How Employees Can Deal With Security Challenges When Handling LLMs At Work

By Mohan Subrahmanya

Increasingly, organisations are using Large Language Models (LLMs) to improve efficiency and boost productivity in the workplace. They are being used to automate repetitive tasks such as report generation, data summarisation, research, predictive modelling, and trend analysis. This, in turn, improves the decision-making process and enhances collaboration and employee productivity.

In fact, LLMs are seen as the game-changers for smaller companies because of the scalability, customisation, creativity, consistency and ease of use that come at a fraction of the cost. LLM-based tools can be leveraged by every department to enhance the business processes and functioning.

Security risks employees face with LLMs

While we know the tremendous benefits that LLMs can offer an organisation, it is crucial to be aware of the security challenges they pose for employees in the workplace. Data security and privacy are the most crucial risks that top the list of challenges, followed by compliance concerns and possible misuse with malicious intent. 

Companies process large amounts using LLMs to generate business insights; this massive amount of data is stored by the LLMs, which makes them prime targets for data breaches by hackers. Hackers and cyber criminals pose risks by gaining unauthorised access, compromising the model's integrity and misusing its confidential data.

Once a hacker gains access to the LLM structure, they manipulate the model to leverage its vulnerabilities and misuse the LLMs for malicious activities. They can exploit the LLM's biases and engineer data that generates false or amplified information, which can affect an organisation's business processes and outcomes. 

An LLM that has been exploited by an external agency can generate and spread misinformation, which in turn negatively impacts the business outcomes of an organisation. In fact, this can even land a company in ethical and legal issues. For instance, if an LLM is manipulated to produce biased or false information about a competitor, it could lead to defamation lawsuits and damage to the organisation's reputation. Additionally, if an LLM is used to spread false product information, it could result in regulatory penalties and loss of customer trust.

Ways to deal with LLM-related security challenges

Contrary to popular perception, managing challenges posed by LLM-powered systems in an organisation is not difficult. Here are some ways one can avoid security challenges:  

  • Putting in place a regulatory framework for data security and privacy is the first and most crucial step. There is a growing list of regulatory frameworks for organisations to consider when deploying LLMs. They should integrate existing legislation along with new laws written specifically to address emerging threats to LLM security.
  • Once the regulatory framework has been put in place, the organisation should work on staying compliant with the regulations. Logging and auditing the data and tracking interactions with LLMs can help in building transparency and enable quick compliance checks. Aligning with the LLMs provider's data retention policies and ensuring an agreed-upon timeframe with the LLM provider is crucial to keep data stored and available for use by the organisation. 
  • Businesses should put in place data privacy and confidentiality policies. Strategies like limiting data exposure, anonymisation, access controls and on-premise or private models go a long way in preventing security breaches. The workforce should be trained extensively to avoid inputting sensitive or proprietary data into the LLM systems. Even if such information needs to be put, they should use an anonymisation technique – masking personally identifiable information – before processing the data. 
  • Access control is another strategy that can be implemented by organisations to restrict the use of LLM. Access levels can be defined by the nature of the job or the project that an employee is handling. To ensure data safety, organisations can put controls in place to manage the kind of data each employee is meant to interact with. 
  • Using an on-premise or private cloud infrastructure for LLM deployment ensures that LLM-based systems cannot be breached by outside hackers easily. Besides, this also gives a lot more control to the information security teams to track and prevent data security threats. 
  • Training and upskilling the workforce around LLM-based tools and keeping them informed about security issues is a good step to undertake. Employees should be trained in security best practices to prevent security and privacy breaches. They need to be aware of external risks like social engineering, online scams, and phishing. They should be kept abreast of all policies around LLM usage and the ethical considerations in mind while generating content using LLMs. 

Every organisation should prioritise integrating ethical AI practices among its employees. Therefore, implementing a regulatory framework, robust security measures, and regular auditing of the systems can help businesses take advantage of LLMs without compromising security.

(The author is the Country Leader - India, Insight Enterprises)

Disclaimer: The opinions, beliefs, and views expressed by the various authors and forum participants on this website are personal and do not reflect the opinions, beliefs, and views of ABP Network Pvt. Ltd.

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