External Source for SaaS Credential Use

SaaS / Compliance / M365 Security Information Modified

RESPOND/ Apps

Antigena / SaaS / Antigena Suspicious SaaS Activity Block

Antigena / SaaS / Antigena Unusual Activity Block

DETECT & RESPOND/ Email

·      Link / High Risk Link + Low Sender Association

·      Link / New Correspondent Classified Link

·      Link / Watched Link Type

·      Antigena Anomaly

·      Association / Unknown Sender

·      History / New Sender

·      Link / Link to File Storage

·      Link / Link to File Storage + Unknown Sender

·      Link / Low Link Association

List of IoCs

·      142.252.106[.]251 - IP            - Possible malicious IP used by attacker during cloud account compromise

·      199.231.85[.]153 – IP - Probable malicious IP used by attacker during cloud account compromise

·      vukoqo.hebakyon[.]com – Endpoint - Credential harvesting endpoint

MITRE ATT&CK Mapping

·      Resource Development - T1586 - Compromise Accounts

·      Resource Development - T1598.003 – Spearphishing Link

·      Persistence - T1078.004 - Cloud Accounts

·      Persistence - T1556.006 - Modify Authentication Process: Multi-Factor Authentication

·      Reconnaissance - T1593.001 – Social Media

·      Reconnaissance - T1598 – Phishing for Information

·      Reconnaissance - T1589.001 – Credentials

·      Reconnaissance - T1591.002 – Business Relationships

·      Collection - T1111 – Multifactor Authentication Interception

·      Collection - T1539 – Steal Web Session Cookie

·      Lateral Movement - T1021.007 – Cloud Services

·      Lateral Movement - T1213.002 - Sharepoint

References

[1] Jessica Barker, Hacked: The secrets behind cyber attacks, (London: Kogan Page, 2024), p. 130-146.

[2] https://www.bitdefender.co.uk/blog/hotforsecurity/5-linkedin-scams-and-how-to-avoid-them/

[3] https://www.washingtonpost.com/technology/2023/08/31/linkedin-personal-posts/

[4] https://www.forbes.com/sites/joshbersin/2012/05/21/facebook-vs-linkedin-whats-the-difference/

[5] https://thelinkedblog.com/2022/3-reasons-why-you-should-make-your-profile-public-1248/

[6] https://www.linkedin.com/pulse/50-linkedin-statistics-every-professional-should-ti9ue

[7] https://www.nytimes.com/2022/09/24/business/linkedin-social-experiments.html

[8] https://darktrace.com/blog/the-domain-game-how-email-attackers-are-buying-their-way-into-inboxes

[9] https://spur.us/context/142.252.106[.]251

[10] https://spur.us/context/199.231.85[.]153

[11]https://www.statista.com/statistics/1328849/linkedin-number-of-fake-accounts-detected-and-removed

Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Nicole Wong
Cyber Security Analyst

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May 28, 2026

From Efficiency to Exposure: How AI Adoption Is Creating Unseen Vulnerabilities on the Factory Floor

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How AI agents impact the manufacturing industry

Security teams and IT personnel across the manufacturing industry are under constant pressure to protect production, maintain uptime, and safeguard critical assets but the rise of AI is bringing huge new opportunities alongside new cyber risks. Across manufacturing, AI is embedded into workflows, decision-making, and increasingly, autonomous AI agents are acting on behalf of employees and systems.  

Agentic systems are powerful because they can act independently, but that same autonomy also creates cyber and operational risk. Agents have extensive permissions and are capable of carrying out complex tasks, making decisions, and interacting with tools or external systems with little to no human intervention.

Unlike traditional AI models that perform predefined tasks, AI agents use advanced techniques to mimic human decision-making processes, dynamically adapting to new challenges, making decision and taking action based on their own judgement. They look like employees operationally but lack judgment, ethics, or fear of consequences like humans do. This means they can be easily manipulated by cybercriminals, and an AI agent embedded across an OT network creates threats that extend well beyond data exposure. For example, at BMW, AI identifies faults in welding processes as they occur. At its Spartanburg plant, AI monitors the weld of 300-400 metal studs onto every SUV frame to detect misplaced or faulty studs and correct them instantly. Corruption of BMW’s AI system could lead to catastrophic quality control errors.

