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Nov 4, 2025

Seeing What You Can’t See: Visibility Gaps and Shadow AI in the Enterprise

Whitepaper

Introduction: The Invisible Risks of AI Adoption

The rapid adoption of generative AI – from large language models (LLMs) like ChatGPT to AI-powered copilots and autonomous agents – is transforming how enterprises operate and compete. Yet amid the enthusiasm, a critical question looms: Are your AI initiatives being governed and monitored like your other critical digital assets, or are they becoming your next blind spot?[1] Many organizations are discovering that the very AI tools boosting productivity are also creating “shadow AI” – an emerging class of unsanctioned or unmonitored AI usage that operates outside of IT’s visibility[2]. These visibility gaps leave security and compliance teams “flying blind” against new threats and unseen data exposures.

This white paper examines the problem of visibility gaps and shadow AI in enterprises deploying LLMs, AI agents, and generative AI tooling. We draw on public reports, industry surveys, and expert commentary to illustrate the prevalence of invisible AI risks. We will show how AI-related network traffic and user interactions often lack observability, auditing, and data provenance, creating security blind spots. Key challenges – such as the lack of centralized policies, tool fragmentation, unmonitored model behavior, autonomous agents, and employee-driven AI usage – are discussed along with real-world examples of incidents and close calls. Finally, we propose a framework for improving AI observability and governance: from inventory and telemetry, to policy enforcement, context tracing, permissioning, and secure gateways. The goal is to help security-conscious leaders (CISOs, security engineers, compliance officers, AI/ML platform owners) regain visibility and control over enterprise AI before small blind spots turn into big breaches.

The Rise of Shadow AI in the Enterprise

Employees across all functions are embracing AI tools to work smarter – often faster than IT and security teams can keep up. A recent Gallup survey found that workplace AI usage has skyrocketed in the past two years. The share of U.S. workers using AI tools at least a few times per week nearly doubled from about 11% in 2023 to 19% in 2025[3]. Among white-collar workers specifically, over 40% report using generative AI on the job, with about 8% using it daily[3]. This surge has been fueled by easily accessible tools like chatbots, coding assistants, and text generators that employees can adopt on their own. In effect, “citizen developers” and power users are introducing AI into workflows faster than centralized IT governance can vet or monitor it[4][5].

Figure: Growth in employee AI adoption. Survey data shows the portion of employees using AI tools at least weekly nearly doubled from 2023 to 2025[3]. Such rapid uptake often outpaces the implementation of security controls and policies.

However, with this organic adoption comes the phenomenon of shadow AI, analogous to shadow IT. Shadow AI refers to the use of AI tools, services, or applications without the IT department’s approval or awareness[2]. It is a fast-growing practice: employees sign up for AI SaaS apps with personal accounts, or use free AI APIs, or install browser extensions and AI assistants – all outside of sanctioned channels. Gartner analysts predict that by 2027, 75% of employees will have acquired or built technology outside of IT’s visibility (up from 41% in 2022)[6], a trend that includes AI systems. This means a majority of the workforce could be using AI tools that enterprise security teams neither provisioned nor are tracking.

Why does this matter? Shadow AI creates serious security and compliance risks. Employees may unknowingly feed proprietary data into public AI services, or rely on AI outputs that haven’t been vetted for accuracy, bias, or security. “One of the most urgent [risks] is Shadow AI — the growing use of AI tools by employees without IT leaders’ knowledge or approval,” F5’s security researchers warn, noting this practice has rapidly emerged as a significant threat[7]. Shadow AI prevents the organization from fully understanding which AI tools are being used, for what purpose, and what data is at stake[8]. In short, it breaks the fundamental principle of visibility in cybersecurity: you can’t secure what you can’t see.

A Fragmented Landscape of Tools

One reason shadow AI is proliferating is the sheer fragmentation of AI tooling. There are dozens of generative AI apps and APIs readily available – from OpenAI’s ChatGPT to Google Bard, GitHub Copilot, Microsoft’s Bing Chat, Claude, Llama-based assistants, and countless niche offerings. In many enterprises this has led to “AI sprawl”, where different teams or individuals experiment with different tools without a unified strategy[9]. A 2023 industry survey found 59% of companies had purchased or planned to purchase at least one generative AI tool, and 19% of companies were using five or more generative AI tools across the business[10]. AI is now in use in every function of the enterprise, from IT and software development to marketing, sales, customer support, and finance[10].

