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How to address shadow AI risks with an AI employee helpdesk

Thu Mar 28 2024

4 mins read
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How to address shadow AI risks with an AI employee helpdesk
Shadow AI - also dubbed “shadow IT” or “BYOAI,”- is a term describing unsanctioned or ad-hoc generative AI use within an organization that's outside IT governance. This can occur when individual departments or employees adopt AI-powered tools or solutions independently, often without the knowledge or approval of central IT or data management teams. That enterprising spirit can be great, in a vacuum—but eager employees may lack relevant information or perspective regarding security, privacy or compliance. This can expose businesses to a great deal of risk. For example, an employee might unknowingly feed trade secrets to a public-facing AI model that continually trains on user input, or use copyright-protected material to train a proprietary model for content generation and expose their company to legal action. \ \ Shadow AI is an issue often overlooked when thinking about the risks of inefficient and slow employee support. And that’s too bad: according to [Salesforce](https://www.salesforce.com/news/press-releases/2023/09/07/ai-usage-research/), 75% of users are looking to automate tasks at work by using generative AI. But eager employees may lack relevant information or perspective regarding security, privacy or compliance. This can significantly increase the risk exposure for businesses. For instance, an employee could inadvertently disclose trade secrets by feeding them into a publicly accessible AI model that continuously learns from user input. \ \ As if IT departments didn't have enough on their plate, the adoption of generative AI is gaining momentum. As per the Salesforce survey, 52% of respondents noted a rise in their usage of generative AI since its initial implementation. This surge signifies that the challenge of shadow AI is not only here but is also expanding, posing a growing concern for IT. \ \ "The reality is, everybody's using it," said Matt Barrington, Americas emerging technologies leader at EY about recent [EY research](https://www.ey.com/en_us/consulting/businesses-can-stop-rising-ai-use-from-fueling-anxiety) finding that 90% of respondents used AI at work. "Whether you like it or not, your people are using it today, so you should figure out how to align them to its ethical and responsible use." ## Why employees resort to shadow AI? There are various reasons why employees resort to shadow IT, including: 1. Perceiving the IT/helpdesk department as slow and unresponsive or lacking the expertise to manage new technologies effectively. Think about it: with the Mean Time to Resolve an issue being 26 hours, oftentimes they can’t be bothered to remain idle for so long and potentially be unproductive. 2. Needing solutions for specific use cases that approved applications don't cover. 3. Facing budget limitations that prevent the adoption of officially sanctioned enterprise-level versions of emerging technologies. 4. Finding the technological and tooling constraints of approved projects too limiting for their needs. They thus turn to user-friendly Generative AI tools such as ChatGPT, which they can try out in their web browsers with little difficulty - without going through IT review and approval processes. The impulse is understandable, but shadow AI -- as with any sanctioned large language model (LLM) AI project -- presents various cybersecurity and business risks. ## What are the risks associated with shadow AI? Shadow AI can present various challenges and risks to companies, including the following: - **Functional risks**: Functional risks arise from the operational capabilities of an AI tool. Take, for instance, model drift, which occurs when the AI model deviates from its intended purpose due to changes in the technical environment or outdated training data. This deviation renders the model ineffective and potentially misleading, posing a functional risk to the company. - **Operational risks**: Operational risks pose threats to the company’s ability to conduct business effectively. These risks manifest in various forms. For example, a shadow AI tool might provide inaccurate advice due to model drift or might be hallucinating and generating false information. Acting on such advice could lead to wrong decisions. - **Data security and privacy concerns**: When using an unsanctioned AI system without proper oversight, there's a risk of sensitive data being mishandled or exposed, leading to potential breaches or compliance violations. For instance, a chatbot might ingest information included in an employee’s prompt; use it as training data; make it available to platform operators; and make it available to other users outside the company when answering their prompts. If the AI platform were to suffer a cyberattack, the data could also fall into cybercriminals' hands. Sharing such sensitive information with an LLM poses significant risks, including jeopardizing intellectual property, empowering competitors, violating data privacy regulations, eroding customer trust, and tarnishing the company’s reputation. - **Legal risks**: These materialize if shadow AI exposes the company to lawsuits or fines. Say the model advises leadership on business strategy. But the information is incorrect, and the company wastes a huge amount of money doing the wrong thing. Shareholders might sue. Lawsuits might also materialize if the shadow tool provides customers with bad advice generated by model drift or poisoned training data or if the model uses copyright-protected data for self-training. And, of course, violations of data privacy regulations could result in hefty legal penalties. - **Costs**: There can be wasteful or duplicative spending among shadow projects or between shadow and sanctioned ones. In some cases, shadow AI users may also waste money by failing to take advantage of volume and negotiated rates for similar, sanctioned technology. Consider, too, the opportunity cost stemming from shadow projects that ultimately fail because they do not follow company policies or good practices - that time and money could have been put toward other projects. - **Fragmentation and inconsistency**: Different departments or teams using disparate shadow AI tools can lead to inconsistencies in resource procurement, configuration and use. - **Control**: IT teams cannot see shadow IT resources, which means they can’t manage, organize and support them. ## Unmasking shadow AI: how to get started To address the challenges posed by shadow AI, companies need to implement robust governance frameworks and establish clear policies regarding the adoption and use of AI technologies. They should also encourage the collaboration between IT and business leaders to understand how various departments want to use AI. Additionally, centralizing AI deployment