Adopting agentic AI systems across manufacturing raises some concerns across security teams. New data from our State of AI Cybersecurity survey shows that 78% of manufacturing security professionals are worried about employee use of AI agents – their top concern. That’s followed by employee use of generative AI tools like CoPilot and ChatGPT, a worry for 76% of security professionals at manufacturing organizations. As these tools gain more access to business data and processes, and more autonomy within organizations, security teams, who today have minimal visibility of agent activity in their environments, increasingly have sensitive data exposure (a worry for 60%) and accidental policy and regulatory violations (59%) on their minds.

External AI-powered threats are evolving just as quickly

The same capabilities transforming manufacturing are also reshaping cyberattacks.

AI is enabling attackers to automate reconnaissance, refine targeting, and adapt in real time. What once required time and manual effort can now be executed continuously and at scale. Manufacturers are already seeing the impact. According to manufacturing security professionals we surveyed, 76% are already being impacted by AI-powered threats and 90% see AI increasing the success of social engineering attacks.

And the techniques themselves are evolving. Concerns across the manufacturing sector show growing anxiety about the range of AI-powered attack routes, most pressingly of adaptive malware that evolves in real-time – a prospect half (49%) of manufacturing security professionals we surveyed are worried by, a full 9% more than the average across industries. AI adaptive malware is followed by:

  • Automated vulnerability scanning and exploit chaining (48%) which has become even more pressing as Anthropic’s new Mythos AI Model supercharges vulnerability discovery
  • Hyper-personalized phishing campaigns (46%), which remain a mainstay in hackers’ arsenals, and AI has amplified their effectiveness by making phishing emails more convincing and harder to detect.

This is not just an increase in volume, it is a shift toward threats that evolve as they unfold - often faster than static defenses can respond.

Despite rising awareness, many manufacturers are not yet equipped to manage this shift. More than half (51%) say they are not adequately prepared for AI-driven threats, and only 37% have formal policies governing AI deployment.  

Securing AI through visibility, context, and guardrails

Addressing this challenge does not require manufacturers to slow innovation. It requires a different approach to security, one that can operate at the same speed and scale as AI. Three specific priorities are emerging for manufacturers looking to take advantage of the power of AI.

Visibility is foundational.  

Organizations need to understand where AI is being used, what it can access, and how it behaves across both IT and OT environments. Without that, risk cannot be measured or managed. It is no surprise that Darktrace’s research found that 91% of manufacturing security professionals said that they need to understand how AI makes decisions before trusting it. This is even more critical in operational settings where disruption has safety, environmental, financial, and reputational impacts.

Context is what turns visibility into action.  

In environments shaped by AI, normal behavior is constantly shifting. Detecting threats requires a behavioral approach; understanding patterns of life across the organization and identifying subtle deviations in real time – a step change in organizations’ traditional approach to security and risk management.

Guardrails ensure that agency does not become exposure  

As AI systems take on greater responsibility, organizations need clear boundaries around what they can do and when they can act independently. These controls must be embedded into systems themselves, not applied after the fact.  

Securing AI Agents Across Manufacturing IT and OT

The rise of agentic AI is transforming manufacturing - powering next-generation operations while reshaping the security landscape. This is not just an increase in threats, but a shift to autonomous systems, continuously evolving behaviors, and risks moving at machine speed. For organizations trying to grapple with the challenge of enabling AI while managing the risk, visibility, context and guardrails should be foundational.

Darktrace helps manufacturers build secure AI approaches by making those foundations possible. It provides visibility and real-time detection and response to unusual activity across IT and OT environments and allows organizations to understand AI activity from the prompts employees use and the agents they build to how those agents are behaving across the environment. For manufacturers scaling AI, this delivers a foundation for innovation without sacrificing control.