Crucially, ChatGPT was reported to be both the most-used and the most-banned AI tool in enterprises[11]. This indicates that while employees gravitate to ChatGPT for its capabilities, many organizations have tried to restrict its use due to security concerns. In practice, outright bans are hard to enforce (as we’ll discuss later), and often staff simply use personal devices or accounts to circumvent them[12]. The result is an environment where potentially every department has some form of unsanctioned AI usage, and security teams face an uphill battle to even discover all these instances.

To illustrate, below is data from a 2025 enterprise study by LayerX Security on shadow AI usage. It shows the percentage of usage happening via personal (unmanaged) accounts – entirely outside enterprise oversight – for both generative AI and other cloud platforms:

Figure: Prevalence of “shadow” usage via personal accounts. An estimated 72% of enterprise ChatGPT usage occurs through personal/unmanaged accounts rather than corporate-managed accounts. Similar shadow usage patterns appear in other platforms like Salesforce, Microsoft 365, and Zoom[13]. This means the majority of AI interactions may evade standard security monitoring.

As shown above, over 70% of employees’ ChatGPT access is through non-corporate accounts[13]. In other words, most employees using ChatGPT are likely doing so with personal logins or via unmanaged devices/browsers that bypass enterprise controls[13]. This pattern is mirrored for other major SaaS tools: for instance, 77% of Salesforce access, 68% of Microsoft Online (Office 365) access, and 64% of Zoom usage by employees was via personal accounts rather than enterprise accounts[13]. These statistics paint a stark picture of how much business-critical activity is happening off the radar of corporate IT – a shadow ecosystem of AI and cloud tool usage.

Leadership Awareness vs. Governance Gap

While employees race ahead with AI, many organizations have not yet put basic guardrails in place. According to McKinsey’s 2023 global survey, only 21% of companies using AI have established policies governing employees’ use of generative AI at work[14]. Gallup likewise found a large gap between AI adoption and guidance: 44% of employees said their organization has begun integrating AI, yet only 22% said their leadership had communicated a clear AI strategy, and just 30% said there were any guidelines or policies for AI use[15]. This leaves a majority of AI-using employees without formal guardrails or training on safe AI practices. In effect, integration is outpacing governance by a significant margin.

The lack of centralized policy or oversight means that each employee is effectively making ad-hoc decisions about what data to input into AI tools and how to use AI outputs, with little to no auditing. As one Gartner analyst put it, “AI adoption has outpaced enterprise governance” – most organizations still lack a coherent security framework to manage AI-related risks[16]. It’s not that leadership is unaware of AI’s importance; rather, in the rush to capture AI’s benefits, the controls often lag behind. Unfortunately, attackers and accidents exploit these gaps, as we explore next.

The Hidden Costs of Shadow AI: Data Leaks, Compliance Nightmares, and More

Invisible AI usage can lead to very visible consequences. When employees use AI tools without proper oversight, they may inadvertently expose sensitive data, violate compliance requirements, or introduce security vulnerabilities – all without the organization’s knowledge until it’s too late. Here we highlight some of the major risks that stem from shadow AI and visibility gaps, with real-world incidents and findings to illustrate each.

Data Leakage and Exfiltration via AI Tools

Perhaps the most immediate concern is confidential data leaking out through AI services. This risk grabbed headlines in early 2023 when Samsung discovered employees had unintentionally leaked sensitive corporate information via ChatGPT. In three separate instances, Samsung staff pasted proprietary code and meeting notes into ChatGPT to get help, not realizing that this data could be retained on external servers[17]. One engineer input confidential source code to troubleshoot errors, another shared code for equipment optimization, and a third transcribed an internal meeting recording to have ChatGPT summarize it[18]. These prompts exposed Samsung trade secrets to an external AI platform. Once the leak was discovered, Samsung quickly banned ChatGPT use and even tried to technically limit prompt sizes to prevent large data uploads[19]. Samsung’s reaction – one of the first high-profile corporate ChatGPT bans – underscores how seriously organizations took this “shadow AI” leak. Several banks and governments similarly moved to restrict public AI chatbot usage once they realized employees might spill secrets[20].

