Maximizing the ROI on LLMs for Enterprise

The advent of large language model (LLM) applications offers significant potential for major corporations. However, the hazards associated with the swift and unregulated implementation of LLMs can be substantial, as demonstrated by a recent incident involving Open AI. The organization inadvertently revealed the titles of some user conversations via ChatGPT without obtaining the necessary permissions, a potential violation of the General Data Protection Regulation (GDPR) that is currently under investigation. The penalties for such breaches can be hefty for large corporations, as evidenced by Amazon's $877 million fine in 2021 for GDPR violations and Didi's $1.2 billion fine in 2022 for Chinese cybersecurity breaches. Apart from compliance risks, there are few barriers to prevent employees in large corporations from unintentionally disclosing company secrets or confidential information to third-party LLM services.

For large corporations to effectively implement LLMs on a large scale and optimize the return on their LLM investments, they need to devise a strategy that thoroughly addresses the risks associated with LLMs.

In this article, we will discuss the following challenges:

• Key risk factors that corporations need to consider when using LLM services.
• The dangers of exposing highly confidential or private corporate data and the seriousness of potential compliance violations.
• A roadmap for creating LLM services that respect privacy and comply with regulations, thereby reducing these risks.

Key privacy and compliance challenges when leveraging LLMs for the large enterprise

While employees are usually bound by non-disclosure agreements (NDAs), it can be a challenge for a large corporation with over 10,000 employees to ensure complete compliance with these agreements. The widespread use of third-party LLM querying services by the general public poses a direct risk that employees might unintentionally disclose confidential company information or regulated personal data to third-party LLM APIs hosted on servers or virtual private clouds (VPCs) owned by third parties. For instance, an engineer might want to summarize a confidential patent application to create an abstract and could use third-party LLM APIs to do this. The employer can only hope that this third-party service has robust data deletion and control measures in place to ensure this confidential data is properly protected and removed when requested. However, as the recent issue with Open AI demonstrated, such hopes might be overly optimistic.

How Mano ensures you remain compliant?

To ensure sensitive data is kept secure and managed according to your company's data governance standards, we strongly recommend that large corporations consider deploying their own local LLM services within their VPC or on-premise servers. This would allow employees to use fine LLMs to enhance their workflow in an environment completely contained within the company's VPC, reducing the risk of uncontrolled spread of sensitive and regulated company data to third-party services. These corporations will need to develop LLMs with performance levels that encourage employees to use their locally deployed services. To achieve competitive performance, corporations will need to fine-tune models for specific tasks using large amounts of task-specific data. Our partnership with GigaML allows fine tuning llama 2 with comparable performance with GPT4.

Training and fine-tuning performant LLMs for enterprise applications requires the collection of massive datasets

Today's cutting-edge Language Learning Models (LLMs) are powered by an impressive 500+ GB of data, sourced from a diverse range of books, web texts, and articles, and further refined with additional data. For large corporations to create their own bespoke LLM services, tailored to their unique workloads such as generating custom company reports or summarizing routine documents, they must amass and consolidate substantial volumes of training data pertinent to their applications.

These corporations have the golden opportunity to harness the vast amounts of data generated internally within their organization, or even potentially capture data from partners and clients they collaborate with. However, these corporations often grapple with the daunting task of aggregating data across isolated datasets within their own organization. For instance, a multinational corporation may find it challenging to centrally consolidate data produced by branches situated in various jurisdictions. Similarly, large financial institutions often encounter significant hurdles when it comes to sharing data between internal teams, such as wealth management divisions, commercial, and retail banking business units. The sensitive nature of these unstructured text data, which may contain company secrets or customer Personally Identifiable Information (PII), necessitates meticulous tracking and monitoring to ensure GDPR compliance.

How Mano can help create data lakes to be used by our partners for fine tuning?

Mano offers a revolutionary solution to the data aggregation challenges faced by large enterprises. By creating aggregated data lakes, Mano provides a centralized repository for data from various sources, including in-house, partners, and clients. This eliminates the struggle of consolidating data across disparate datasets within an organization.

These data lakes not only unify an organization's data but also ensure sensitive information, such as company secrets or customer PII, is meticulously tracked and monitored for GDPR compliance.

By leveraging Mano's data lakes, enterprises can effectively fine-tune their LLM services, optimizing them for specific workloads through our direct partnership with GigaML. This solution unlocks new levels of performance and productivity, enabling enterprises to fully harness the potential of their data.

LLM models inherently pose significant data leakage threats.

The academic community has long recognized the substantial data leakage risks associated with deep neural network models trained on sensitive datasets. For instance, basic techniques can be used to recreate sensitive training data from LLM models, even without access to the original training dataset.

Key data leakage vulnerabilities related to LLMs include:

1. Unintended Data Memorization: This occurs when a generative LLM model consistently produces the exact text from its training documents. A 2021 study outlined a statistical method to detect LLM outputs likely to be exact memorized samples from the training dataset. The researchers extracted 604 unique memorized training examples, including 46 instances of personal identifiable information (PII), by simply making random queries to a GPT-2 API.

2. Model Inversion Attacks: This technique allows for the reconstruction of training data from the LLM model file. A recent announcement revealed a successful model inversion attack on text data used to train a GPT-2 model.

3. Membership Inference Attacks: This attack determines whether a specific data point was used in the training dataset. Unlike the previous two attacks, membership inference can specifically query if a target phrase was included in the training data. This makes it particularly potent and a significant risk when training LLMs on sensitive datasets. Alarmingly, a recent study found that common LLMs, known as masked language models, are highly vulnerable to likelihood ratio membership inference attacks.

How Mano’s LLM solution can prevent data leaks?

Differential privacy is a proven method to mitigate these data leakage attacks. It introduces noise and restricts weight updates during the model training process. This addition of noise provides plausible deniability, as the outputs of the aforementioned attacks are generated using a noised process, making it impossible for the attacker to confidently claim that the output belongs to the original training data. Furthermore, differential privacy significantly impairs the effectiveness of inversion and membership attacks and has been proven to address memorization vulnerabilities.

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