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August 7, 2024

How Darktrace’s AI Applies a Zero-Trust Mentality within Critical Infrastructure Supply Chains

Darktrace prevented a Critical National Infrastructure organization from falling victim to a SharePoint phishing attack originating from one of its trusted suppliers. This blog discusses common perceptions of zero-trust in email security, how AI that uses anomaly-based threat detection embodies core zero-trust principles and the relevance of this approach to securing CNI bodies with complex but interdependent supply chains from Cloud account compromise. 
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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07
Aug 2024

Note: In order to name anonymity, real organization names have been replaced, all names used in this blog are fictitious.

What are critical national infrastructure sectors?

Critical National Infrastructure (CNI) sectors encompass of assets, systems, and networks essential to the functioning of society. Any disruption or destruction of these sectors could have wide-reaching and potentially disastrous effects on a country’s economy, security and/or healthcare services [1].

Cyber risks across Transportation Systems sector

Transportation Systems is one such CNI sector comprising of interconnected networks of fixed and mobile assets managed by both public and private operators. These systems are highly interdependent with other CNI sectors too. As such, the digital technologies this sector relies on – such as positioning and tracking, signaling, communications, industrial system controls, and data and business management – are often interconnected through different networks and remote access terminals. This interconnectedness creates multiple entry points that need to be security across the supply.

Digital transformation has swept through CNI sectors in recent years, including Transportation Systems. These organizations are now increasingly dependent on third-party and cloud providers for data storage and transmission, making their supply chains vulnerable to exploitation by malicious actors [2].

The exploitation of legitimate and popular cloud services mirrors the well-known “living-off-the-land” techniques, which are not being adapted to the cloud along with the resources they support. In one recent case previously discussed by Darktrace, for example, a phishing attack attempted to abuse Dropbox to deliver malicious payloads.

Zero-Trust within CNI Sectors

One recommended approach to secure an organization’s supply chain and cloud environments is the implementation of zero-trust strategies, which remove inherent trust within the network [3] [4]. The principle of “never trust, always verify” is widely recognized as an architectural design, with 63% of organizations surveyed by Gartner reportedly implementing a zero-trust strategy, but in most cases to less than 50% of their environments [5]

Although this figure reflects the reality and challenge of balancing operations and security, demands from the threat landscape and supply chain risks mean that organizations must adopt zero-trust principles in areas not traditionally considered part of network architecture, such as email and cloud environments.

Email is often the primary entry point for cyber-attacks with Business Email Compromise (BEC) being a major threat to CNI organizations. However, the application of zero-trust principles to secure email environments is still not well understood. Common misconceptions include:

  • “Positively identifying known and trusted senders” – Maintaining a list of “known and trusted senders” contradicts the zero-trust model, which assumes that no entity is inherently trustworthy.
  • “Using DMARC, DKIM and SPF” – While these protocols offer some protection, they are often insufficient on their own, as they can be bypassed and do not protect against email account takeovers. Research published from Darktrace’s last two threat reports consistently shows that at least 60% of phishing emails detected by Darktrace had bypassed Domain-based Message Authentication, Reporting & Conformance (DMARC) [6] [7].  
  • “Mapping transaction flows between internal and external users to determine what access is required/not required” – Although this aligns with the principles of least privilege, it is too static for today’s dynamic supply chains and evolving digital infrastructure. This approach also suggests the existence of “trusted” access routes into a network.

Attack Overview

In July 2024, Darktrace / EMAIL™ detected and contained a sophisticated phishing attack leveraging Microsoft SharePoint. This attack exploited the trusted relationship between a Darktrace customer in the public transport sector and a compromised supplier. Traditional methods, such as those detailed above, would likely have failed to defend against such an advanced threat. However, Darktrace’s behavioral analysis and zero-trust approach to email security allowed it to successfully identify and neutralize the attack, preventing any potential disruption.

Initial Intrusion Attempt

The observed phishing attack by Darktrace would suggest that the customer’s supplier was targeted by a similar campaign beforehand. This initial breach likely allowed the attacker to use the now compromised account as a vector to compromise additional accounts and networks.