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Oakley Cox
Director of Product

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May 28, 2026

How to Evaluate AI Vendors: 5 Key categories for AI Adoption

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Understanding the AI buyers’ market

AI adoption has become a central topic of discussion in boardrooms, drawing growing interest from business leaders. Ultimately, organizations hope that an investment in AI technology will have tremendous returns. However, the process of buying an AI solution is not as straight forward as it appears on the surface.  

While business leaders may be eager to improve productivity across their operations, practitioners responsible for evaluating and selecting AI solutions may not always have the visibility or technical understanding needed to make the right decisions for their business. What is typically marketed as a holistic solution to their most critical problems is usually followed by uncertainty when AI tools are finally operationalized in real environments.

This guide is intended to support security leaders who are under growing pressure to adopt AI tools while navigating complex terminology, vendor claims, and increasingly crowded buying cycles. Ultimately, the goal is to help organizations evaluate and adopt AI in a safe, effective, and well-governed way. To support this, we’ve structured the evaluation framework across five key categories:

  1. Governance, safety, and data controls
  1. Data gathering and training
  1. Model and technique choice
  1. Performance and accuracy validation    
  1. Interpretability, adjustability, and transparency    

What buying AI looks like in cybersecurity

While investing in AI can bring immense benefits to your security team, first-time buyers of AI cybersecurity solutions may not know where to start. They will have to determine the type of tool they want, know the options available, and evaluate vendors. Research and understanding are critical to ensure purchases are worth the investment.  

With acceleration in AI adoption, accompanied by the recent boom in agentic AI and autonomous agents, CISOs must look “beneath the hood" of these tools to understand how they work, how they are governed, and to ensure the system is secure and compliant with internal policies.

Challenges in the AI buyers’ marketplace  

The AI security software market is buzzing with hype and flashy promises, which, understandably, needs to be addressed with due diligence. Potential buyers, especially in the cybersecurity space, are hesitant when it comes to allowing AI autonomous capabilities across their workflows, and a lack of vendor transparency can exacerbate those feelings.  

Reinforcing this sentiment, research from this year's Darktrace’s State of AI Cybersecurity report shows where confidence and hesitancy emerge amongst potential buyers. On the one hand, security professionals agree that they have good visibility into the logic and reasoning processes their AI solutions use. However, they lack the explainability and trust to allow AI to take independent remedial action.

  • 89% say they have good visibility into the reasoning behind the outputs generated by AI solutions
  • 92% say they need to understand how a defensive AI tool makes decisions before they can trust it
  • Only 14% say they allow AI to act independently, performing autonomous actions without human approval
  • 74% say they are limiting the autonomy of AI taking action in their SOC until explainability improves

Given the desire for trust and explainability we are seeing from buyers, it's important for them to be equipped with the right questions to ask vendors during an assessment or POV of AI tools in order to demystify marketing hype from real operational outcomes.

Below is a list of categories in which buyers can assess AI vendors or AI Service Providers (AISPs) to help reach safe adoption and maximize their ROI.  

5 categories of AI vendor assessment

Darktrace groups these AI-related questions into 5 categories: governance, data and training, model and technique choice, performance validation, and interpretability and adjustability. By asking questions regarding each of these 5 categories, buyers can gain a deeper understanding of how an AISP’s systems work and whether they suit their business requirements.

Governance, safety, and data controls

Governance of AI systems is critical for all AISPs. Whether their platform is based around a single model, or is a more complex, composite AI solution, strong governance is essential to ensure the system is safe, robust, and reliable.

A simple question you could ask is:

What AI governance policies and frameworks do you follow, and/or certifications do you currently maintain?

For more questions you can ask vendors, download the full guide here.

Darktrace is certified to the ISO/IEC 42001 standard, the world’s first AI Management System (AIMS) standard. ISO/IEC 42001 addresses the unique ethical and technical challenges AI poses by setting out a structured way to manage risks such as transparency, accuracy, and misuse. This includes a commitment to ethical AI development, and effective management and monitoring of AI systems both prior to and continually after release.

Data gathering and training

Accurate, meaningful, and unbiased data gathering is the first important step in producing any AI system. An AI model trained using inaccurate, unbalanced, or poor-quality training data will fail to perform optimally.

To alleviate concerns regarding training data quality, a question you could ask is:

What steps do you take to prevent bias in your AI models and training data?

