Shadow AI: The hidden risk in AI adoption
AI is undoubtedly a game-changer. According to a McKinsey survey,78% of respondents deploy AI in at least one business process, a jump from 55% in 2023. For knowledge workers, AI saves time, makes work easier, and boosts productivity. Many (46%) of these workers would not give it up if prohibited. However, if employees use AI in non-transparent ways, it could potentially undermine data security measures and expose the company to legal risks.
Another survey found that organizations are unaware of 89% of enterprise AI usage, despite the application of security policies. Furthermore, over one-third (38%) of employees that use AI for work-related purposes admit to sharing sensitive data with AI apps without their employer’s consent. All of these represent examples of shadow AI risk.
Shadow AI results when employees or teams try to accelerate productivity or automate routine tasks by introducing AI tools without IT or data governance approval. The result is a business environment that may be subjected to serious security risks, including data exposure, compliance issues, and insider threats.
Shadow AI is driven by a combination of convenience, necessity, and the lack of regulatory oversight for organizational AI. Employees often see official IT policies as too restrictive, slow, or outdated, leading them to seek unvetted AI solutions that offer immediate efficiency gains. The reason shadow AI is proliferating is that most AI chatbots are offered freely or as a SaaS model. Cloud-hosted AI applications can be accessed remotely, and employees can learn to use such applications easily without training or IT assistance.
Often, shadow AI enables teams to respond to problems in real time. Employees who want to utilize AI to enhance competitiveness or increase operational efficiencies believe that front-office bureaucracy and rigid control by internal IT will dissuade or hinder approvals and stifle innovation.
If AI usage is not adequately managed or enforced by established security polices and transparency, its illicit applications can introduce several organizational risks, including:
Data leakage: Unapproved use of AI may allow employees to inadvertently share sensitive information with public AI models. Without data governance or encryption within the tools, confidential and proprietary information is exposed, which can result in corporate espionage. This is evidenced in a recent CISO survey, which reported that 20% of UK businesses suffered from data leakage as a result of employee misuse of generative AI.
Non-compliance risks: Many industries have strict mandates regarding data usage, privacy, and processing. Non-compliant data processing via unvetted AI tools can potentially result in legal penalties or sanctions, causing harm to reputation.
Cybersecurity risks: IT teams face challenges in maintaining visibility, control, and governance of AI tools used by employees outside of official protocols. This leads to security blind spots and hinders their ability to track the data being transmitted and determine who has access to it. Unapproved AI tools often operate on third-party cloud infrastructure, where data storage locations may be unknown or unregulated. Employees bypassing security protocols can create unmonitored attack surfaces, where external threats exploit unsecured AI interactions.
Insider threats: When employees use unauthorized AI tools, they may unknowingly expose sensitive information or intellectual property to AI platforms that store and analyze user input. This elevates the risk of information leakage and unauthorized data storage. Third-party AI vendors and competitors may intercept or steal this information and compromise the organization’s competitive advantage.
Operational risks: The use of unverified AI outputs may compromise the organization’s decision-making quality. Without adequate governance, the outputs of the AI models might be the outcome of biased data, an overfit model, and model drift, resulting in deceptive or unreliable outputs that deviate from the purpose or ethical guidelines of the organization.
- Develop acceptable AI-use policy: Create concise and simple-to-follow policies that outline your company’s expectations for the application of AI. State what tools are permissible, how they are to be applied, and who is accountable for them. The policy should also establish data use guidelines, ensure compliance, and include penalties for the misuse of AI. Timely and routine notification of policies helps educate employees and follow policies, reducing confusion and ambiguity.
- Set clear data handling guidelines: Organizations must sensitize employees about how AI solutions work and how they process data. Prohibiting users from inputting any confidential, proprietary, or sensitive data into open generative AI tools can help limit the risk of unauthorized access to company assets.
- Prioritize AI education and training: Training programs must incorporate the risks of using AI, as well as general lessons for using specific tools or solutions. Increasing awareness of the implications of using unauthorized AI tools will make companies develop a culture of responsibility. This will lead employees to seek approved alternatives or consult the IT department before introducing new applications.
- Create a secure AI culture: Building a strong security culture creates trust, accountability, and authorized AI adoption. Organizations should maintain AI security as a shared responsibility so that employees feel encouraged to report unauthorized AI usage rather than circumventing official procedures. Open communication makes employees feel more inclined to approach IT for AI training and suggestions, helping to balance usability with security.
Shadow AI presents a growing risk as employees increasingly turn to unvetted AI tools for workplace efficiency. Some vendors I’ve spoken with take a ‘walled garden’ approach and actually build their own generative AI applications using years’ worth of data they deem trustworthy, such as historical virus signatures and data generated by their employees, customers, and closest partners. Large Language Models are only as good as the data they are trained on.
The goal is not to stifle the use of AI but to integrate it securely, allowing organizations to capitalize on the benefits of AI while prioritizing security.
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