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November 2, 2022

How Darktrace Identifies Shadow IT

Find out how Darktrace identifies and manages shadow IT. Gain valuable insights into detecting unauthorized IT activities within your organization.
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
Dan Fein
VP, Product
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02
Nov 2022

Darktrace was recently called into a situation where a department had set up an online questionnaire, which had been included in a newsletter to customers. They’d used a free version of the software and it had not been authorised by IT. 

The questionnaire requested some sensitive data from the respondents, but as there was no third-party contractor agreement in place, there was no agreement on data usage, storage, protection or maintenance. Unfortunately, the software provider had a security vulnerability in their solution, and this resulted in a massive data breach of the questionnaire answers – a situation that could have been avoided, had the organization been using Darktrace PREVENT.

This type of unauthorized usage is a common instance of the growing problem of shadow IT. Unlike formal IT, which is routed through an IT department closely involved in approving, setting up, and maintaining it, shadow IT falls outside of that team’s control. It is made up of systems – including cloud and SaaS applications – which the IT department are either unaware exist, or are unable to remove without disrupting workflows.

Because it lacks proper involvement from IT, shadow IT’s impact on a company’s overall security risk can be ill-defined, not least because it is difficult for many organizations to know how much of it exists within their digital estates. Full visibility over the digital environment, and every asset it contains, is necessary before the problem can begin to be addressed.

The reality is: shadow IT happens

Shadow IT crops up for a number of reasons. This is often employees taking steps to save time: having your IT team acquire and set up new infrastructure and software is important for managing security risks, but they necessarily take time. For some employees, the time taken to go through these formal channels is enough to push them to use shadow IT systems, which are generally quicker and cheaper to set up and begin using. It’s easier than ever, for instance, to spin up cloud IT environments. The pressure of completing projects within strict budgetary limitations may also tempt employees down this cheaper, but more hazardous path.

There is also a problem of business-led IT, whereby business decisions involving the use of new systems are made without consulting IT departments. Organizations should always look to adopt a security-first approach, because when business interests lead the way, IT teams can struggle to keep up, leading to the emergence of new vulnerabilities. In cases where these business decisions are intentionally hidden from the IT team, Shadow IT becomes a serious concern.

Reducing the effects of Shadow IT

In the end, security teams, particularly those charged with securing large organizations, will never entirely prevent employees from occasionally turning to unauthorized systems. They can, however, reduce the impact that these systems have on the organization’s overall risk landscape.

One way to do this is to reexamine the organization’s workflows. Try to identify which formal processes are unnecessarily cumbersome, forcing employees to work around them, and figure out whether they can be improved. When properly managed, formal adoption of the Shadow IT systems employees are already using can be an effective solution.

Improving workflows in this way will begin to help to address the problem, but it will not be the first step an organization takes in the fight against Shadow IT: first it must discover that it has a problem that needs solving, and this cannot be done until its security team uncovers the sheer amount of shadow IT lurking within the organization. The first step, therefore, is to find a way of obtaining total visibility over every system in the digital environment. 

The Power of Prevention

For years Darktrace has illuminated the assets silently lurking within an organization, and now Darktrace / Attack Surface Management is finally giving security teams a clear and complete view over the external attack surface of their digital estates, including all of the shadow IT they didn’t previously know about. It does so by continuously monitoring assets and connections on the attack surface for risks and vulnerabilities. On average, this process reveals 30-50% more externally-facing assets than were previously known to the organization’s IT team and, importantly, analyzes the respective risk posed by each. 

This information is visualized for the security team in a way which makes it simple to determine the ownership of each system and asset, and helps teams to prioritize those vulnerabilities which require the most attention. 

This was crucial recently for an organization that had just been hacked through a shadow IT website created by the marketing department, without the knowledge of the security team. The company immediately brought on Darktrace / Attack Surface Management (ASM) to increase their cyber resilience, and the technology identified 12 urgent vulnerabilities due to shadow IT and misconfigurations which allowed the company to plug those holes before a repeat event occurred.