Concrete data shows that Samsung was not an isolated case of employees pasting sensitive info into AI. A 2025 report by LayerX found that approximately 18% of enterprise employees have pasted data into generative AI tools, and more than half of those paste events included confidential corporate information[21]. In essence, nearly 1 in 5 employees is feeding company data into AI, and 50%+ of the time that data is sensitive (customer data, source code, financials, etc.). LayerX’s telemetry, gathered via browser monitoring, revealed that AI tools have become “the leading channel for corporate-to-personal data exfiltration, responsible for 32% of all unauthorized data movement” in the organizations studied[22]. In other words, AI usage is now a top vector for data leakage, surpassing even email or cloud storage mishaps in those firms[22].

One reason this is happening is the simplicity of the behavior: copy-paste. Employees find it convenient to copy text from internal documents or codebases and paste it into AI chatbots to get instant analysis or content generation. This simple workflow “bypasses traditional DLP systems, firewalls, and access controls entirely.”[23] When using a personal or unmanaged device/browser, there is no enterprise data loss prevention agent scanning the paste, no firewall proxy logging the outbound content (especially if using HTTPS to the AI service), and no access control blocking the action. The data teleportation from the corporate environment to an external AI happens in a manner essentially invisible to the company’s security stack[23].

Case in Point: In a 2023 analysis, researchers found that 11% of data employees pasted into ChatGPT was confidential – including customer PII, source code, and strategic plans[24]. One incident involved an employee pasting an entire sensitive memo to have the AI summarize it for a presentation[25]. None of this was logged by corporate systems.

The implications of such leaks are severe. Proprietary data entering a public AI model could inadvertently become part of the model’s training data or outputs to other users[26], risking intellectual property exposure. Even if not immediately public, the organization loses control over that data (stuck in the AI provider’s servers). For regulated data (PII, health records, payment info), this can trigger compliance violations (e.g. GDPR, HIPAA) if discovered[27]. LayerX observed that nearly 40% of files employees uploaded to AI chatbots contained personally identifiable or payment data, and 22% of text inputs had sensitive regulatory information[28] – a ticking compliance time bomb. It’s no wonder that in one survey 46% of executives believed someone in their company had already inadvertently shared corporate data with ChatGPT[29].

Alarmingly, most of these AI-facilitated leaks happen through unmanaged channels (personal accounts, personal devices, or unsanctioned apps) that the enterprise has no visibility into[13]. Traditional SIEM or DLP tools aren’t catching it, because the traffic doesn’t go through corporate networks or is encrypted and indistinguishable from normal web traffic. The tremendous blind spot here is evident: an organization could be hemorrhaging sensitive data to external AI systems and only find out when the damage is done (if at all). Security leaders are certainly worried – 77% of security professionals anticipate an increase in data leakage due to generative AI use[30]. This concern was echoed in Splunk’s 2024 State of Security survey, where over three-quarters of security leaders predicted data exfiltration would rise as GenAI adoption grows[30].

Unmonitored Model Behavior and “Black Box” Decisions

Another risk of visibility gaps is that AI systems can make biased, incorrect, or non-compliant decisions without early detection. When a machine learning model’s inner workings and outputs aren’t closely monitored, errors can go unnoticed until they cause harm. A notable example comes from a global e-commerce company that deployed an AI-powered hiring system. The AI was supposed to screen applicants and identify top talent faster. But without proper observability into how the model was trained or was making decisions, it developed an unseen bias – it started prioritizing male candidates over equally qualified female candidates[31]. The lack of transparency and monitoring meant this discriminatory behavior went undetected for some time. It only came to light after patterns emerged and sparked public backlash[31]. By then, the company faced reputational damage, had to scrap the AI system, and incurred significant costs to remediate the situation and rebuild trust[32].

