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In our previous article, we’ve delved deep into discovering the potential of Generative AI for ITSM or AIOps, where users can unravel different ways to leverage the potential of LLMs to automate critical tasks and gain immense benefits.
Likewise, the potential to transform user experience with service request fulfillment using Generative AI or its underlying properties large language model 一 more usually what we call GPT 3.5 or GPT 4, can also be transformative or revolutionary.
One of many advantages that Generative AI unleashes for service desk automation is that SMEs need to make fewer efforts to create, design, approve, and publish knowledge base resources for a wider variety of enterprise use cases.
With large language models providing a significant boost in knowledge search, enterprise leaders can facilitate effortless service request management and provide an elevated user experience.
But, as the old saying goes, ‘All things have their own limits,’ large language models also have certain limitations.
Not every language model is the same, including the domain-specific use cases for which the LLMs are applied. In such a scenario, the language model behavior can be different.
Without real-time application monitoring, data protection, and access control, there could be serious vulnerabilities to service desk operations, impacting user experience while threatening the business's reputation.
What best practice should you adopt to use a large language model safely to maximize the potential of LLMs to facilitate operational and productivity efficiency and prevent threats to enterprise security?
Let’s walk you through this article, where we will discuss the best security practice for using LLMs for your service desk operations. We will also discuss the benefits and vulnerabilities of large language models to service deskautomation.
Large language models help facilitate a wide range of tasks through seamless automation, which saves service desk team time and give more energy to solve critical service request.
Service desk automation makes it easy to resolve issues at scale through self-service autonomously. But it isn’t unfair to say that the service desk also receives requests which need expert attention, such as requests for miscalculated compensation or payroll error. In such a circumstance, Generative AI helps the service desk prioritize the request, route optimization, and resolve fast.
Generative AI better resolves employee queries by refining service request responses across its models or databases. It is more capable of offering more personalized and humanized suggestions over time. Say you quickly need to know about monthly tax payments, a Generative AI gives a straightforward answer with total calculations rather than asking you to search through regular tax calculation documents or URLs and make personal assumptions.
Any unique service requests and their resolutions need to be documented. Manual classification of unique service requests can be tedious, error-prone, and even not timely drafted, documented, and published. Generative AI eliminates the pain and accelerates summarization, drafting, and publishing tasks through automated workflows.
Generative AI-based SaaS applications such as service desk conversational AI chatbots bring immense opportunities for enterprises to enhance the service desk experience by improving productivity through automated workflows and quick diagnosis of emergency service requests.
However, while Generative AI can make use of Large Language Models to transform service desks, they can also be vulnerable to user experience. Not just do LLMs expose sensitive data to those who want to satisfy their illegitimate expectations and trigger multiple vulnerabilities to raise compliance issues and reputational damage.
In a service management setting, prompt injection attacks could be as threatful as other cybersecurity vulnerabilities.
Using prompt injection into the large language models, attackers can take control of the underlying infrastructure of the model and easily manipulate them to ignore its safety guardrails or behave indifferently to quash its embedded instructions and instead perform unintended tasks.
For example, when an LLM-integrated chatbot goes offline, a compromised LLM application can take over and perform a task without raising suspicion.
A marketing bot is instructed to email the company’s existing customer contacts about a promotional offer for a new product launch. A compromised LLM shares an email template for the bot to follow exactly how it is written. That bot may do what it is asked to, although it isn’t intended on the company front.
Below is an example of a compromised LLM-generated instruction to the marketing department bot:
Another instance of prompt injection is malicious bots can intrigue users into divulging financial information and harm them.
A user complains about a misadjustment in compensation. The compromised LLM can trick the user into providing his credit card information as it assures him of real-time or instant payment adjustment.
When he discloses his information, he can lose all his money or fall prey to unintentional workplace fraud.
The example conversation shows how injected prompts can easily steal a user’s credit card information and put service desk operations in trouble.
You can well imagine the confusion and frustrations for the whole department, not to mention the reputational damage.
During LLM’s training process, chances are the training environment may expose LLM to sensitive data, especially PII personally identifiable information, proprietary company data, or other sensitive data unintentionally.
This can lead to intentional or unintentional scenarios of data leakage of sensitive information.
A legitimate user can inadvertently use prompts that would reveal sensitive data he wasn’t aware of.
This is similar to the Samsung incident, where they inadvertently disclosed the company’s data publicly.
A cybersecurity attacker would create careful prompts that work in the LLM interface and reveal secret data by manipulating LLM to retrieve data from memorization of training.
Let’s say an attacker can craft prompts to ask the LLM interface to share company contacts for a certain solution similar to their competition.
LLMs can make up facts and generate a hallucinated response, content, or other resources. This probably occurs as a large language model trains on massive crawled datasets.
Another reason is LLMs are not connected to search engines that can produce real-time factual and grounded information.
On top of it, LLMs are not trained to verify the fact of the data they are producing, and as a result, they can generate responses that can be hallucinated. Interestingly, the hallucinated responses LLMs generate are confident and compelling in nature to influence user acceptance.
LLM can lose its reasoning and intelligence to perpetuate bias and discrimination. This happens due to faulty training data, which may contain discriminatory data. As a result, human reasoning can also get affected and lead to risky environments in the workplace and everywhere.
For example, Generative AI can create content that contains offensive language to be sent to clients. Or, most probably, during a one-on-one chat with an enterprise bot, it can issue misleading guidance to an employee.