But having a holistic understanding of the risks of shadow IT requires looking beyond the external attack surface. To this end, Darktrace / Proactive Exposure Management identifies and evaluates all of the attack paths which exist in an organization, and reveals unknown devices which may sit along them. These devices may prove to be components in the middle of critical attack paths leading to precious data or vulnerable assets, but Darktrace minimizes this risk by identifying them and assessing the risk they pose to the environment.

At another organization, Darktrace recently identified a disaster recovery domain controller that was supposed to be an exact replication of the production domain controller. Being a standby for the main domain controller, this device was not regularly monitored by the IT team. However, Darktrace continuously monitors assets within a customer’s environment and identified that, even though they should in theory be the exact same, the back-up domain controller had different potential damage scores due to a Microsoft patch failing to install. No one in the IT team had identified this risk, with Darkrtace identifying the need for patching before to avoid the vulnerability being exploited – and critical data falling into the wrong hands. 

From there, it’s up to security teams how they wish to proceed. Some systems and assets may pose too great a risk and will need to be closed off, while others, particularly those which are already widely used within the organization and can be easily secured by the IT department, may be allowed to stay. What matters, is that the ‘shadow’ of shadow IT – the element of mystery which makes these systems such a hazard to security teams – has been lifted. With full visibility over every system and asset, and a clear understanding of which ones constitute network vulnerabilities, security teams no longer need to live in fear of their own organization’s digital environments.

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
Dan Fein
VP, Product

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June 26, 2026

How Darktrace Transformed Cybersecurity at Our Health Center: A CIO’s Perspective

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How Darktrace Transformed Cybersecurity at Our Health Center: A CIO’s Perspective

In my role as CIO, I bring years of experience leading IT for healthcare organizations. I’ve seen firsthand the unique cybersecurity challenges that nonprofit health centers face: limited budgets, small IT teams, and the constant pressure to prioritize patient care over technology investments. Yet, the threat landscape for health is relentless, and the stakes for protecting patient data and ensuring operational continuity have never been higher. It’s a balancing act.

The search for a better solution

Like many nonprofits, organizations I work at start with Microsoft’s security stack. The discounted pricing for nonprofits makes it an obvious choice, and Microsoft Defender provided a solid foundation for endpoint and email security. However, I quickly realized that relying on a single vendor, even one as robust as Microsoft, left gaps in our defenses. Cybersecurity is never one-size-fits-all, which is why my preference was to layer an additional solution on top of our native security to improve our security posture.

Teams needed a solution that could layer seamlessly on top of Microsoft, without adding complexity or draining limited resources. That’s when I found Darktrace. I had heard of their reputation after seeing how other organizations used Darktrace to secure their infrastructure and was impressed by their AI-native, agentless approach and agreed to a proof of value (POV).

Our goal was to elavate Microsoft with an additional layer of intelligence- one that could seamlessly integrate, operate autonomously, and support a small team without increasing overhead. We turned to Darktrace because its AI-native, agentless approach offered a fundamentally different way to detect and respond to threats, learning our environment in real time and filling gaps that traditional tools can miss. With a quick POV, we were able to validate how effectively Darktrace works alongside Microsoft to deliver a more complete and resilient security architecture.

Why Darktrace stood out

From the start, Darktrace differentiated itself in several critical ways:

  • Deep visibility: Unlike other solutions that rely simply on host-based monitoring with endpoint agents, Darktrace operates passively at the network layer and integrates via APIs for email and identity security. This gave full visibility into network traffic that we previously didn’t have, going beyond our existing endpoint-based tools without adding additional maintenance overhead for our small IT team.
  • AI-native from the ground up: Darktrace wasn’t just layering AI on top of an existing product; it was built with AI at its core. Their autonomous detection and response to threats immediately reduced the need for constant human supervision. In a world where cyber-attacks are increasingly sophisticated and subtle, having an AI that learns our environment and adapts in real time is invaluable.
  • Comprehensive coverage: We started with a POV focused on email security, but quickly expanded to full deployment across our entire infrastructure. Darktrace’s products now protect our email, network, and identity layers, providing visibility and defense against lateral movement and abnormal behavior that traditional tools often miss.