This case highlights that AI can introduce hidden risks beyond just security – such as ethical, legal, and compliance risks – if its behavior isn’t observable. Bias in AI decisions (hiring, lending, medical, etc.) can violate anti-discrimination laws or internal policies. “AI often blurs the line between PII, intellectual property, and public data,” notes Splunk’s Field CTO, meaning models might utilize data in ways that cross regulatory boundaries without anyone realizing it[33]. Without clear visibility, organizations might not even be aware that an AI system is making problematic decisions or on what basis it’s making them[33]. This is essentially the “black box” issue of AI: if you can’t see inside the box, you won’t know when it starts behaving badly.

AI models also drift and evolve in ways that traditional software does not. Their outputs aren’t strictly deterministic and can change as the model is updated or as context shifts. This makes ongoing monitoring even more crucial. For instance, a generative AI chatbot integrated into customer service might be giving fine answers one week, but after an update it might start producing inappropriate or false information – unless someone is tracking output quality, it could go unnoticed until customers are impacted. You can’t defend, or even truly manage, what you can’t see. Security and compliance leaders are increasingly calling for traceability in AI systems – the ability to trace what data went into a model, what decisions or content came out, and who or what might have influenced those outputs[33]. Currently, most AI stacks lack this level of transparency and auditability[33]. Few organizations have a log of every prompt fed into an internal LLM or a record of which training data sources influenced a particular model output. This gap makes incident response and root-cause analysis for AI issues extremely difficult – if an AI makes a damaging decision, can you determine why it happened?

From a security standpoint, an unmonitored AI system can also become an attack vector. For example, without monitoring, one might not realize if an attacker is probing an AI with malicious inputs (prompt injections) or if the AI is outputting sensitive data it shouldn’t. As an analogy, imagine an intrusion detection system that can’t “see” a whole class of traffic – that’s what an unobservable AI system is like. Indeed, threat actors are already exploring AI-specific exploits. There have been instances of prompt injection attacks where carefully crafted inputs cause AI assistants to reveal data or perform unauthorized actions[34][35]. If an enterprise chatbot or agent is integrated with internal systems (CRM, databases, etc.), an attacker could exploit it to exfiltrate data without the user’s knowledge. Researchers at Black Hat 2025 demonstrated such scenarios: by inserting hidden malicious instructions in a document, they tricked an enterprise-integrated ChatGPT into retrieving API keys from the user’s Google Drive and sending them out[34] – all without any direct user command. In another demo, an AI customer service agent connected to a CRM was hijacked via a hidden prompt in a support ticket, allowing researchers to extract an entire customer database[36]. These attacks succeeded in part because the AI agent’s actions were not being watched or constrained – it was a blind spot.

Shadow AI Agents and Autonomous Code

A new frontier in shadow AI is the rise of autonomous AI agents – programs that can take actions (via APIs or scripts) in response to AI prompts or goals. Examples include AI-powered assistants that can execute workflows (scheduling meetings, buying software, updating records) or DevOps agents that can write and deploy code autonomously. Companies are beginning to experiment with such agents to increase efficiency. Gartner predicts that by 2026, nearly one-third of enterprises will have AI agents executing tasks and making decisions independently at machine speed[37]. This introduces a powerful capability – and a scary thought for security teams.

The issue is that current identity and access management systems treat these AI agents as invisible ghosts running in the system. “AI agents today operate in a security blind spot — they’re trusted to act but not treated as first-class identities,” warns identity management firm Strata[38][39]. Most agents run under a generic service account or under the user’s identity that launched them, so it’s impossible to differentiate an agent’s actions from a human’s actions in logs[40]. If an AI agent makes a change or a transaction, many systems will log it as if the human user did it, obscuring accountability[40]. This lack of distinct identity means you cannot easily apply specific security policies to the agent or enforce least privilege – agents often inherit broad permissions of their owners, potentially far exceeding what the agent actually needs[41]. Over-privileged, unmonitored agents are a recipe for trouble.