On July 9, Darktrace / EMAIL identified a significant spike in inbound emails from “supplier@engineeringcompany[.]com”. The emails appeared to be legitimate notifications sent via SharePoint and contained a file named “Payment Applications Docs”.

Email correspondence in the weeks around the phishing attack.
Figure 1: Email correspondence in the weeks around the phishing attack. The sender is an established correspondent with ongoing communications prior to and after the attack, however there is a significant spike in incoming emails on the day of the attack.

This reflects a common technique in malicious social engineering attempts, where references to payment are used to draw attention and prompt a response. Darktrace observed a large number of recipients within the organization receiving the same file, suggesting that the motive was likely credential harvesting rather than financial gain. Financially motivated attacks typically require a more targeted, ‘under-the-radar’ approach to be successful.

These phishing emails were able to bypass the customer’s email gateways as they were sent from a trusted and authoritative source, SharePoint, and utilized an email address with which the customer had previously corresponded. The compromised account was likely whitelisted by traditional email security tools that rely on SPF, DKIM, and DMAC, allowing the malicious emails to evade detection.

Autonomous Response

Darktrace / EMAIL analysis of the unusual characteristics of the phishing email in relation to the supplier’s typical behaviour, despite the email originating from a legitimate SharePoint notification.
Figure 2: Darktrace / EMAIL analysis of the unusual characteristics of the phishing email in relation to the supplier’s typical behavior, despite the email originating from a legitimate SharePoint notification.

However, Darktrace / EMAIL did not use these static rules to automatically trust the email. Darktrace’s Self-Learning AI detected the following anomalies:

  • Although the sender was known, it was not normal for the supplier to share files with the customer via SharePoint.
  • The supplier initiated an unusually large number of file shares in a short period of time, indicating potential spam activity.
  • The SharePoint link had wide access permissions, which is unusual for a sensitive payment document legitimately shared between established contacts.

Darktrace understood that the email activity constituted a significant deviation in expected behavior between the sender and customer, regardless of the known sender and use of a legitimate filesharing platform like SharePoint.

As a result, Darktrace took action to hold more than 100 malicious emails connected to the phishing attack, preventing them from landing in recipient inboxes in the first instance.  By taking a behavioral approach to securing customer email environments, Darktrace’s Self-Learning AI embodies the principles of zero trust, assessing each interaction in real-time against a user’s dynamic baseline rather than relying on static and often inaccurate rules to define trust.

Conclusion

Cloud services, such as SharePoint, offer significant advantages to the transportation sector by streamlining data exchange with supply chain partners and facilitating access to information for analytics and planning. However, these benefits come with notable risks. If a cloud account is compromised, unauthorized access to sensitive information could lead to extortion and lateral movement into mission-critical systems for more damaging attacks on CNI. Even a brief disruption in cloud access can have severe economic repercussions due to the sector’s dependence on these services for resource coordination and the cascading impacts on other critical systems [9].

While supply chain resilience is often evaluated based on a supplier’s initial compliance with baseline standards, organizations must be wary of potential future threats and focus on post-implementation security. It is essential for organizations to employ strategies to protect their assets from attacks that would exploit vulnerabilities within the trusted supply chain. Given that CNI and the transportation sector are prime targets for state-sponsored actors and Advanced Persistent Threat (APT) groups, the complex and interconnected nature of their supply chains opens the door for opportunistic attackers.

Defenders face the challenge of ensuring secure access and collaboration across numerous, dynamic assets, often without full visibility. Therefore, security solutions must be as dynamic as the threats they face, avoiding reliance on static rules. Real-time assessment of devices behavior, even if deemed trusted by end-users and human security teams, is crucial for maintaining security.

Darktrace’s AI-driven threat detection aligns with the zero-trust principle of assuming the risk of a breach. By leveraging AI that learns an organization’s specific patterns of life, Darktrace provides a tailored security approach ideal for organizations with complex supply chains.

