Widespread use of unsanctioned generative AI tools has led to negative outcomes.
Most leaders in tech already understand the potential of generative AI, but the quiet threat isn’t innovation, it’s what’s happening without oversight. Nearly 80% of IT executives have seen their organizations take a hit because employees used generative AI tools without approval or clear guardrails. The impacts are not minor. What we’re talking about is inaccurate results being passed as truth and sensitive company data entering external AI models without intent or realization.
These tools are easy to access, and that’s part of the problem. They’re available to everyone in your company, not just your data scientists. When someone drops proprietary information into a free AI chatbot, it’s not just a loss of data, it’s a loss of control. What looks like a productivity boost can turn into a serious liability. Without clear boundaries, mistakes don’t just stay internal, they leak out and replicate.
A structured strategy around AI use is no longer optional. This type of unmanaged behavior, referred to as “shadow AI”—is increasingly common in large enterprises. Employees aren’t trying to create risk, they just don’t have enough clarity on what’s permissible. If your business isn’t actively defining the rules of AI engagement, you’re playing defense without a scoreboard.
The data backs it up. In April 2024, Komprise surveyed 200 IT leaders from U.S. enterprises with over 1,000 employees. They found that 46% had experienced false or inaccurate AI-generated content, and 44% reported data leaks. For 13% of respondents, those errors weren’t just internal, they damaged financial results, eroded customer trust, or hit their brand integrity.
You don’t solve this with policy alone. You need visibility, real-time oversight, and education embedded into every level of the organization. That’s how you protect data assets and keep your company accountable in an AI-driven market. Leaders must move from passive risk recognition to active risk management, or expect losses across both operations and reputation.
Security and privacy concerns dominate the challenges posed by shadow AI.
If there’s one thing executives can’t afford to underestimate, it’s the impact of poor data hygiene when generative AI enters the picture. Unauthorized use of AI tools, what many now refer to as “shadow AI”—isn’t just an operational oversight. It’s a growing security and privacy risk. When sensitive data flows through tools without IT supervision, the organization no longer controls where that data lands or how it’s used downstream.
Most companies are already under pressure to secure customer and enterprise data. Add AI to the mix, and the complexity multiplies. Key information, personally identifiable data, financial records, proprietary models, can end up in generative engines that operate outside your infrastructure. That’s not just a theoretical threat. It’s an active breach of data governance and, depending on your industry, possibly a regulatory one too.
IT leaders are clearly aware of this shift. According to Komprise’s April 2024 survey of 200 enterprise IT leaders, 90% expressed concern about how shadow AI affects security and privacy. That concern isn’t mild. Roughly half, 46%—said they’re “extremely worried.” This tells you something: the technology’s moving fast, but internal controls are lagging behind.
For C-suite leaders, this concern isn’t just technical, it’s strategic. Data security dictates business continuity, customer retention, and brand trust. You can’t grow reliably while the internal use of AI tools remains fragmented and largely invisible. Knowing who is using generative AI, and for what purpose, needs to be a core part of any executive’s oversight responsibilities.
Strong governance doesn’t mean shutting AI down, it means setting guardrails that keep the company secure while still leveraging what the technology offers. That means clear policy, active monitoring, and integrating AI awareness into employee training. The message needs to be simple: AI is welcome, but only when security doesn’t take a back seat.
Enterprises are actively investing in technology to counteract the risks associated with shadow AI.
Organizations are taking the risks of unmonitored AI seriously, and many are acting fast. The majority of enterprise IT leaders are responding with targeted investments in infrastructure that can detect, monitor, and control how AI is being used within the company. This isn’t speculation, it’s a shift in operational priority.
Executives are directing resources toward platforms that manage data workflows, discover unauthorized AI use, and audit how tools are being deployed internally. These aren’t experimental acquisitions; they’re core components of a strategy to bring visibility and control to something that’s currently opaque. The goal is simple: reduce exposure, ensure compliance, and protect sensitive data without slowing down progress.
Komprise’s 2024 study confirms this trend. Among the 200 IT leaders surveyed, 75% said they plan to implement data management platforms. Another 74% are investing in AI discovery and monitoring tools. These investments are designed to give IT teams clarity around where AI is deployed, what it’s doing, and what data it touches. That level of visibility is critical if a company wants to scale AI-powered workflows without inheriting chaotic risk.
It doesn’t stop at tech adoption. Roughly 55% of IT teams are also pairing these tools with access management systems and data loss prevention software. Layering that with employee training gives organizations a more complete approach, technology backed by human awareness. For C-suite leaders, this is where strategy becomes execution. You can’t delegate AI risk management entirely to IT. It needs executive buy-in to align budgets, policy shifts, and cultural adoption.
Shadow AI isn’t a technical nuance, it’s a governance issue. The companies that move fastest to contain and control it will give themselves a clear advantage, not just in avoiding risk but in building an intelligent, accountable AI framework that supports long-term growth.
Proper preparation and management of unstructured data is critical for safe AI integration.
The real value from generative AI doesn’t come from the tool itself, it comes from what you feed into it. Unstructured data, which makes up the majority of enterprise information, needs structure before it touches AI systems. That means IT teams must prepare, classify, and govern data in a way that protects proprietary, financial, and personally identifiable information from uncontrolled exposure.
Data classification is where most organizations are focusing. It’s one thing to restrict access to confidential data, but when employees interact with AI tools, that line becomes harder to maintain without automation. Enterprises solving that challenge are automating workflows to scan, tag, and control how specific data sets are used with generative models. This gives them a real-time understanding of what data is approved for AI processing and what isn’t.
Based on Komprise’s April 2024 survey, 73% of IT teams are already classifying sensitive data and using workflow automation to enforce access rules. Many are deploying tools capable of scanning large volumes of content, tagging it with metadata, and restricting high-risk information. Technologies like vector databases are being used to enable semantic search or retrieval-augmented generation (RAG), allowing models to retrieve information without ingesting the data permanently. This creates more flexibility while maintaining a tighter grip on data exposure.
The technical effort matters, but the strategic thinking is what’s driving the shift. AI adoption is no longer limited to a few departments, it’s spreading across teams. If the integrity of internal data isn’t reinforced before AI use expands, the organization will face growing risks tied to compliance, quality, and trust.
For senior leaders, this is a moment to align data governance with AI policy. Safe AI isn’t about limiting usage, it’s about making sure what the AI sees is clean, categorized, and compliant. The companies that invest in foundational data readiness now will be in a stronger position to scale safely and confidently over time.
Main highlights
- Shadow AI is triggering real business risk: 80% of IT leaders report negative outcomes from unauthorized AI use, including financial loss, data leaks, and brand damage. Leaders should implement clear AI usage policies and enforce oversight to prevent hidden exposure.
- Security and privacy are top concerns: 90% of IT leaders worry about shadow AI compromising sensitive data, with nearly half extremely concerned. Executives should treat AI governance as a board-level priority to protect customer trust and ensure regulatory compliance.
- Enterprises are investing in visibility and control: 75% of organizations are deploying data management and AI monitoring tools to bring shadow AI activity into view. Leaders should fund technologies that offer real-time insight and equip teams to audit AI use internally.
- Unstructured data must be managed at scale: 73% of IT teams are classifying sensitive data and using automation to safely prepare it for AI. To scale generative AI securely, decision-makers must prioritize strong data preparation workflows and automated governance solutions.
