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Shadow AI: How to Protect Your Business From Its Latest Security Threat

Published Mon May 20 2024

Shadow AI: How to Protect Your Business From Its Latest Security Threat
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In a world that’s increasingly obsessed with efficiency and productivity, artificial intelligence (AI) is quickly becoming a favorite tool across industries. Its ability to speed up everyday tasks—writing emails, analyzing data, optimizing workflows, and more—has catapulted us into a new era of innovation.

Its popularity has grown so fast that 55 percent of Americans say they interact with AI at least once per day, with 27 percent saying they interact with AI almost constantly, according to Pew Research. But what happens when AI use happens at work without an employer’s knowledge?

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In some cases, nothing. In other cases, it could allow employees to speed up their workflow and be more productive. However, even employees with the best intentions for AI could be putting their businesses at risk. The reason? Shadow AI.

What Is Shadow AI?

Shadow AI refers to the use of artificial intelligence without official approval or oversight from management or IT departments. Essentially, it involves employees independently implementing AI to enhance their productivity or streamline processes without following proper protocols or receiving necessary permissions.

What Are the Threats Associated With Shadow AI?

Data security risks: Shadow AI can create security problems. Because employees are not sticking to the organization's security standards, sensitive data might get exposed or hacked. This could lead to data breaches and even legal trouble for not following regulations.

For example, let’s say an employee installs an unauthorized AI tool to boost productivity, ignoring security standards. The tool’s lax security allows hackers to breach sensitive data, leading to legal trouble and reputation damage for the company.

Quality control concerns: Without proper oversight, the quality of AI algorithms and models may be questionable. This can result in inaccurate insights or decisions, influencing the overall effectiveness of business operations.

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For example, a finance team begins using an AI algorithm to predict market trends for investment decisions without proper oversight. The algorithm is based on outdated data and flawed assumptions, leading to inaccurate predictions. As a result, the company makes substantial investments based on flawed insights, resulting in financial losses.

Operational inconsistencies: Shadow AI initiatives may introduce inconsistencies in processes and workflows across different departments or teams. This lack of coordination can hinder collaboration and cause confusion among employees.

Imagine a sales team starts using a new AI-powered customer relationship management tool without informing other departments. They customize it to fit their workflow, but it doesn't align with how the customer support team operates. This leads to confusion and inefficiencies when transferring customer data between teams.

Dependency on unsanctioned tools: Relying on unapproved AI tools may lead to dependency on unsupported or outdated software, increasing the risk of system failures, compatibility issues, and difficulties in maintenance and troubleshooting.

For example, imagine a marketing team adopts a free AI analytics tool without official approval, hoping to streamline their data analysis. They become dependent on it for their daily tasks, but when the tool isn't updated to work with the company’s latest system update, it crashes, leaving the team stranded without crucial analytics.

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Legal and compliance risks: Unauthorized AI usage may violate industry regulations or legal requirements, exposing the organization to potential lawsuits, fines, or damage to its reputation.

Imagine an employee uses an unauthorized AI tool to expedite data analysis, unaware that it violates industry regulations. The system’s improper handling of sensitive information leads to a breach, triggering legal action against the company for non-compliance. As news of the breach spreads, the company’s reputation suffers, resulting in financial losses and diminished trust from customers and stakeholders.

What You Can Do About It

Educate employees. Provide comprehensive training and education programs to raise awareness about the risks associated with shadow AI and promote responsible AI use among employees.

An easy and effective way to implement a training program is by using a learning management system (LMS). A LMS enables you to easily develop training courses on your AI policies, and you can quickly update and disseminate information as policies change. Plus, if your LMS tracks employee skills development progress, management can see how well employees understand the measures and provide proof of employee compliance.

Establish clear policies. Develop and communicate clear policies and guidelines regarding the use of AI within your organization. Most of your organization should agree with the policies and understand why they are necessary. To effectively enforce AI policies, you need buy-in from your employees.

By sharing the potential consequences of not abiding by the policies, such as security breaches, dependency on unsanctioned tools, and operational inconsistencies, employees will be more likely to get behind policies.

Encourage collaboration and transparency. Foster a culture of collaboration between IT departments, management, and employees to facilitate the official adoption of AI solutions that meet the organization's needs while ensuring compliance with security and regulatory requirements.

The goal of implementing policies around AI isn’t to stop its usage completely, it’s to ensure AI is being used in a way that doesn’t jeopardize your company. By encouraging open communication and allowing employees to use AI tools, they will be less likely to use them in a way that could harm the company.

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