Credit to Nicole Wong, Senior Cyber Analyst Consultant and Ryan Traill, Threat Content Lead

Appendices

Darktrace Model Detections

Key model alerts:

  • Personalized Sharepoint Share + New Unknown Link
  • Personalized Sharepoint Share + Bad Display Text
  • Personalized Sharepoint Share + Distant Recipient Interaction with Domain
  • Personalized Sharepoint Share + Sender Surge
  • Personalized Sharepoint Share + Wide Access Sharepoint Link

MITRE ATT&CK Mapping

Resource Development • Compromise Accounts: Cloud Accounts • T1586.003

Initial Access • Supply Chain Compromise • T1195

References

[1] https://www.cisa.gov/topics/critical-infrastructure-security-and-resilience/critical-infrastructure-sectors

[2]  https://committees.parliament.uk/writtenevidence/126313/pdf/

[3] https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-161r1.pdf

[4] https://cloudsecurityalliance.org/press-releases/2023/11/15/cloud-security-alliance-launches-the-industry-s-first-authoritative-zero-trust-training-and-credential-the-certificate-of-competence-in-zero-trust-cczt

[5] https://www.gartner.com/en/documents/5286863#:~:text=Summary,anticipate%20staffing%20and%20cost%20increases.

[6] https://darktrace.com/threat-report-2023

[7] https://darktrace.com/resources/first-6-half-year-threat-report-2024

[8] https://dfrlab.org/2023/07/10/critical-infrastructure-and-the-cloud-policy-for-emerging-risk/#transportation

[9] https://access-national-risk-register.service.cabinetoffice.gov.uk/risk-scenario/cyber-attack-transport-sector

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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January 7, 2026

How a leading bank is prioritizing risk management to power a resilient future

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As one of the region’s most established financial institutions, this bank sits at the heart of its community’s economic life – powering everything from daily transactions to business growth and long-term wealth planning. Its blend of physical branches and advanced digital services gives customers the convenience they expect and the personal trust they rely on. But as the financial world becomes more interconnected and adversaries more sophisticated, safeguarding that trust requires more than traditional cybersecurity. It demands a resilient, forward-leaning approach that keeps pace with rising threats and tightening regulatory standards.

A complex risk landscape demands a new approach

The bank faced a challenge familiar across the financial sector: too many tools, not enough clarity. Vulnerability scans, pen tests, and risk reports all produced data, yet none worked together to show how exposures connected across systems or what they meant for day-to-day operations. Without a central platform to link and contextualize this data, teams struggled to see how individual findings translated into real exposure across the business.

  • Fragmented risk assessments: Cyber and operational risks were evaluated in silos, often duplicated across teams, and lacked the context needed to prioritize what truly mattered.
  • Limited executive visibility: Leadership struggled to gain a complete, real-time view of trends or progress, making risk ownership difficult to enforce.
  • Emerging compliance pressure: This gap also posed compliance challenges under the EU’s Digital Operational Resilience Act (DORA), which requires financial institutions to demonstrate continuous oversight, effective reporting, and the ability to withstand and recover from cyber and IT disruptions.
“The issue wasn’t the lack of data,” recalls the bank’s Chief Technology Officer. “The challenge was transforming that data into a unified, contextualized picture we could act on quickly and decisively.”

As the bank advanced its digital capabilities and embraced cloud services, its risk environment became more intricate. New pathways for exploitation emerged, human factors grew harder to quantify, and manual processes hindered timely decision-making. To maintain resilience, the security team sought a proactive, AI-powered platform that could consolidate exposures, deliver continuous insight, and ensure high-value risks were addressed before they escalated.

Choosing Darktrace to unlock proactive cyber resilience

To reclaim control over its fragmented risk landscape, the bank selected Darktrace / Proactive Exposure Management™ for cyber risk insight. The solution’s ability to consolidate scanner outputs, pen test results, CVE data, and operational context into one AI-powered view made it the clear choice. Darktrace delivered comprehensive visibility the team had long been missing.