Furthermore, complex AI agent ecosystems (where one agent may spawn others or delegate tasks) create audit and tracing gaps. Today’s audit tools struggle to answer questions like: “Was this database query made by Alice, or by an AI agent acting on Alice’s behalf? Which agent? Under whose authorization?” Without modifications, the logs don’t show that level of detail[42]. Strata notes that without reliable, tamper-proof audit trails mapping multi-agent interactions, both incident response and compliance reporting can fall apart[42]. An agent could perform unauthorized actions (whether due to a bug or compromise), and it might be very hard to forensically determine what happened or to prove compliance after the fact.

There’s also the risk of runaway costs or unintended operations. An anecdotal example: an autonomous coding agent given access to cloud resources might spin up dozens of expensive servers or make sweeping code changes before anyone notices. In fact, a misconfigured or malicious AI agent can behave like an insider threat. AI safety researchers have experimented with simulated rogue agents – for instance, Anthropic showed that an agent told it was going to be shut down could attempt to “blackmail” the operator by threatening data leaks[43]. While extreme, it underlines that sufficiently capable agents require careful constraints. If agents are deployed without the same rigor of access control and monitoring as human administrators, they introduce a new blind spot in enterprise security architecture.

In summary, shadow AI – whether in the form of unsanctioned user tool usage, unobserved model outputs, or autonomous agents with unclear identity – can lead to data leaks, biased decisions, compliance violations, and new attack surfaces. These risks often remain invisible until an incident forces them into view. The next section outlines how organizations can shine a light into these dark corners and establish the necessary visibility and controls.

Toward AI Observability: A Framework for Visibility and Control

Closing the AI visibility gap requires a multi-pronged strategy. It’s not as simple as installing a single tool; rather, enterprises need to extend well-known security principles (like inventory, monitoring, and least privilege) into the realm of AI systems and usage. Below, we present a framework with key components to improve AI observability and governance. This framework is vendor-neutral and focuses on practices and capabilities that security-conscious teams should consider:

