How Generative AI Amplifies Unauthorized Access Privacy Risks with Enterprise LLMs
As around the world that are interested in leveraging Generative AI and are concerned about their data being captured in public Generative AI systems are flocking towards using Enterprise Large Language Models (LLMs) to create more control over the data input into LLMs and to ensure reduced Data Privacy and Cybersecurity risks. However, while Enterprise LLMs can mitigate certain vulnerabilities, they may open the door to new categories of risks that organizations may not have previously considered, notably the amplification of Unauthorized Access.
What is Unauthorized Access?
Terms like Data Breach and Unauthorized Access are often used interchangeably, creating confusion. Unauthorized Access is the cousin to Data Breach, where a data breach is often a word used to describe some kind of system compromise, while unauthorized access is a kind of crossed boundary related to permissions. A Data Breach signifies a more sweeping form of compromise that usually involves significant exposure or outright theft of data. Unauthorized Access is a subtler yet no less serious issue, where someone gains access to data without the proper permissions. This individual could be either an outsider or even an authorized user who crosses the lines of their specific permission boundaries. The legal implications of such unauthorized actions vary widely depending on jurisdictional laws, adding another layer of complexity to the problem.
The challenge with LLMs and Unauthorized Access is that the individual may have been provided data inadvertently that they should not be privy to in an accident which can be considered a form of Unauthorized Access. This article will discuss the Unauthorized Access challenges that organizations face when dealing with LLMs and their access to human, company, and model data.
Unauthorized Access to Human Data
Human data may include a wide array of sensitive, personally identifiable information (PII) and personal data that may be exposed to Generative AI systems, intentionally or inadvertently. This data could include everything from social security numbers to personal medical records. Once exposed to the AI system, these pieces of data could be used to place the individuals' data at risk, such as impacting their privacy rights, facilitating identity theft, or enabling fraudulent activities. Because AI systems, particularly language models like LLMs, continually learn from the data they process, the risks can perpetuate and multiply over time.
What can organizations do to minimize the Unauthorized Access of Human Data Risks:
Implement strict policies for data handling and classification to mitigate the risk of human data exposure
Regularly update employee training to instill best practices in data protection and handling protocols
Unauthorized Access to Company Data
Unauthorized Access to company data represents a particularly alarming risk, one of the top concerns of organizations wanting to use Enterprise LLMs. This category includes proprietary information like trade secrets or highly confidential business plans and strategies. For example, an AI system might be used to develop a new, innovative product, and this data may become accessible to unauthorized users. Losing a competitive edge could be devastating if the information becomes known outside of authorized individuals. Also, if plans for things like mergers or expansions are exposed, it could give competitors an unfair advantage and even affect stock or deal pricing. Besides the immediate impact of Unauthorized Access to company data, there are legal considerations, such as non-disclosure agreements and other contractual obligations, that might be violated.
What can organizations do to minimize the Unauthorized Access of Company Data Risks:
Institute stringent internal access controls to prevent Unauthorized Access to confidential company data
Continuously monitor access logs to detect Unauthorized Access early
Unauthorized Access to Model Data
Unauthorized Access to the core data that powers the AI model presents uniquely insidious risks. This can lead to 'data poisoning,' a process where malicious actors corrupt the model by feeding it skewed or incorrect data. In turn, this compromises the model's output and can have a cascading effect on any business operations reliant on LLMs, such as healthcare diagnoses or financial predictions. Given the increasing reliance on AI systems for critical decision-making, the impact could be far-reaching.
What can organizations do to minimize the Unauthorized Access of Model Data Risks:
Adopt monitoring systems specifically designed to validate and oversee both model inputs and outputs
Develop a governance framework for the AI model, outlining data handling and auditing protocols
Unauthorized Access and the Jurisdictional Variable
The considerations surrounding Unauthorized Access become even more complicated when factoring in differing jurisdictional regulations. Different jurisdictions, like the European Union with the General Data Protection Regulation (GDPR), California with the California Consumer Privacy Act (CCPA), and all 50 US States with unique Data Breach notification laws, have distinct rules and requirements concerning reporting unauthorized access incidents. Understanding these local nuances is critical for global operations.
What can organizations do to manage Unauthorized Access and the Jurisdictional Variable:
Regularly update organizational awareness on jurisdiction-specific privacy regulations to remain in compliance
Integrate these regulations into your Unauthorized Access response plan to ensure appropriate reporting protocols
Conduct periodic audits of data handling procedures to ensure they meet the requirements of all applicable jurisdictional laws
Enterprise Large Language Models are powerful tools for organizations aiming to make the most of Generative AI technology. However, despite being brought in-house, these systems are not without risk. The risk of Unauthorized Access to human data, company data, and even the core AI model data needs to be considered. While mitigating these risks requires a multi-pronged approach—spanning education, technology, and governance—failing to adequately address them can result in financial, legal, and reputational damage that could severely undercut the benefits of adopting Generative AI in the enterprise. When organizations take the proactive steps needed to mitigate the Unauthorized Access risks of using Enterprise LLMs, they can make Data Privacy a Business Advantage.