By shifting from a reactive model to proactive security, the bank aimed to:

  • Improve resilience and compliance with DORA
  • Prioritize remediation efforts with greater accuracy
  • Eliminate duplicated work across teams
  • Provide leadership with a complete view of risk, updated continuously
  • Reduce the overall likelihood of attack or disruption

The CTO explains: “We needed a solution that didn’t just list vulnerabilities but showed us what mattered most for our business – how risks connected, how they could be exploited, and what actions would create the biggest reduction in exposure. Darktrace gave us that clarity.”

Targeting the risks that matter most

Darktrace / Proactive Exposure Management offered the bank a new level of visibility and control by continuously analyzing misconfigurations, critical attack paths, human communication patterns, and high-value assets. Its AI-driven risk scoring allowed the team to understand which vulnerabilities had meaningful business impact, not just which were technically severe.

Unifying exposure across architectures

Darktrace aggregates and contextualizes data from across the bank’s security stack, eliminating the need to manually compile or correlate findings. What once required hours of cross-team coordination now appears in a single, continuously updated dashboard.

Revealing an adversarial view of risk

The solution maps multi-stage, complex attack paths across network, cloud, identity systems, email environments, and endpoints – highlighting risks that traditional CVE lists overlook.

Identifying misconfigurations and controlling gaps

Using Self-Learning AI, Darktrace / Proactive Exposure Management spots misconfigurations and prioritizes them based on MITRE adversary techniques, business context, and the bank’s unique digital environment.

Enhancing red-team and pen test effectiveness

By directing testers to the highest-value targets, Darktrace removes guesswork and validates whether defenses hold up against realistic adversarial behavior.

Supporting DORA compliance

From continuous monitoring to executive-ready reporting, the solution provides the transparency and accountability the bank needs to demonstrate operational resilience frameworks.

Proactive security delivers tangible outcomes

Since deploying Darktrace / Proactive Exposure Management, the bank has significantly strengthened its cybersecurity posture while improving operational efficiency.

Greater insight, smarter prioritization, stronger defensee

Security teams are now saving more than four hours per week previously spent aggregating and analyzing risk data. With a unified view of their exposure, they can focus directly on remediation instead of manually correlating multiple reports.

Because risks are now prioritized based on business impact and real-time operational context, they no longer waste time on low-value tasks. Instead, critical issues are identified and resolved sooner, reducing potential windows for exploitation and strengthening the bank’s ongoing resilience against both known and emerging threats.

“Our goal was to move from reactive to proactive security,” the CTO says. “Darktrace didn’t just help us achieve that, it accelerated our roadmap. We now understand our environment with a level of clarity we simply didn’t have before.”

Leadership clarity and stronger governance

Executives and board stakeholders now receive clear, organization-wide visibility into the bank’s risk posture, supported by consistent reporting that highlights trends, progress, and areas requiring attention. This transparency has strengthened confidence in the bank’s cyber resilience and enabled leadership to take true ownership of risk across the institution.

Beyond improved visibility, the bank has also deepened its overall governance maturity. Continuous monitoring and structured oversight allow leaders to make faster, more informed decisions that strategically align security efforts with business priorities. With a more predictable understanding of exposure and risk movement over time, the organization can maintain operational continuity, demonstrate accountability, and adapt more effectively as regulatory expectations evolve.

Trading stress for control

With Darktrace, leaders now have the clarity and confidence they need to report to executives and regulators with accuracy. The ability to see organization-wide risk in context provides assurance that the right issues are being addressed at the right time. That clarity is also empowering security analysts who no longer shoulder the anxiety of wondering which risks matter most or whether something critical has slipped through the cracks. Instead, they’re working with focus and intention, redirecting hours of manual effort into strategic initiatives that strengthen the bank’s overall resilience.

Prioritizing risk to power a resilient future

For this leading financial institution, Darktrace / Proactive Exposure Management has become the foundation for a more unified, data-driven, and resilient cybersecurity program. With clearer, business-relevant priorities, stronger oversight, and measurable efficiency gains, the bank has strengthened its resilience and met demanding regulatory expectations without adding operational strain.