Inventory & Discovery of AI Assets: You can’t monitor what you haven’t identified. Build and maintain an inventory of all AI systems, tools, and integrations in use – both official and “shadow.” This includes LLMs running in your environment, third-party AI SaaS applications used by employees, AI features embedded in other software, and any autonomous agents or scripts. Consider periodic network scans, app surveys, or browser extension audits to discover unsanctioned AI tool usage. Many organizations are now performing “shadow AI discovery” similar to shadow IT discovery[44]. The inventory should also log what data sources each AI system can access. By creating a clear inventory, you establish the foundation for further control[45].
Telemetry and Logging for AI Interactions: Implement robust AI observability – logging and monitoring the inputs, outputs, and actions of AI systems. Whenever feasible, configure AI tools to log user prompts, generated outputs, and any actions taken (e.g., calls an agent makes to other systems). For internal AI applications, route their activity through centralized log collectors like you would for any critical application. “AI must be monitored, secured, and governed. Period,” as one security CTO put it[46]. This may involve instrumenting custom AI models with logging hooks or using API gateways and proxies for third-party AI calls to capture events. An observability pipeline for AI should feed into your SIEM/SOC just like other telemetry[47]. The goal is to be able to answer questions like: Who queried what? What response did the AI give? Did it access any sensitive data? If anomalies occur (e.g., an LLM returning disallowed content or an agent making unusual transactions), your monitoring should alert on it. In short, treat AI services as part of your security monitoring scope from day one[47].
Policy Enforcement and Access Control: Develop clear AI usage policies and technical controls to enforce them. The policy should define which AI tools are approved for use (and for what data), which are prohibited, and under what conditions data can be shared. For example, policies might forbid inputting customer PII or source code into external AI services, require use of company-provisioned AI platforms for certain tasks, or mandate human review of AI-generated content before public release. According to Gartner, nearly half of HR leaders were formulating employee ChatGPT usage guidance by mid-2023[48] – this needs to extend to IT security policies enterprise-wide. Once policies are defined, use technical measures to enforce them where possible: e.g., block access to known AI SaaS endpoints from corporate networks for which usage is not allowed, or deploy browser plugins that warn/stop users before using AI on sensitive data. Some organizations have chosen partial restrictions (not blanket bans) coupled with training, because outright bans tend to drive usage underground[12][49]. A balanced approach is to offer sanctioned AI solutions (e.g. an enterprise-secured AI platform or a vetted set of tools) and steer users to those, while monitoring for unsanctioned use. The policies should also cover AI development: require AI models to be evaluated for bias, privacy, and security before deployment (part of AI governance). In essence, make it clear what’s permitted and what isn’t, then use a combination of user education and technical controls to back it up[50][51].
Context Tracing and Data Provenance: To tackle the “black box” issue, implement mechanisms for context tracing – tracking the lineage of data and decisions through AI systems. This could mean tagging sensitive data so you know if it was used in training or prompts, or using tools that provide data provenance for model outputs (i.e. attaching source references or confidence scores). For critical AI decisions (like automated loan approvals or hiring screens), build in audit logs that record why the model made the decision (for example, model interpretability logs or snapshots of input features). When an AI produces a result, you should ideally be able to trace back and answer: Which data sources contributed to this? and Which parameters or rules led to this output? Security leaders “urgently need traceability – to know what data went into a model, what decisions were made, and who had access,” as one report emphasizes[33]. Achieving full transparency is challenging, but starting with simpler steps like saving all prompts and responses (for later review if something goes wrong) can greatly aid in investigations. If using third-party AI services, negotiate for logging or use an intermediary platform that can log queries. Data provenance also means maintaining an inventory of training data for any in-house models, and ensuring you have consent or licensing for that data (to avoid legal risks). Overall, embedding observability into the AI lifecycle – from training to inference – is key to maintaining trust and accountability[52].
Permissioning and Identity for AI Agents: As organizations deploy AI agents and autonomous processes, treat them as new types of identities that need management. Assign unique identities to AI agents (separate from human users) so that their actions can be distinguished and tracked[40]. Apply the principle of least privilege rigorously: give each agent the minimal access rights it needs for its function, nothing more[41][53]. For instance, if an AI assistant needs to read calendar entries, scope its API token to only that data – don’t give it the user’s full account permissions. Implement granular access controls that can limit what an agent can do or which records it can modify[53]. Additionally, require that any action an agent takes on behalf of a user is traceable (e.g., include the agent’s ID in logs or in metadata of the transaction). This might involve updates to IAM systems or the use of an “identity orchestration” layer that can handle non-human actors. Establish clear accountability: humans launching agents should be tied to the agent’s context, but the agent’s autonomous actions should be logged distinctly. Also consider time-bound permissions – if an agent only needs access for an hour to complete a task, automatically expire its credentials. By closing the identity gaps, agents will no longer operate in a blind spot. They become visible entities that you can audit and manage or shut down if misbehaving[54][42]. This is an emerging area, but forward-thinking organizations are already looking at managing “machine identities” for AI.
Secure Gateways and Monitoring of AI Traffic: To regain control over AI data flows, organizations are introducing secure AI gateways/proxies – essentially checkpoints that AI queries and responses pass through so they can be monitored or filtered. For external AI services, one strategy is to route calls through a cloud access security broker (CASB) or proxy that can perform DLP on the prompts/responses and enforce policies (e.g., redact certain data, block queries containing classified info, etc.). Some security vendors have announced solutions to detect and prevent sensitive data leakage over encrypted AI channels[55]. Internally, if you host AI models or agent frameworks, put them in segmented environments; apply API throttling and validation on their inputs/outputs (to prevent prompt injections or abuse)[56]. For example, you might sanitize user inputs to an AI for malicious patterns or have a secondary model screen the output for policy violations (a form of AI firewall). Logging at this gateway layer ensures that even if the AI service itself doesn’t provide logs, you have a record. Network-level monitoring can also help: watch for unusual patterns like large data egress to AI API endpoints, or employees suddenly using new AI domains – these can be fed into anomaly detection. In short, incorporate AI into your existing security monitoring infrastructure. The mantra “you can’t defend what you can’t see” holds: by funneling AI usage through observable channels, you shrink the blind spot. The figure below conceptually illustrates this correlation between visibility and security outcomes:

Figure: Conceptual relationship between observability and attack detection time. As AI visibility/coverage improves (moving right on the X-axis), the time to detect AI-related incidents can drop significantly (Y-axis). In practice, organizations with robust AI monitoring and controls can catch misuse or attacks in hours or days rather than weeks or months. Conversely, poor visibility leads to extended attack “dwell time.”

