Shadow AI: The Risk Governance Gap You Didn't Know You Had

Here's a question worth asking at your next risk committee meeting: how many AI tools are running inside your organization right now?

If you answered with the number your IT team approved, I'd bet the real number is significantly higher.

That gap, the one between sanctioned AI and the AI your employees are really using, is shadow AI. And for GRC professionals, it represents one of the fastest-growing, least-governed risk surfaces in the enterprise today.

Shadow AI isn't new. It's the next evolution of shadow IT, which we as risk professionals, especially IT risk and cyber risk professionals have been fighting for decades. With shadow AI, though, the stakes are fundamentally different.

When an employee used an unapproved SaaS app or a rogue software program, the worst-case scenario was usually a data exposure or a compliance gap. When an employee uses an unapproved AI tool, they may be uploading sensitive financial data, generating unvalidated outputs that drive strategic decisions, or deploying autonomous agents that interact with customers, all outside any governance framework, any controls regime, and any audit trail.

Think about what that means in practice.

• A marketing analyst uploads confidential performance reports to a public AI tool to generate trend insights.
• A developer uses a generative AI assistant to write automation scripts, introducing security vulnerabilities that no one owns.
• A customer support manager deploys an AI chatbot without engaging IT or compliance, creating gaps in data privacy and response quality controls.
• A product team makes strategic decisions based on AI-generated analytics that have never been validated for accuracy, bias, or regulatory alignment.

Each of these is a scenario playing out in enterprises right now. And each one is, at its core, a culture and governance failure, not a failure of AI.

The Numbers Tell the Story

Almost 80 percent of professionals report using unapproved AI tools to do their jobs. Gartner estimates that by 2028, more than 75% of enterprises will rely on AI-amplified cybersecurity products, yet most organizations still lack the governance infrastructure to manage the AI tools already in use across their workforce.

Fast-changing regulations compound the situation. GDPR, DORA, NIS2, and the EU AI Act all establish stringent requirements around data storage, access, and management. The EU AI Act especially introduces requirements around transparency, bias mitigation, and human oversight for certain categories of AI use -- requirements that are essentially impossible to satisfy if you don't even know which AI tools are operating in your environment. Non-compliance isn't theoretical risk. It carries real financial and reputational consequences.

And when breaches occur -- as they did with the DeepSeek platform compromise, which exposed over a million records including chat logs and API keys -- the damage extends well beyond the vendor. Any enterprise whose employees were using that platform without governance guardrails becomes collateral.

This Is a GRC Problem – And a GRC Opportunity

Here's the key takeaway: shadow AI is not fundamentally an IT problem or a cybersecurity problem. It's a governance problem. And that means we as GRC professionals are the right people to solve it.

The core issue is a gap between risk appetite and reality. Most organizations have articulated policies around data handling, third-party risk, and AI governance. But without continuous monitoring of where and how AI is being used, those policies are effectively unenforceable. Controls exist on paper. Visibility doesn't exist in practice.

Closing that gap requires GRC teams to stop treating AI risk as a one-time assessment item and start treating it as a continuous monitoring priority, the same way leading organizations now approach controls testing and compliance validation. Point-in-time reviews of AI usage aren’t enough in an environment where new tools are being adopted daily.

The practical path forward:

• Establish AI governance policies that clearly define approved tools, set parameters around data sharing, and assign accountability for AI-assisted decisions. This sounds basic, but in most enterprises it still doesn't exist in any enforceable form.
• Build a cross-functional AI oversight team. AI adoption cuts across finance, legal, operations, marketing, and engineering. A governance process that lives only inside IT or cybersecurity will miss most of the risk surface. GRC professionals are uniquely positioned to broker that cross-functional alignment.
• Deploy AI observability. You need to be able to scan networks for large language model traffic to understand where AI is being used. That inventory is the foundation of everything else.
• Integrate AI risk into your existing cyber and operational risk frameworks. AI risk isn't a separate discipline. Just as we are deploying AI for GRC, so must we focus on GRC for AI. AI belongs inside the same assessments, the same control structures, and the same escalation pathways as every other operational risk.
• Automate policy enforcement and compliance monitoring through a unified Cyber GRC platform. Real-time visibility into AI usage, mapped against your governance policies, is the difference between proactive risk management and reactive incident response.
• And remember awareness and training, including your third-party partners. Your supply chain's shadow AI is your risk too.

The Anticipatory Imperative

The organizations that manage shadow AI risk effectively won't be the ones that react to the next major platform incident. They'll be the ones that built visibility, controls, and governance infrastructure before the incident happened, anticipating the risk rather than scrambling to contain it.

Shadow AI exists because employees are trying to do their jobs better. That instinct is worth supporting. But productivity without governance isn't innovation. It’s unmanaged risk.

As GRC professionals, we have the frameworks, the mandate, and increasingly the tools to change that equation. The question is whether we act before the next audit finding, or the next breach, forces our hand.

Let’s take AI risks out of the shadows.