Most importantly, it shifted the bank’s security posture from a reactive stance to a proactive, continuous program. Giving teams the confidence and intelligence to anticipate threats and safeguard the people and services that depend on them.

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About the author
Kelland Goodin
Product Marketing Specialist

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January 5, 2026

How to Secure AI in the Enterprise: A Practical Framework for Models, Data, and Agents

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Introduction: Why securing AI is now a security priority

AI adoption is at the forefront of the digital movement in businesses, outpacing the rate at which IT and security professionals can set up governance models and security parameters. Adopting Generative AI chatbots, autonomous agents, and AI-enabled SaaS tools promises efficiency and speed but also introduces new forms of risk that traditional security controls were never designed to manage. For many organizations, the first challenge is not whether AI should be secured, but what “securing AI” actually means in practice. Is it about protecting models? Governing data? Monitoring outputs? Or controlling how AI agents behave once deployed?  

While demand for adoption increases, securing AI use in the enterprise is still an abstract concept to many and operationalizing its use goes far beyond just having visibility. Practitioners need to also consider how AI is sourced, built, deployed, used, and governed across the enterprise.

The goal for security teams: Implement a clear, lifecycle-based AI security framework. This blog will demonstrate the variety of AI use cases that should be considered when developing this framework and how to frame this conversation to non-technical audiences.  

What does “securing AI” actually mean?

Securing AI is often framed as an extension of existing security disciplines. In practice, this assumption can cause confusion.

Traditional security functions are built around relatively stable boundaries. Application security focuses on code and logic. Cloud security governs infrastructure and identity. Data security protects sensitive information at rest and in motion. Identity security controls who can access systems and services. Each function has clear ownership, established tooling, and well-understood failure modes.

AI does not fit neatly into any of these categories. An AI system is simultaneously:

  • An application that executes logic
  • A data processor that ingests and generates sensitive information
  • A decision-making layer that influences or automates actions
  • A dynamic system that changes behavior over time

As a result, the security risks introduced by AI cuts across multiple domains at once. A single AI interaction can involve identity misuse, data exposure, application logic abuse, and supply chain risk all within the same workflow. This is where the traditional lines between security functions begin to blur.

For example, a malicious prompt submitted by an authorized user is not a classic identity breach, yet it can trigger data leakage or unauthorized actions. An AI agent calling an external service may appear as legitimate application behavior, even as it violates data sovereignty or compliance requirements. AI-generated code may pass standard development checks while introducing subtle vulnerabilities or compromised dependencies.

In each case, no single security team “owns” the risk outright.

This is why securing AI cannot be reduced to model safety, governance policies, or perimeter controls alone. It requires a shared security lens that spans development, operations, data handling, and user interaction. Securing AI means understanding not just whether systems are accessed securely, but whether they are being used, trained, and allowed to act in ways that align with business intent and risk tolerance.

At its core, securing AI is about restoring clarity in environments where accountability can quickly blur. It is about knowing where AI exists, how it behaves, what it is allowed to do, and how its decisions affect the wider enterprise. Without this clarity, AI becomes a force multiplier for both productivity and risk.

The five categories of AI risk in the enterprise

A practical way to approach AI security is to organize risk around how AI is used and where it operates. The framework below defines five categories of AI risk, each aligned to a distinct layer of the enterprise AI ecosystem  

How to Secure AI in the Enterprise:

  • Defending against misuse and emergent behaviors
  • Monitoring and controlling AI in operation
  • Protecting AI development and infrastructure
  • Securing the AI supply chain
  • Strengthening readiness and oversight

Together, these categories provide a structured lens for understanding how AI risk manifests and where security teams should focus their efforts.

1. Defending against misuse and emergent AI behaviors

Generative AI systems and agents can be manipulated in ways that bypass traditional controls. Even when access is authorized, AI can be misused, repurposed, or influenced through carefully crafted prompts and interactions.