Integration and workflow: Smooth and simple

One of the most impressive aspects of Darktrace is how easy it was to integrate into an existing environment. For network security, it was as simple as plugging an appliance into our top-of-rack switch – no downtime, no complex configuration. For email and identity, API integrations meant we could be up and running in hours, not weeks.

This simplicity extended to day-to-day operations. Our IT team received regular security reports, and any time we had questions or needed to adjust policies, Darktrace’s support team was there with white-glove service. Their responsiveness- even in the middle of the night- gave us confidence that we had true partners, not just a vendor.

Real-world impact: Threats stopped, time saved

The results spoke for themselves. During the time with Darktrace, I did not experience any security incidents. The team slept better at night knowing that Darktrace was monitoring for anomalies and proactively blocking suspicious activity, alerting us even before we noticed anything was wrong.

A memorable example was during an Electronic Health Record (EHR) upgrade, when my team forgot to adjust the policy in advance. Darktrace’s autonomous response was so effective that it blocked our upgrade activities- proof that nothing, not even internal changes, could slip by unnoticed. This level of vigilance meant that ransomware, data exfiltration attempts, or insider threats would be detected and contained before causing harm.

While I can’t share specific ROI numbers, the value was clear: we’ve avoided costly breaches, reduced the time spent investigating alerts, and eliminated the performance drag of agent-based tools. With Darktrace layered on top of Microsoft, I’ve hit the right balance of maximum protection with minimal spending. The cost of Darktrace / EMAIL was competitive, especially when factoring in the included Managed Detection and Response (MDR) service, which provides expert human oversight on top of the AI.

Key differentiators over the competition

  • Extending visibility beyond the endpoint: Traditional host-based monitoring solutions, such as EDR, play a critical role in securing individual devices. By adding a network detection and response (NDR) layer, we gained visibility into activity across our wider digital environment, surfacing threats that move laterally, operate between devices, or bypass endpoint controls. Darktrace also stood out for its ability to learn our normal patterns of behavior and identify subtle deviations in real time, not just known indicators of compromise. Because this is delivered through passive, non-disruptive monitoring, we were able to strengthen our defenses without adding complexity or impacting performance.
  • Layered security without complexity: Darktrace elevated our Microsoft foundation without creating conflicts or requiring us to disable existing protections. This layered approach maximized our security posture without adding operational burden.
  • Expert partnership: Beyond technology, Darktrace’s team acted as true partners, guiding us through deployment, providing ongoing support, and helping us interpret findings. This partnership was as valuable as the technology itself.

Advice for other nonprofits

If you’re an IT leader in a nonprofit, my advice is simple: look for solutions that are easy to deploy, intelligent in their response, and cost-effective. Don’t settle for more endpoint based tools that overlap with what you already have. Seek out a layered approach that covers your blind spots – especially at the network and email layers- at a price point that suits your organization.

Most importantly, don’t be afraid to evaluate new solutions. Even if you’re inundated with vendor pitches, you owe it to your organization to explore options that could save you time, money, and sleepless nights.

For organizations I work at, combining Microsoft’s security stack with Darktrace’s AI-native, platform struck the right balance between protection and practicality. We gained enterprise-grade security without sacrificing performance or stretching our budget. In the end, that meant more resources for what matters most: delivering care to our patients. If you’re facing similar challenges, I encourage you to consider how Darktrace could transform your security posture, and give your team the peace of mind they deserve.

For the organization I work in, combining Microsoft with Darktrace delivered a clear step-change in our security posture. Microsoft provided the foundation, while Darktrace’s behavioral intelligence added visibility into the unknown, surfacing emerging threats based on deviations in real-time activity, not just known indicators.