Generative AI is revolutionary for enterprise processes.Appropriate safety guardrails and good governance are mandatory to maximize its use cases and benefits.
What can you do to avoid Generative AI limitations and use its potential to improve service desk automation?
Vulnerabilities like prompt injection attacks can be restricted using model fine-tuning or preprocessing data before they are fed into the model. At a time when data sanitization removes the likelihood of LLMs searching across the database, it also does not disclose information that is otherwise harmful to the organization.
One example of fine-tuning the LLM model to reduce prompt injection is using specific instructions and training the model not to reveal any secret information.
Another effective way to restrict prompt injection attacks is by leveraging the Query Classification model to be embedded inside the Generative AI solutions to prevent attempts of prompt manipulation by applying filters.
In an enterprise context, there are many scenarios where employees can seek financial information such as compensation or tax-related questions. Chances are likely that LLMs can inadvertently or intentionally ask for Personal Identification Numbers (PII) or Payment Card Industry data.
To reduce the likelihood of these incidents, look for if training data contains any PCI Or PII information. Remove these pieces of information and mask them with an additional security layer so that hijackers cannot unmask the information if there is any leakage.
For example, if an employee has serious health complications, it is desirable that he wants to keep this information protected. When embedded in an LLM-powered chat interface, a multi-layer approach will reduce the exposure of sensitive information and keep it extremely private.
LLMs tend to behave differently due to inadequate enterprise-grade or domain-specific data. With a low level of understanding of input data, users can retrieve incorrect information. Nonsensical responses can have negative business outcomes and trigger reputational damage.
However, the hallucinations' probability will decrease by integrating a large language model with the power of conversational AI and KnowledgeAI.
Whenever an enterprise-grade query is raised, an LLM will pass it to the KnowledgeAI and extract the contextual element from conversational AI to produce a factual response.
Instead, it returns the following response,
Sorry, I have no answer to your question.
In this situation, a fallback action can take place.
To improve the resilience of large language models to reduce the tendency of biases, adversarial training works best. By ingesting the model with sample data of adversarial attacks, developers can build adversarial perturbation to impact bias in the resulting embeddings and safeguard against adversarial attacks. As a result, LLMs are less susceptible to adversarial losses and restrict biases in the workplace.
For example, if an AI system is trained on a specific period of data of an organization that reflects a certain pattern in applications forwarded by male candidates in large volume rather than female candidates, the AI model will absorb this pattern and learn to favor male candidates only. As a result, in a real-world scenario, the model may ignore female candidates and prefer male candidacy, which is likely to foster gender-neutral bias. With the help of adversarial debiasing techniques or word embedding debiasing, gender-specific bias can be restricted in the workplace.
Not only this, culture, race, or ethnicity-related bias can be mitigated using the same adversarial debiasing techniques.
No one can replace human intelligence, and it’s about providing real-time validation to any response, humans are indispensable.
Let’s not underestimate the power of human intelligence's real-time supervision and monitoring capability that helps create non-biased responses by reviewing, editing, and approving before they are passed to the live environment.
Also, the best approach is to ensure that enterprise conversations are intricately built in compliance with standard safety and quality guidelines.
This approach also helps comply with regulatory compliance such as GDPR, HIPPA, CCPA, and many others.
What can be the most effective way to mitigate LLMs risks and improve user expeirene rather than leveraging advanced reporting? Advanced analytics and reporting are handy with Generative AI solutions.
Using the reporting dashboards, you can easily reveal service request data and find metrics to improve LLM's capabilities.
Based on your analytics report, you can easily find loopholes in the capability to solve users’ problems and fine-tune the model to act more in a humanized way.
Workativ brings to you the power of hybrid NLU built within its conversational AI platform that provides app workflow automation capability. This is an advanced Generative AI solution with the capability of conversational AI that helps you achieve enterprise use cases for a diverse range of service requests.
With the ability to build your large language model with industry-specific data around the service requests or IT issues, domain-specific data, or Workativ’s data (data containing common enterprise-related IT issues), you can build a robust database to improve user response and help your employees get a response to their workplace issues in real-time and enhance their productivity.
Workativ uses hybrid NLU to augment response validity by ensuring every query finds an appropriate match and offers real-time responses to improve user satisfaction.
Available 24x7, anywhere, anytime, Workativ conversational AI can allow you to build your IT support chatbot, making it easy to create app workflow automation for any ITSM platform to solve workplace issues.
Using virtual assistants by Workativ, users have the flexibility to automate 80% of repetitive IT support issues, achieve 90% first contact resolution, and reduce MTTR by 5X.
To learn more about Workativ’s KnowledgeAI feature that allows you to leverage Generative AI and LLM, get in touch with our sales experts.
We must accept that no innovation can have only a positive spectrum. It has certain downsides.
To unleash the best of something requires patience and wearing an inclusive attitude. Excluding only pertains to depriving or discarding the best thing.
Large language models are not new but need time to evolve and scale. To gain significant business benefits out of LLMs, patience is key.
Above anything else, continuous model training and audits are imperative to improve their capabilities and bring more useful use cases for enterprises and mankind.
Over time, the large language models are supposed to become more powerful and facilitate a wide user experience everywhere with expanding use cases.
Want to learn how it is easy and fast to transform your service desk with LLM's best security practices? Connect with Workativ today.