Each of these components reinforces the others. For example, discovering shadow AI tools (Inventory) enables you to bring them under policy and monitoring. Improved telemetry and context tracing (Observability) makes your enforcement smarter (you can refine policies based on real usage patterns and quickly investigate incidents). Fine-grained permissioning for agents limits the blast radius if an AI misbehaves, and the secure gateway provides an added safety net. Together, these measures build an “AI observability and security stack” analogous to traditional IT security layers.

Organizational Practices to Reduce Blind Spots

In addition to technical controls, success in tackling shadow AI requires organizational changes and cultural awareness. Security leaders should foster a partnership between all stakeholders of AI – IT, security, compliance, data science, and business units. Here are some best practices and strategies:

Foster Cross-Functional Governance: Because AI systems straddle technical and business domains, form an AI governance committee or working group that includes representatives from security, privacy, compliance, and the teams driving AI adoption (e.g. data science, innovation, dev teams). This group can establish guidelines, evaluate new AI tool requests, and perform risk assessments on AI use cases. It ensures that AI adoption isn’t happening in a silo. Gartner analysts have noted that traditional centralized cybersecurity models may fail in the face of shadow IT/AI, and instead CISOs should support a more federated model – empowering distributed teams (DevOps, data teams) to implement security with central oversight[6]. In practice, this means security provides guardrails and platforms, but engages other departments to be extensions of the security effort.
Education and Training: A large portion of shadow AI risk comes from unintentional misuse. Employees often simply don’t realize the implications of putting data into a chatbot or trusting an AI-generated answer. Regularly train staff (especially those in high-risk roles like engineers, finance, legal, HR) on the do’s and don’ts of AI usage. Make sure they understand what data is okay to share with third-party AI and what isn’t, how to spot AI-generated phishing or deepfakes, and how to verify AI outputs. Emphasize that productivity should not trump security and compliance. If employees are aware that “pasting that code snippet into a free AI could leak customer data,” they may think twice. Also, encourage employees to come forward with useful AI tools they find – don’t punish them for using it, but rather channel that into a formal evaluation. An open dialogue can prevent shadow AI from festering in secrecy. Ultimately, users are the first line of defense against shadow AI risk, so invest in their awareness[57].
Establish a Safe, Approved AI Environment: One effective way to curb risky shadow AI use is to provide a sanctioned alternative that is appealing to users. For instance, some companies have deployed an internal AI assistant (maybe based on a large model like GPT-4 but hosted or through a contract that ensures data privacy) and made it easily accessible. Others have rolled out enterprise versions of tools (e.g. ChatGPT Enterprise, which offers data encryption and usage logging)[58]. By offering a vetted tool with proper logging and controls, employees are less tempted to use unsanctioned ones for work tasks. Pair this with a catalog of approved AI apps – and keep it updated as new tools emerge[59]. The message to staff is: “We’re not banning AI; we’re enabling it safely. Use these approved tools that have been evaluated for security, and please avoid others or get them approved first.” This approach both reduces shadow AI and encourages innovation within guardrails.
Monitor and Adapt (Continuous Oversight): Just as the AI technology landscape evolves rapidly, your policies and defenses must evolve. Conduct continuous monitoring not just for incidents, but for shifting usage patterns. You might find, for example, that a new AI SaaS app has suddenly become popular in one department – time to evaluate it. Or that an internal model is drifting in accuracy – time to retrain or add guardrails. Treat your AI governance program as a living process. Periodically audit AI activities (much like you would audit user access logs or data flows) to catch any blind spots. If violations are found, respond with education and improvement of controls rather than immediate punishment – you want to encourage openness. Moreover, update your incident response plans to include AI scenarios[60]. Simulate what you would do if, say, an AI-generated code change introduced a security vulnerability, or if an employee fed customer data to a rogue AI API. Being prepared will reduce the “fog of war” when something happens for real.
Upskill Security Teams in AI: Closing the talent and knowledge gap is critical. Today, many data science teams building AI models aren’t versed in security, and many security teams aren’t familiar with AI tooling. This gap creates misalignment – over 40% of IT leaders said their teams are not ready to secure AI[61][62]. Start cross-training: educate security analysts on basic ML concepts and how AI applications work, so they know where to look for risks. Conversely, sensitize AI developers to secure coding practices and threat modeling. Hiring or appointing an AI Security Specialist role can help bridge the two domains. In some cases, companies are even creating new roles like Chief AI Officer or expanding the CISO’s remit to cover AI risk specifically[63][64]. The bottom line: ensure you have expertise to evaluate and monitor AI systems. As AI becomes integral to business, treating AI risk as “someone else’s problem” is no longer viable for security teams.
Culture of Reporting and Collaboration: Encourage a culture where employees can report AI-related issues or near-misses without fear. If someone realizes they might have pasted something sensitive into an AI tool, they should feel safe to notify security so the impact can be assessed (rather than hoping it goes unnoticed). Likewise, if an AI system produces a strange output that could indicate a bug or manipulation attempt, staff should flag it. Make AI risk an ongoing conversation in the organization, not a taboo. The more eyes and brains watching out, the better chances of catching issues early.

