The race to harness AI, including agentic AI, is accelerating in modern enterprises. Yet, beneath the surface of sanctioned deployments and official rollouts, a quieter revolution is unfolding: Employees are adopting AI tools outside formal governance — creating a new frontier of risk and opportunity. This phenomenon, known as shadow AI, mirrors the rise of shadow IT, but with amplified stakes and a more complex landscape. As organizations strive to balance innovation with security, understanding and addressing Shadow AI is crucial.
What Is Shadow AI?
Shadow AI echoes the early days of Shadow IT. Employees bypass official channels, integrating generative AI (genAI) platforms like ChatGPT, Claude, Gemini, and other niche solutions into their daily tasks — often without IT’s knowledge. These tools are embedded in everyday processes, from drafting emails to analyzing data, sometimes through unofficial integrations with productivity apps like Google Docs, Slack, or customer relationship management (CRM) systems.
It’s not an act of rebellion but more about productivity outpacing policy. Employees adopt AI tools outside formal governance, using free or personal subscriptions, browser extensions, and plug-ins to integrate AI into their workflows.
The scale of the issue is significant: only 23% of employees stick to approved AI tools, while the rest use alternatives. This creates a governance blind spot, where sensitive corporate data can flow through tools IT never approved, exposing confidential strategies, customer information, or intellectual property to external systems.
Shadow AI isn’t a fringe behavior; it’s mainstream, and it demands a rethink of enterprise governance before history repeats itself.
The Scale of the Shadow
Shadow AI adoption is everywhere — across roles, industries, and faster than formal enablement. Studies show workplace AI use is surging, but employees aren’t sure what’s sanctioned. One report revealed a 68% spike in Shadow AI use, with 57% of employees admitting to entering sensitive data into these tools.
Leaders know AI is in play, but they can’t map where it’s being used — or how data flows through it. And the consequences are mounting. AvePoint’s The State of AI Report highlights 75% of organizations experienced at least one AI-related security breach in the past year, and 86% delayed AI deployments by up to a year due to security and data quality concerns.
The drivers behind Shadow AI are familiar: speed, convenience, and frustration with official tools. The blind spots are just as recognizable, such as the lack of visibility and reactive policies. The difference is that risks are amplified: compliance gaps, intellectual property leakage, and data exposure loom large. Despite 90% of organizations claiming to have an information management framework, only 30% effectively classify and protect their data. Governance exists on paper but fails in practice.
Governance Lessons from Shadow IT
Shadow IT taught us that ignoring unsanctioned behavior only heightens vulnerabilities. With Shadow AI, the stakes are higher: data leakage, regulatory exposure, and IP loss embedded in AI models. Gartner projects that 40% of enterprises could face AI-related security or compliance incidents by 2030.
Approving a few tools isn’t enough, and sanctioned tools don’t stop Shadow AI. Employees pick what fits their workflow. Governance focused on tools misses the bigger picture: how data moves and is processed.
Approximately 99.5% of organizations have invested heavily in AI literacy; however, inaccurate outputs and hallucinations still erode trust. Training alone doesn’t solve governance gaps. The lesson here is to treat AI governance as data governance, not just tech approval. Prioritize visibility, clear boundaries, and ongoing education to ensure effective communication. Continuous auditing and monitoring are also non-negotiable.
In short, the experience with Shadow IT reveals three essentials of governance for tackling Shadow AI:
1. Proactive governance prevents risks before they escalate.
Being proactive means setting clear rules upfront, enforcing classification and access in advance, and automating policies so compliance happens in real time.
2. Data-centric oversight is more effective than tool approval alone.
Rather than only focusing on which tools are allowed, prioritize protecting the data — monitoring flows, applying sensitivity-based controls, and auditing continuously.
3. Simplicity and clarity in policies drive better compliance.
Governance should feel intuitive: plain-language policies, streamlined approvals, and clear “why” behind the rules make doing the right thing effortless.
Turning Governance Blind Spots into Strategic Advantage
Shadow AI is a wake-up call – and a signal – for every enterprise. The risks are real, but so are the opportunities.
Our experience shows that resilient enterprises prioritize governance strategies that protect data and empower people to innovate responsibly. By moving beyond reactive policies and tool lists, organizations can turn the challenge of shadow AI into an opportunity to strengthen governance, enhance visibility, and drive responsible innovation.
The future belongs to enterprises that strike a balance between agility and accountability, fostering a culture where responsible AI adoption is the norm. Shadow AI may be the sequel to Shadow IT, but with the right approach, it can be the catalyst for smarter, safer, and more resilient growth.
