TL;DR

Shadow AI—employees’ unsanctioned use of AI tools without IT approval or monitoring—poses escalating enterprise risks through data leakage, regulatory violations, and blind trust in AI outputs. Unlike Shadow IT which moved data, Shadow AI transforms, exposes, and learns from data, introducing higher-stakes vulnerabilities. LevelBlue research warns organisations lack visibility into how, where, and why AI is being used across their operations.

From Shadow IT to Shadow AI

AI is reshaping how employees work, create, and make decisions across industries. From automating tasks to generating code and analysing data, workers turn to AI tools to work faster and smarter. However, most organisations have no visibility into how, where, or why these tools are being used.

This unsanctioned AI usage mirrors the early days of Shadow IT, when employees turned to unapproved SaaS tools and messaging apps to accelerate productivity. But the stakes are far higher with Shadow AI. Unlike Shadow IT, which simply moved data around, Shadow AI transforms, exposes, and learns from data—introducing unseen vulnerabilities and higher risks.

Blind trust in AI outputs, poor cybersecurity training, and lack of clear governance allow sensitive data, intellectual property, and decision-making processes to slip beyond organisational control. What began as an efficiency tool is fueling unseen threats.

Drivers of Shadow AI Adoption

The rapid rise of Shadow AI isn’t the result of malicious intent, but rather lack of awareness and education. Employees who use AI in personal lives often bring those same tools into the workplace, assuming they’re safe for company-approved systems. For many, the line between personal and professional use has blurred.

With rapid advancement of AI tools, some organisations have yet to establish clear policies or training guidelines on what constitutes appropriate workplace AI use. Without explicit guidance, employees experiment on their own. The convenience and popularity of AI tools often outweigh perceived risk.

Critical Risks Introduced

Unmanaged AI adoption introduces a range of risks. The most immediate concern is data leakage, highlighted by this year’s DeepSeek breach. When employees feed confidential information into public AI tools, that data may be logged, retained, or even used to train future models. This can lead to violations of data protection laws such as GDPR or HIPAA, and in some cases, even data espionage.

Storage of sensitive information on servers in unaffiliated countries raises concerns about potential data theft or geopolitical surveillance. Several government agencies across the US and Europe have banned DeepSeek use within their organisations for this reason.

Another major risk is legal and regulatory liabilities. When employees rely on AI-generated outputs without validating accuracy or legality, they open doors to serious consequences—from copyright violations and privacy breaches to full-scale compliance failures.

Unauthorised sharing of personal or sensitive information with external models can trigger breach notifications, regulatory investigations, and contractual violations, exposing organisations to costly fines and reputational damage.

Emerging Amplification: Vibe Coding and Agentic AI

These risks are being amplified by emerging trends such as vibe coding and agentic AI. In some cases, code is deployed directly into production without review. These flaws can remain hidden until exploited.

Agentic AI poses an even broader concern. Internal AI agents, built to automate workflows or assist employees, are often granted overly permissive access to organisational data. Without strict controls, they can become backdoors to sensitive systems, exposing confidential records or triggering unintended actions.

Together, these practices expand attack surfaces in ways that evolve faster than most organisations can detect or contain.

Equally concerning is blind trust in AI outputs. As users grow more comfortable with these systems, their scrutiny level drops. Inaccurate or biased results can spread unchecked through workflows, and when used outside sanctioned environments, IT teams lose visibility needed to identify errors or investigate incidents.

Visibility as Foundation for Control

Addressing Shadow AI starts with visibility. Organisations can’t protect what they can’t see, and currently, many have little to no insight into how AI tools are being utilised across their networks.

The first step is assessing where AI is being used and establishing clear, updated policies defining what’s approved, what’s restricted, and under what conditions AI can be used. Enterprise-wide training is equally critical to help employees understand both benefits and risks of using large language models.

Companies should also provide the right resources. When employees have access to sanctioned AI tools meeting their business needs, they’re far less likely to seek unauthorised ones. Developers, for example, may require specialised models hosted in secure environments to safely leverage AI without exposing proprietary code or customer data.

Technical Controls and Architecture Integration

Organisations must integrate AI into their security architecture. Privileged access management should be implemented for certain LLM access, ensuring only authorised users can interact with sensitive systems or datasets. Security teams should deploy technical controls such as CASB, DLP, or proxy filtering to detect and block Shadow AI usage.

Looking Forward

Shadow AI will not wait for policy. It’s already shaping workflows, decisions, and data flows across organisations. The choice is not whether to allow AI, but whether to manage it.

Organisations that bring visibility, control, and accountability to their AI usage can enable innovation safely. But ignoring Shadow AI won’t make it go away. It’s far better to confront it head-on, understand how it’s being used, and manage risks before they manage you.

The parallel to Shadow IT is instructive: organisations that addressed unsanctioned SaaS usage early through governance and approved alternatives fared better than those that simply blocked access. The same principle applies to Shadow AI—but with even greater urgency given the higher stakes of data transformation, exposure, and learning that AI introduces.

Source: TechRadar