The rapid development of artificial intelligence (AI) has exceeded the speed at which most organizations can adequately address their use of AI. Employees in many organizations are implementing and using AI tools without approval from IT or waiting for a security review or leadership authorization. As such, Shadow AI security risks have emerged, resulting in increasing concerns for organizations regarding associated AI security risks that will become major concerns for organizations in 2025 and beyond.
"Shadow AI" is defined as any AI-based tool, chatbot, code assistant, or other automated process used by a worker outside of an organization’s established technology governance framework. Workers have turned to these tools because of their expediency and usefulness. However, when workers use these tools, there are significant security consequences when sensitive information from the organization is sent into an unvalidated AI system.
To protect against these security risk issues, organizations need to understand what those risks entail before taking appropriate action. Forming a partnership with an experienced AI development solutionprovider will assist organizations in developing a governance framework that addresses these risks to prevent them from becoming liabilities.
What Is Shadow AI and Why Is It Spreading?
Similar to shadow IT, which refers to employees using personal devices or cloud-based, non-approved tools outside their organization's approved stack, the emergence of shadow AI represents an expanded opportunity for potential harm. This is because AI tools will often process, store, and at times, learn from the data they receive.
For example, an employee using a public large language model (LLM) to draft proposals, summarize contracts, or write code may be unknowingly providing confidential business data to third-party servers. Furthermore, the third-party servers may keep the data for model training purposes, or they may allow for the storage of that information under data privacy laws in other jurisdictions.
The spread of shadow AI is primarily due to the gap between employee demand for productive tools versus the organizational ability to provide those tools through IT and security policies. The gap is where shadow AI creates security risks.
Core Shadow AI Security Risks Every Business Faces
The first step toward mitigation is to know the nature of your threats. Here are the most common shadow AI security risks faced by an organization:
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Leakage and exposure of sensitive data: Employees can use public-facing AI tools to cut and paste client databases, financial forecasts, legal documents, and proprietary code. After submission, organizations cannot control how their data is stored, used, and disseminated.
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Noncompliance with laws and regulations: Organizations that are subject to the rules established by HIPAA, GDPR, SOC 2, or other frameworks must have established procedures and safeguards for handling data. Unauthorized AI tools typically do not have the certifications or contractual assurances required by governing bodies.
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Corruption of machine learning models: When users begin to use AI tools that are not validated, they can be exposed to inaccurate, biased, or manipulated results. These inaccuracies, biases, and manipulations will corrupt an organization's business processes and decision-making over time and will continue to do so.
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Risks associated with third-party vendors: Most shadow AI tools rely on a third-party API or a model that does not follow the same data practices as the company's. Organizations take on additional risk from a vendor that they have never evaluated or had a contract with.
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Lack of audit and accountability: Authorized enterprise AI deployments come with the ability to log activity, have audit trails, and control access. Shadow tools are used in the dark, and it will be difficult for organizations to investigate incidents and prepare reports to comply with regulations.
How Shadow AI Enters the Organization
Shadow AI does not normally show up through traditional means. Instead, it often finds its way into the organization through web browsing extensions, free-tier SaaS, and plug-ins that connect to AI back-end systems without the user being aware of the risks associated with these activities. A developer who implements an AI code assistant, a marketer who uses a free content rewriting tool or a finance analyst who uses a chatbot service to summarize a report can all create Shadow AI risk when engaged in this behavior.
When an organization does not have a defined policy for using AIs, the risk is compounded. Employees will ultimately use what is fastest unless they have another method of determining how to proceed. Most of these employees are not trying to be harmful; they simply do not have a clear understanding of the risks they are taking.
Professional AI consulting servicesassist businesses in minimizing the problems associated with Shadow AI. An experienced consultant can help map the use of existing AI tools within a company to identify the existence of any gaps in employee policies and develop a governance structure that allows for the efficient utilization of AI while managing overall exposure to risk.
Building a Defense Against Shadow AI Security Risks
Blocking a list of URLs is not enough to address the security risks posed by shadow AI. A comprehensive approach involving policies, technology, and culture is necessary to mitigate the risk associated with shadow AI.
Visibility is essential to be able to govern a network; therefore, use tools to gain visibility into the network, including network traffic monitoring, browser activity monitoring, and SaaS monitoring. Through gaining visibility into how AI tools are being used in your organization, you can determine which tools are already being used by employees.
Establish an AI governance policy that outlines which kinds of AI tools are approved and the process by which employees may share data with external systems. Ensure that the policy is readily available and easy for employees to understand.
Develop and provide approved alternatives to employees. If your organization provides approved and capable alternatives to employees, there is less incentive for employees to find alternatives.
Provide employees with training to help bridge the knowledge gap that exists with regard to the risks related to data handling, AI tools, and compliance. This will help employees to become a line of defense against shadow AI.
The Role of Governed AI Deployment
Preventing people from using AI is not the objective. Actually implementing AI can produce many real competitive advantages, such as increased productivity, enhanced analysis of information, and complete automation. It is important, however, to ensure that the adoption of AI takes place using secure, compliant, and auditable channels.
Governed deployment of AI tools involves selecting AI tools that conform to your security standards; negotiating data processing agreements with vendors; using role-based access controls; and keeping logs of AI usage for compliance purposes.
By following this approach, organizations can achieve many of the productivity gains provided by AI while at the same time protecting themselves from the security risks of shadow or unmanaged AI adoption. Governance does not slow down the speed at which an organization can adopt AI; on the contrary, it enables organizations to implement AI in a sustainable manner.
Conclusion
Shadow Artificial Intelligence is already here, not a future threat! There are AI tools being used by employees that have never received approval from an organization’s leaders, resulting in sensitive data flowing through unknown and unverified platforms. These shadow AI security risks create data leaks, violations of compliance, and accountability gaps that require immediate remediation.
Organizations that invest in effective AI governance, approved tooling, employee training, and expert counsel will reap the benefits of using AI without becoming victims of it. By utilizing professional AI integration services, organizations can ensure their AI infrastructure is built on a strong foundation of security, compliance, and future scalability.
Organizations that take action now will not only avoid risk but will also be developing the trust and governance competencies that will provide them with a competitive advantage as responsible leaders of the future.
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