Shadow AI Security: Why It Is Becoming Every Organisation’s Blind Spot

Article attributed to Blair Hasforth, Country Manager ANZ at OneTrust

Artificial intelligence is quickly becoming part of everyday work. Employees now use AI to write emails, analyse information, create presentations, generate code and automate routine tasks. However, adoption is often moving faster than organisations can establish clear rules for its use.

That gap is creating a growing shadow AI security challenge. Shadow AI refers to employees using AI tools without appropriate organisational visibility, approval or governance.

The challenge extends far beyond public chatbots. AI now appears inside business software, browser extensions, coding assistants and increasingly autonomous systems that interact with other applications. As a result, organisations may struggle to identify where AI operates and what information it can access.

Unlike traditional shadow IT, shadow AI can actively interact with an organisation’s data and workflows. It can process sensitive information, generate outputs that influence decisions and, increasingly, take actions across connected systems.

Shadow AI Security Is Moving Faster Than Governance

Employees adopt AI because it solves real problems. If an AI tool can save someone an hour on a task, that employee is unlikely to wait months for formal approval. That is especially true when the technology is readily available.

Consequently, a widening gap has emerged between AI adoption and governance. Employees may use several AI tools across their roles. Meanwhile, IT, security and risk teams may have limited visibility into those tools.

They may not know what information employees share with AI systems or how they use the resulting outputs. That lack of visibility creates a significant shadow AI security problem.

The challenge becomes more complex as vendors add AI to software that employees already use. For example, an organisation may approve an application without realising that the vendor has introduced new AI capabilities. Those capabilities may access or process business information in new ways.

Therefore, organisations need to understand more than which AI tools employees use. They also need to identify where AI operates within technology they have already approved.

Shadow AI Security Is About What AI Can Do

The risk from shadow AI goes beyond sensitive information entering an unapproved tool. AI can become embedded across approved systems without organisations fully understanding how employees use it or what data it can access.

Moreover, AI can transform information, generate recommendations and influence decisions at a speed and scale that traditional technology could not.

For example, an employee might use AI to analyse customer information or summarise an internal document. However, an inaccurate output could then appear in a report, reach a customer or influence a business decision.

That error could damage the organisation’s reputation. As several high-profile cases have shown, even minor AI mistakes can quickly become public issues when they reach published or customer-facing content.

The risks may increase further as AI agents become more autonomous. These systems may access applications, retrieve information and take actions with limited human involvement.

As a result, AI can amplify existing weaknesses in data governance, access controls and employee training. A small mistake or policy gap can travel much further when AI is involved. Therefore, visibility and oversight become increasingly important.

Shadow AI Security Can Expose Hidden Productivity Costs

Productivity drives much of today’s AI adoption. However, unmanaged AI can introduce hidden costs that organisations may overlook.

For instance, employees may spend significant time checking, correcting and rewriting AI-generated content. When that happens, the expected productivity gains can quickly disappear.

Repeated prompts, additional AI usage and greater compute requirements can also increase costs. More importantly, inaccurate or inappropriate outputs may create much larger financial, compliance and reputational consequences.

However, organisations should not respond by restricting AI adoption altogether. Instead, they should make responsible AI use easier than unmanaged AI use.

Employees need access to trusted tools. They also need clear guidance about what information they can share, which AI use cases the organisation permits and when human oversight is required.

This approach gives organisations a clearer path forward. It also reduces the need to continually revisit or rebuild their AI governance strategy.

Shadow AI Security Starts With Visibility

The first step in addressing shadow AI is understanding what actually happens across the organisation.

Businesses need visibility into which AI tools employees use, who uses them and what data they share. Organisations also need to understand what those AI systems can do.

Without this information, businesses are effectively trying to govern an environment they cannot see.

However, a one-off audit or list of approved software will not provide enough visibility. AI use changes constantly as employees adopt new tools and vendors add AI capabilities to existing products.

Therefore, organisations need continuous monitoring and controls that can identify emerging shadow AI security risks as they develop.

Clear policies also require practical enforcement. Employees need to understand the boundaries around AI use. At the same time, security, privacy, risk and compliance teams need the ability to respond when employees cross those boundaries.

Blocking AI Will Not Solve Shadow AI Security

The instinctive response to shadow AI may be to restrict access to unapproved tools. However, blocking AI without providing secure alternatives can push usage further underground.

Employees already see value in AI. If approved tools are difficult to access or cumbersome to use, employees may search for alternatives that help them complete their work faster.

In that environment, innovation can quickly outpace the guardrails designed to manage it.

A more effective approach gives employees the confidence to use AI within clear boundaries. Organisations should provide trusted tools, practical training and straightforward guidance.

Meanwhile, security, privacy, risk and compliance teams need enough visibility to identify and manage emerging risks.

Good governance should support responsible AI adoption rather than create unnecessary friction.

Shadow AI Security Requires Continuous Governance

Shadow AI is not a problem organisations can solve with a single policy or software audit.

New AI capabilities, applications and autonomous agents will continue to emerge. Consequently, the technology landscape will keep changing.

Organisations need governance that can evolve alongside AI. That includes continuous monitoring, proactive risk assessments, vendor oversight and controls that adapt as new use cases emerge.

The organisations that succeed with AI will not necessarily be those that use it the least. Instead, success will depend on how well organisations understand and manage its use.

Rather than trying to eliminate shadow AI through blanket restrictions, organisations should give employees trusted tools and clear guidance. They also need visibility into how AI operates across the business.

That approach can help organisations capture AI’s productivity benefits while keeping security, compliance and risk firmly in check.