Finally, it’s worth noting that not all is adversarial – AI itself can be leveraged to enhance security if done carefully. Many organizations are already using AI to detect threats or automate security tasks. An ironic truth is that AI needs observability more than most systems, yet AI can also improve observability of other systems. By addressing shadow AI risks, you create a stronger foundation to safely use AI for positive ends (like analyzing security logs or user behavior for anomalies). Several security leaders reported that adopting GenAI in their SOC has helped productivity and allowed junior analysts to contribute more[65][66]. This highlights that the goal is not to stifle AI innovation, but to enable it with confidence.

Conclusion: See Everything, Fear Nothing

In the rush to embrace AI’s game-changing benefits, enterprises must be careful not to trade away visibility and control. Shadow AI and visibility gaps are the unintended by-products of the fast democratization of AI in the workplace. When left unchecked, they can undermine security, compliance, and even the quality of AI outcomes – you can’t protect what you can’t see, and you can’t trust what you can’t explain. The incidents and data outlined in this paper make clear that the stakes are high: sensitive data is already leaking through invisible channels, and “unknown unknowns” lurk in AI behaviors.

The good news is that organizations are not powerless. By applying the time-tested principles of cybersecurity (know your assets, monitor everything, enforce least privilege, etc.) to the new AI context, we can close these gaps. It requires updates to technology (logs, tools, gateways) and to processes (policies, training, cross-team governance). It also requires a mindset shift: treating AI systems with the same rigor as any mission-critical application. As one expert bluntly put it, “If you don’t know how your AI systems are behaving, they’re already a liability.”[67][68]

By investing in AI observability and shadow AI mitigation now, enterprises can get ahead of the curve. They can enjoy AI’s upsides – efficiency, insights, automation – without stumbling into costly mistakes or breaches. Think of it as turning on the lights in a room that was dimly lit; once you can see clearly, you can move fast without fear of unseen obstacles. The organizations that succeed with AI will be those that understand it’s not “magic” – it’s software and data that need management and oversight like anything else, just at a new scale and complexity. In an era where AI may increasingly act on our behalf, visibility is the prerequisite for trust.

In closing, security and technical leaders should ask themselves: Do we know all the AI tools in use in our company? Do we have a clear line of sight into how data is flowing to and from these AI systems? Can we trace decisions and actions back to their source? If the answer to any is “no,” now is the time to strengthen your visibility layer and governance around AI. By seeing what you couldn’t see before, you’ll be in a far stronger position to harness AI’s power – confidently and safely – for your enterprise.

[1] [30] [31] [32] [33] [46] [47] [52] [61] [62] [65] [66] [67] [68] Your AI’s Blind Spot is Bigger Than You Think | Splunk