and management under the supervision of IT and data governance teams can help mitigate risks and ensure that AI initiatives contribute positively to the organization's overall success. \ \ However, as mentioned before, there is no guaranteed way to keep employees from using Generative AI to work more efficiently. Therefore, the most effective way to address Shadow IT is to provide an approved AI helpdesk platform. An AI employee helpdesk is a secure and always available support platform built for an AI-first world. Thanks to advanced language models, it offers dynamic, human-like conversations, seamlessly integrating with various communication channels and IT systems. It automates tasks and support resolution, and empowers employees to act, search, query, and generate content across a multitude of enterprise applications. Fueled by constantly improving generative models, AI-driven analytics for performance evaluation, and developer tools for customizable use cases, it's the future of assistance. ## Addressing shadow IT challenges with an AI employee helpdesk The AI helpdesk's swift and dependable issue resolution encourages employees to seek help from IT instead of resorting to unsafe fixes and shadow AI. Let's review how it addresses the main reasons why users resort to shadow AI in the first place. You will then understand why an AI employee helpdesk such as Gaspar AI can be your best ally in your fight against shadow IT. \ \ **1. Slow response times and lacking expertise** - **24/7 availability**: the AI helpdesk is available anytime, anywhere, so employees don’t have to rely on the agents’ work schedule or resort to unsecure fixes when the helpdesk team is not available. - **Instant resolution**: By automatically solving their most common issues, employees have no reason to seek help elsewhere. Also, by facilitating handoff to the right agent, they can resolve even the most complex issues easier and more quickly. - **Ease of use**: Thanks to LLM training, NLP and NLU, employees just talk to the AI helpdesk’s chatbot in everyday language on your company’s chat platform, e.g. Slack, MS Teams. They are instantly understood, engage in interactive conversations and get human-like answers. They now have one user-friendly, unified solution for their support. - **Accurate answers based on your company’s data**: All answers generated are precise and based on your company’s information, as opposed to using shadow AI. At Gaspar AI for instance, we use Retrieval Augmented Generation which means that our language model generates answers based on your knowledge base. - **AI trained with sector-specific datasets**: This means that answers accurately depict your company’s specific environment and context. **2. No approved solutions for specific use cases** - **AI customization**: It’s very easy to build specific uses cases with an AI tool. With Gaspar AI for example, you can automate workflows (e.g. employee onboarding) and connect systems without coding – just use our intuitive templates! - **Out-the-box integrations**: Most AI helpdesks come with ready-to-use integrations that offer solutions to the most common use cases. **3. No access to officially sanctioned solutions due to budget limitations** - **Low cost**: Contrary to what one would believe, an AI helpdesk can be a very cost-effective solution making it possible to be adopted at enterprise level. Gaspar AI, for example, has a cost of $4 per user monthly, therefore allowing even small companies to deploy it. **4. Limiting technological and tooling constraints of approved projects** - **Scalability**: A well-chosen AI helpdesk is easy to scale and accommodate more use cases and increasing support demands. - **Flexibility**: AI employee helpdesks offer flexibility to connect with tools such as IT management systems and commonly used apps, covering most user needs. - **Multiple features**: A good AI helpdesk offers a wide variety of features such as issue resolution, ticket management, asset management, incident management, change management, business insights, analytics and reporting. ## From shadow AI to safe solutions: harnessing the power of AI helpdesks Security, IT and risk leaders should not expect shadow AI to go away any time soon. As new-generation LLMs become more numerous and diverse, there is every reason to expect shadow AI projects will multiply as well – an AI employee helpdesk is probably the safest and smartest solution. As generative AI usage becomes increasingly pervasive, companies will need to consider a broader strategy which will include adopting helpdesk tools that better meet business needs and encourage ethical and safe AI use. And data shows the effort is worth it. 76% of IT leaders believe GenAI will be significant if not transformative for their organizations, and 65% believe they will see meaningful results within the next year according to a [recent Dell survey](https://www.dell.com/en-us/perspectives/new-research-the-dell-genai-pulse-survey/?dgc=Af&cid=aithoughtleadership&lid=Forbes14). \ \ With an AI employee helpdesk such as Gaspar AI you can offer easy, instant, accurate and scalable support to end users minimizing their eagerness to seek solutions in unsafe places. You can rest assured that your company’s data and sensitive information will remain private. Any information collected from our customers is strictly confidential and will never be shared with other customers or utilized to train our generic AI model. The sole purpose of collecting these data is to enhance and personalize your individual model for an improved and tailored experience. What’s more, all generated answers and solutions adhere to existing permissions and access rights. Employees won’t be able to view or access information they are not supposed to. \ \ If you’d like to start addressing shadow AI projects today, you can [schedule a free demo](https://www.gaspar.ai/demo-request) where we will discuss your company-specific needs, show you around the platform and experience first-hand the magic of AI-powered support. \ \ _Citations:_ - _TechTarget, 10 top AI and machine learning trends for 2024, https://www.techtarget.com/searchenterpriseai/tip/9-top-AI-and-machine-learning-trends_ - _Forbes, What Is Shadow AI And What Can IT Do About It https://www.forbes.com/sites/delltechnologies/2023/10/31/what-is-shadow-ai-and-what-can-it-do-about-it/_ - _TechTarget, Shadow AI poses new generation of threats to enterprise IT, https://www.techtarget.com/searchsecurity/tip/Shadow-AI-poses-new-generation-of-threats-to-enterprise-IT#:~:text=Operational%20risks%20come%20in%20many,%2D%2D%20i.e.%2C%20generating%20false%20information_ - _EY, AI Anxiety in Business Survey October 2023, https://www.ey.com/en_us/consulting/businesses-can-stop-rising-ai-use-from-fueling-anxiety_ - _The Dell GenAI Pulse Survey October 2023, https://www.dell.com/en-us/perspectives/new-research-the-dell-genai-pulse-survey/?dgc=Af&cid=aithoughtleadership&lid=Forbes14_