The result was enterprise-grade protection without added overhead, allowing us to stay focused on patient outcomes, not security operations. For organizations facing similar pressures, this layered approach offers a smarter, more efficient path to securing modern environments.

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About the author
Mice Chen
Chief Information Security Officer

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June 25, 2026

Shadow AI Detection: The First Step Toward Securing AI

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Why shadow AI is emerging  

Imagine you’re an employee under pressure, deadlines stacking up, repetitive tasks piling higher by the day. You find a free AI tool online that promises to automate the work in seconds; no approvals are needed. It feels like a simple win, paste in some data, write a quick prompt, and move faster.

But in that moment, something changed.  

Sensitive customer information is entered into a tool your organization doesn’t monitor, doesn’t govern, and can’t see and suddenly, that data is no longer where it should be, and no one knows where it’s gone.

This is the reality of Shadow AI: employees using unsanctioned AI tools to move faster, while unintentionally creating risk that exists entirely outside visibility and control.  

This is not just a one off case, research across businesses indicate that nearly half of employees report using unsanctioned AI tools, often prioritizing speed and productivity over security. Additionally, 51% of employees report connecting AI tools to work systems or apps without IT approval, creating significant operational risk where the average cost of security incidents in organizations with a high level of shadow AI usage can reach $670k.

While shadow AI is often top of mind for security professionals, it is just one component of how AI use can increase risk. Understanding and managing shadow AI use should be considered as part of a broader, comprehensive risk management strategy that aims to secure AI systems, including human and agent identities, interactions, human-AI partnerships, and behaviors operating across the digital enterprise from visibility and governance through detection, response, and recovery.  

Effective risk management calls for a layered and interdisciplinary strategy. It requires addressing issues across governance and visibility; identity, access and agent control, data security and privacy, secure MLOps / LLMOps, runtime security, behavior-based detection, autonomous response and recovery.  

This blog explores a specific governance and visibility use case linked to shadow AI and reveals the challenges it presents as well as the defensive strategies that security teams can adopt.

Why shadow AI is hard to detect  

When it comes to AI, what organizations can easily see does not always reflect the full scope of AI activity occurring within the tools, applications, and workflows used across an enterprise. As a result, organizations using traditional rule-based methods to flag unusual activity may struggle to distinguish unsanctioned AI usage from legitimate operational behavior, particularly as SaaS applications, APIs, and orchestration layers increasingly have AI embedded into normal business workflows. Identifying threats using previously observed intelligence or depending on hard to maintain allow and block lists does not provide a dynamic enough strategy to manage risk. Also, many organizations are focusing on identifying Shadow AI in their governed infrastructure, like gateways, endpoints, or SASE, which is foundational. But, organizations require visibility and Shadow AI detection across all networked infrastructure from on-prem, hybrid, data centers, and cloud infrastructure that may not have endpoint agent visibility. This uncovers the utilization of MCP, data flows, and autonomous agents across these domains.

For example, employees interact with AI assistants across approved SaaS platforms every day. However, browser extensions and other types of plug-ins can route prompts that include enterprise data to embedded AI services in ways that are not visible to the security team. AI enabled workflows may invoke multiple APIs, orchestration layers, and cloud services behind the scenes, making it difficult for traditional security tooling to determine where data is processed, stored, or retransmitted. Because much of this activity occurs within trusted browser sessions and encrypted SaaS traffic, conventional network monitoring, DLP, and application allowlisting controls often lack the context needed to accurately identify or govern these interactions

Identifying AI tools in the environment is one part of the equation. Understanding the behavior surrounding their use is where the real challenge lies. An AI application is not inherently risky, but the way users or other assets interact with it may be. Sensitive data exposure, abnormal access patterns, and misuse of AI-assisted workflows often appear legitimate in isolation and only become visible through behavioral analysis across the broader environment.  