For more questions, download the full guide here.

AISPs should be able to provide information about the steps taken, workflows followed, and auditing performed to reduce AI bias where appropriate. While it’s sometimes impossible to fully remove bias from an AI model, appropriate actions should be taken to mitigate or reduce bias where relevant.

Model and technique choice

Different AI techniques are optimal for different tasks. For example, research from Gartner suggests that relying on a single “one-size-fits-all" model can lead to data gaps, especially in highly specialized domains.

To achieve more accurate and robust AI solutions, AI leaders should move beyond using just one model or technique, embrace composite AI practices, and adopt a holistic AI system perspective.

A straightforward question you could ask is simply:

What type(s) of AI model(s) do you utilize in your solution?

For more questions, download the full guide here.

While specific detailed information about custom systems used by AISPs is likely proprietary, buyers should expect vendors to be able to provide an overview of the broad techniques used. This will allow you as a buyer to determine if the type of model is appropriate for your use case.

Performance and accuracy validation  

Testing and evaluation of performance is essential for all AI systems. Performance analysis should be performed both before release and continually after release to identify potential data or model drift.  

A question you could ask to understand an AISPs testing workflow is:

How do you audit, test, evaluate, verify, and validate your AI model outputs?

For more questions, download the full guide here.

Testing workflows will likely vary depending on the type of model – measurements relevant to one system may not always be relevant to others. Assessment of systems should also extend beyond these standard accuracy and robustness tests, and should also feature physical performance, such as latency and resource consumption.  

Interpretability, adjustability, and transparency  

AI systems are typically a black box, simply providing an output without an explanation of how that output was attained. Interpretability and transparency are critical to ensure that both SOC teams and end-users trust the outputs of a system to be accurate and meaningful.

A question you could ask is:

How do you promote a trust relationship between human analysts and AI outputs?

For more questions, download the full guide here.

In the context of cybersecurity, trust and interpretability are even more essential. This is particularly relevant for generative AI-based systems (including most AI Agents), where the risk of hallucination can reduce trust in responses.

Cybersecurity systems often need to perform autonomous actions to block incoming threats – an email filtering system may hold potentially dangerous emails; a firewall may block malicious inbound connections. If SOC teams can’t trust these systems to perform accurately, these systems may be limited or disabled, critically reducing their defensive power.

Darktrace as an AI-native cybersecurity vendor

Darktrace has been building and applying AI in cybersecurity for over a decade, developing its capabilities alongside an increasingly complex and fast‑moving threat landscape. This experience has resulted in a mature, multi-layered approach to AI, which continuously learns the normal patterns of each organization to understand behavior, interpret context, and identify meaningful deviations — without relying on predefined rules or known attack signatures. Over time, this has enabled a proven behavioral understanding that helps uncover subtle signals of risk that may otherwise be missed.

With the backing of our ISO/IEC 42001 certification, stakeholders, customers, and partners can be confident that Darktrace is responsibly, ethically, and safely developing its AI systems, and managing the use of AI in day-to-day operations in a compliant and secure manner.  

Explore the principles behind Darktrace’s responsible AI approach, informed by collaboration with global experts in academia and governments, detailing how accountability, explainability, and continuous validation are built into its cybersecurity technology.

How Darktrace secures AI systems

Darktrace now brings these capabilities to monitor and respond to risk generated from AI systems across organizations with Darktrace / SECURE AI. This solution analyzes how prompts, agents, and systems are used within the context of each organization, bringing every AI interaction into a single view. This unique approach helps teams understand intent, assess risk, protect sensitive data, and enforce policy across both human and AI agent activity.

Stay up to date

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

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

Further Reading on AI in cybersecurity

When deciding to invest in an AI solution, it’s important to understand what this means for you and your organization. The questions presented here are only a starting point in understanding an AI solution and whether it is appropriate for your use case.  

Gain deeper knowledge on applications of AI in cybersecurity and Darktrace’s multi-layered AI in the AI Arsenal White Paper.

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Jamie Bali
Technical Author (AI) Developer
Your data. Our AI.
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