Key risks include:

  • Malicious prompt injection designed to coerce unwanted actions
  • Unauthorized or unintended use cases that bypass guardrails
  • Exposure of sensitive data through prompt histories
  • Hallucinated or malicious outputs that influence human behavior

Unlike traditional applications, AI systems can produce harmful outcomes without being explicitly compromised. Securing this layer requires monitoring intent, not just access. Security teams need visibility into how AI systems are being prompted, how outputs are consumed, and whether usage aligns with approved business purposes

2. Monitoring and controlling AI in operation

Once deployed, AI agents operate at machine speed and scale. They can initiate actions, exchange data, and interact with other systems with little human oversight. This makes runtime visibility critical.

Operational AI risks include:

  • Agents using permissions in unintended ways
  • Uncontrolled outbound connections to external services or agents
  • Loss of forensic visibility into ephemeral AI components
  • Non-compliant data transmission across jurisdictions

Securing AI in operation requires real-time monitoring of agent behavior, centralized control points such as AI gateways, and the ability to capture agent state for investigation. Without these capabilities, security teams may be blind to how AI systems behave once live, particularly in cloud-native or regulated environments.

3. Protecting AI development and infrastructure

Many AI risks are introduced long before deployment. Development pipelines, infrastructure configurations, and architectural decisions all influence the security posture of AI systems.

Common risks include:

  • Misconfigured permissions and guardrails
  • Insecure or overly complex agent architectures
  • Infrastructure-as-Code introducing silent misconfigurations
  • Vulnerabilities in AI-generated code and dependencies

AI-generated code adds a new dimension of risk, as hallucinated packages or insecure logic may be harder to detect and debug than human-written code. Securing AI development means applying security controls early, including static analysis, architectural review, and continuous configuration monitoring throughout the build process.

4. Securing the AI supply chain

AI supply chains are often opaque. Models, datasets, dependencies, and services may come from third parties with varying levels of transparency and assurance.

Key supply chain risks include:

  • Shadow AI tools used outside approved controls
  • External AI agents granted internal access
  • Suppliers applying AI to enterprise data without disclosure
  • Compromised models, training data, or dependencies

Securing the AI supply chain requires discovering where AI is used, validating the provenance and licensing of models and data, and assessing how suppliers process and protect enterprise information. Without this visibility, organizations risk data leakage, regulatory exposure, and downstream compromise through trusted integrations.

5. Strengthening readiness and oversight

Even with strong technical controls, AI security fails without governance, testing, and trained teams. AI introduces new incident scenarios that many security teams are not yet prepared to handle.

Oversight risks include:

  • Lack of meaningful AI risk reporting
  • Untested AI systems in production
  • Security teams untrained in AI-specific threats

Organizations need AI-aware reporting, red and purple team exercises that include AI systems, and ongoing training to build operational readiness. These capabilities ensure AI risks are understood, tested, and continuously improved, rather than discovered during a live incident.

Reframing AI security for the boardroom

AI security is not just a technical issue. It is a trust, accountability, and resilience issue. Boards want assurance that AI-driven decisions are reliable, explainable, and protected from tampering.

Effective communication with leadership focuses on:

  • Trust: confidence in data integrity, model behavior, and outputs
  • Accountability: clear ownership across teams and suppliers
  • Resilience: the ability to operate, audit, and adapt under attack or regulation

Mapping AI security efforts to recognized frameworks such as ISO/IEC 42001 and the NIST AI Risk Management Framework helps demonstrate maturity and aligns AI security with broader governance objectives.

Conclusion: Securing AI is a lifecycle challenge

The same characteristics that make AI transformative also make it difficult to secure. AI systems blur traditional boundaries between software, users, and decision-making, expanding the attack surface in subtle but significant ways.

Securing AI requires restoring clarity. Knowing where AI exists, how it behaves, who controls it, and how it is governed. A framework-based approach allows organizations to innovate with AI while maintaining trust, accountability, and control.

The journey to secure AI is ongoing, but it begins with understanding the risks across the full AI lifecycle and building security practices that evolve alongside the technology.

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About the author
Brittany Woodsmall
Product Marketing Manager, AI & Attack Surface
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