What Shadow AI visibility does and doesn’t show

Comprehensive Shadow AI visibility allows organizations to answer several important questions:

  • What types of AI are we using? What AI platforms, agents, MCP clients/servers, and services are active across the enterprise?  
  • Who is using AI services? Which users, business units, or systems are interacting with those AI services?  
  • Is our data safe? Is sensitive or regulated data being exposed through prompts, workflows, or integrations?  
  • Are AI systems behaving as expected? Are AI systems behaving anomalously or operating outside approved governance processes?  
  • Are our AI systems under attack? Is an attacker attempting to manipulate prompts, influence agent behavior, or abuse AI-enabled workflows?

Answering these questions is foundational to broader AI governance efforts. However, it is limited to helping teams understand initial interactions and fails to offer insight into dependencies and outcomes that are critical to securing AI across an enterprise.  

Deeper visibility that includes the ability to understand dependencies and outcomes are not always available in AI security point products. Answering the questions below requires understanding runtime behavior and operational outcomes:  

  • What actions did the AI interaction trigger?  
  • What systems, applications, or data did it access? Did the AI operate beyond its intended permissions or scope?  
  • Could a low-risk interaction lead to high-risk outcomes?  
  • What is the risk and context understanding of an anomalous activity to assist in prioritization of analysis and autonomous response action?

The distinction between these two sets of questions offers two different layers of AI security. The first set of questions focuses on discovery and interaction visibility. The second set focuses on providing visibility that includes the context and outcomes that are critical for managing follow-on risks associated with obfuscated downstream activities.  

Together, these layers help organizations move beyond simply identifying AI usage toward understanding how AI behaves operationally across the enterprise.

How organizations are addressing shadow AI

Most organizations still approach shadow AI as an application control problem, relying on policies, browser restrictions, and allow/block lists. However, AI adoption is evolving faster than most governance processes can realistically keep pace with. New assistants, plugins, and embedded AI features appear continuously, creating pressure to enable business productivity while simultaneously containing risk.  

Existing governance processes were designed for a more traditional SaaS adoption cycle, where new applications could be reviewed, approved, and monitored over longer time horizons. AI adoption operates differently. New capabilities can appear overnight inside existing platforms employees already use, making it difficult for security and governance teams to maintain an accurate understanding of enterprise AI exposure. This means that many organizations are experiencing significant operational overhead, particularly in large environments where AI usage is decentralized across teams, departments, and third-party services.  

Where should organizations start when securing their AI systems?

Shadow AI identification is an on-going critical component for AI Risk/Governance Boards as well as security organizations. As organizations seek AI certifications like ISO 42001 AI Management Systems, visibility into all AI adoption from enterprise use to custom innovation and development is crucial. Shadow AI identification provides organizations with the visibility needed to decide whether an AI tool should be brought into governed environments to reduce data loss (DLP) risks or whether policies should be established and enforced to restrict their use.

As organizations rapidly innovate and adopt AI, they are taking on more and more risk. Organizations need to have a strategy in place to mitigate the assumed risk, especially with third-party adoption. Visibility, monitoring, governance enforcement, behavioral-based detection of non-deterministic systems, and autonomous investigation and containment becomes critical to mitigating the risk of AI systems.  

How Darktrace secures AI and shadow AI

Attackers are using AI to move faster, scale tactics, and make threats more adaptive and convincing. Internally, organizations are grappling with new forms of risk created by generative AI, autonomous agents, shadow AI, and increasingly complex digital environments.

Darktrace helps organizations protect both people and AI in a world where AI is now central to how business gets done. Darktrace / SECURE AI helps organizations discover and control shadow AI by surfacing unsanctioned or unexpected AI activity where it appears – including MCP detections, distinguishing misuse of legitimate tools and unapproved services, and applying policy to contain data exposure while guiding users toward sanctioned options.

Stay up to date on AI security

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.  

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