c-suite

The AI you don’t see: Why Shadow AI is the urgent frontier of enterprise cyber risk

insight
Nov 11, 2025
9 min read

Author

Eugen

 

Eugen Rosenfeld is a CTO & a Solution Architect in Life Sciences at Nagarro. He has more than 20 years experience in different programming languages, technologies and business domains.

When AI stops asking for permission

AI has crossed a threshold. It’s already inside the system, making choices, shaping decisions, and changing how work gets done. What started as pilots and proofs of concept has quietly become part of the daily machinery of business.

Yet most boardrooms are still looking at AI through the lens of programs and policies, the sanctioned tools, the approved models, the neat dashboards of compliance. What they don’t see is the shadow network growing beneath it.
Shadow AI: the quiet use of unapproved tools by teams who want to move faster than governance can catch up.

This isn’t a handful of rule-breakers. It’s a symptom of something larger. The widening gap between how quickly AI evolves and how slowly organizations adapt. By 2027, Gartner estimates that nearly three out of four companies will experience security incidents linked to shadow AI.

The real problem isn’t that people are using AI. It’s that leadership has lost sight of where it’s being used, and what decisions it’s already shaping.

The inevitable drift

Shadow AI is not born from defiance. It is a natural response to the modern dynamics. 

 

Accessibility is unprecedented.
AI capabilities are now there in everyday tools: browsers, design suites, code editors, CRM systems, and even email clients. Employees don’t need IT approval to use advanced AI; they already have it at their fingertips.

 

Velocity pressure compounds the risk.

 Teams under tight deadlines and constant competition will always choose speed over caution. When governance slows progress, workarounds aren’t defiance; they’re survival.

 

Fragmentation makes governance impossible.

AI is now built into nearly half the enterprise platforms that organizations rely on, from marketing automation to ERP. Most of it operates beyond the line of sight of IT or security. KPMG’s 2025 research shows unsanctioned AI use is already outpacing enterprise controls.

People will always reach for whatever helps them work smarter or faster. The question isn’t if this happens. It’s whether your organization can see it, measure it, and govern it before it scales unchecked.

Even OpenAI acknowledged that shadow usage was the main driver behind its enterprise adoption. Employees were already using these tools long before formal rollouts began; organizations merely caught up to their own workforce. Curiosity moves faster than policy, which means visibility, measurement, and control can no longer be afterthoughts.

The new risk equation

Shadow AI fundamentally shifts enterprise risk away from infrastructure and into information flows.

The greatest risks today don’t begin with hackers; they start with good intentions.

  • Uploading proprietary customer data into public AI models for analysis
  • Deploying unvetted AI plug-ins that connect directly to sensitive data
  • Shipping AI-generated code into production without security validation
  • Automating critical business logic through unsecured third-party APIs

 

Each of these acts seems harmless in isolation, but together they create a web of exposure that grows faster than governance can contain. And when regulators ask, “Who approved this model?” or “What data trained this decision engine?” too many organizations find themselves without documentation, an audit trail, or answers.

Image representing Shadow AI

The financial impact is no longer hypothetical.

The IBM / Ponemon 2025 Cost of a Data Breach Report finds that the global average cost of a data breach is US$4.44 million. Breaches involving AI-related systems show alarming patterns: about 13% of organizations reported such breaches, and among them 97% lacked proper AI access controls. In environments where shadow AI usage is high, the added cost can be approximately US $670,000 more than better-governed peers.

The Reco 2025 State of Shadow AI Report adds further weight to the issue: it reports that companies with elevated shadow AI usage faced breach costs roughly US $670,000 higher and that 71% of office workers admit to using AI tools without IT approval. It also finds that one platform accounted for 53% of shadow AI usage in the studied sample.

All in all, the risk isn’t just theoretical. The gap between where AI is being used and where it is being governed shows up in measurable financial exposure.

The illusion of control

Here’s the uncomfortable truth: you can’t policy your way out of shadow AI. Blocking tools don’t stop the behavior; they only drive it deeper, making it harder to detect and even harder to manage.

The only sustainable answer is total visibility. Not control through restriction, but control through awareness. To achieve it, organizations must:

AI and illusion of control
1. Acknowledge the full scope of internal AI tool usage across all departments 
2. Assess usage patterns and conduct rigorous Off-The-Shelf Software (OTSS) analysis for every AI tool in operation 
3. Select approved tools that meet security, compliance, and operational standards 
4. Introduce clear policies governing the use of approved tools, with defined boundaries and accountability 
5. Design targeted training programs ensuring every employee understands both the capabilities and risks 
6. Build continuous observability systems, not just periodic audits 
Modern governance is not limited to gatekeeping anymore. It needs to evolve into real-time awareness, the ability to see how intelligence flows through the business at any moment. According to Netskope’s Cloud and Threat Report on Shadow AI and Agentic AI (2025), shadow AI now extends across SaaS platforms, on-premise models, and custom-built agents, creating a visibility challenge unlike anything enterprises have faced before.

Turning blind spots into intelligence

Winning with AI is no longer about how boldly you experiment; it’s about how clearly you see. The strongest organizations are those that can open the black box and explain what’s happening inside.

Visibility has become a strategic control layer; it defines your organization’s ability to:

 

blind spots in AI
1. Prove data integrity, appropriate use to auditors and regulators
2. Protect intellectual property and maintain confidentiality of sensitive information
3. Maintain trust, explainability in AI-driven business decisions
4. Scale AI adoption safely without accumulating invisible systemic risk

Without visibility, every AI productivity gain carries a hidden liability: trust debt.

Unlike technical debt, trust debt compounds silently, with every undocumented decision, every unexplained model output, every untracked data flow. Over time, it accumulates into a balance sheet of risk.

The way forward: control without compromise

A thoughtful response to shadow AI is not about blocking new tools; it is about helping people use them wisely. Governance should guide, not restrain. This means knowing where AI is used, what data it draws from, and who is accountable for its outcomes. When teams can see and explain their systems, risk becomes manageable and trust becomes real. The aim is simple: make responsible AI use an everyday habit, not an exception that needs policing.

Three practical strategic shifts enable this transformation:

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From restriction to detection

Stop attempting to block AI tools entirely. Start detecting where, how, and by whom they are being used. Invest in discovery capabilities before enforcement mechanisms.

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From ownership to accountability

Assign explicit AI tool owners who remain accountable for every workflow, every data input, every decision output. Accountability cannot be diffused; it must be named and empowered.

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From compliance to confidence

Reframe governance not as a barrier to innovation but as an enabler of faster, safer AI adoption. Organizations with strong AI governance can move more quickly because they have eliminated the uncertainty, along with the risk that slows everyone else down.

This model transforms governance into a competitive differentiator, aligning with ISO 27001 and SOC 2 frameworks to demonstrate enterprise-grade AI trust.

Seeing the invisible: the CIO’s real AI challenge

For CIOs, the AI challenge isn’t adoption, it’s visibility. Shadow AI has shifted risk from network security to information-flow governance. You can’t secure what you can’t see, and you can’t govern what you don’t understand.

Nearly nine in ten IT leaders now cite shadow AI as a major concern, with some already reporting financial or reputational damage from unapproved tools. Employees are integrating generative and agentic AI into workflows faster than policy can keep pace.

The real question: Do you know where AI is operating in your business right now? If not, your next breach may not come from an attacker but from a well-meaning employee using an invisible tool. Visibility isn’t just about security; it’s about control, trust, and competitiveness. In the age of AI, it’s the difference between leading with clarity and leading in the dark.

AI and cyber risk
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