Risks and Challenges of Deploying Custom LLMs in Enterprises by GPT 4o

Building custom Large Language Models (LLMs) with proprietary data offers numerous benefits, but it’s essential to recognize the associated risks and challenges. This article will explore the potential pitfalls that organizations may face when deploying custom LLMs and how they can mitigate these risks.

1. Data Privacy and Security Concerns

When building custom LLMs, one of the most significant risks involves data privacy and security. Proprietary data often includes sensitive information such as customer records, intellectual property, financial data, or confidential internal communications. Training an LLM on this sensitive data requires extreme caution to avoid breaches or leaks.

Example: In sectors like healthcare or finance, there are strict regulations governing data privacy, such as HIPAA in the United States or GDPR in Europe. If a healthcare organization were to use patient records to train a custom LLM, any failure to protect this data could result in severe legal repercussions and damage to the organization’s reputation.

Mitigation Strategies:

• Encryption: Ensure all data is encrypted during the training process, both in storage and transit.

• Access Control: Limit who can access the data and the LLM model itself to prevent unauthorized usage.

• Data Anonymization: Consider anonymizing data to remove personally identifiable information (PII), ensuring privacy even during training.

2. Model Bias and Ethical Concerns

Custom LLMs, like general LLMs, are prone to inheriting biases present in the training data. If the proprietary data used to train the model reflects biased assumptions or incomplete perspectives, the resulting model may produce skewed outputs. This can lead to ethical concerns, particularly when the model is used for decision-making in areas such as hiring, finance, or healthcare.

Example: A financial institution using a custom LLM to analyze credit applications could unintentionally build in biases that disadvantage certain demographic groups if the historical data contains those biases.

Mitigation Strategies:

• Bias Audits: Regularly audit the model’s outputs for bias and ensure that diverse perspectives are included in the training data.

• Diverse Datasets: Incorporate a range of diverse, balanced data sources to reduce the risk of inheriting historical biases.

• Ethical Oversight: Create an ethical AI board to oversee the deployment of the custom LLM and ensure it aligns with organizational values.

3. Training Complexity and Cost

Training custom LLMs can be a resource-intensive process. Organizations need access to high-performance computing (HPC) infrastructure, significant data storage, and skilled professionals to manage the process. The cost of training an LLM, particularly a large-scale model, can be exorbitant, especially when factoring in the compute power and engineering talent required.

Example: Training GPT-3 (the model that underpins ChatGPT) reportedly cost millions of dollars in compute resources alone. While smaller organizations may not need models as large, creating even moderately sized LLMs with proprietary data can still stretch resources.

Mitigation Strategies:

• Cloud-Based Solutions: Leverage cloud computing platforms like Azure, AWS, or Google Cloud for scalable training environments without upfront infrastructure costs.

• Model Compression: Use techniques like model distillation or parameter pruning to reduce the size and complexity of the LLM while maintaining performance.

• Incremental Training: Consider fine-tuning smaller, pre-trained models like BERT or GPT-4 rather than building an entirely new model from scratch.

4. Organizational Change and Buy-In

Deploying custom LLMs can require significant shifts in how an organization operates, from adopting new technologies to restructuring workflows. Organizational resistance to change is one of the primary challenges that enterprises face when implementing AI solutions. Employees may fear job displacement or find it difficult to adapt to new AI-enhanced processes.

Example: McKinsey’s rollout of Lilli, their custom LLM, required not only technical development but also buy-in from employees across the organization. To get 7,000 consultants onboard with using Lilli, McKinsey had to implement training programs and showcase how the tool augmented their work rather than replaced them.

Mitigation Strategies:

• Change Management Programs: Invest in change management to help employees understand the value of the LLM and how it enhances their role.

• Training and Upskilling: Offer training programs that show employees how to use AI tools effectively and integrate them into their workflows.

• Clear Communication: Regularly communicate with teams about how the LLM will be used and address any concerns they may have regarding automation.

5. Model Drift and Maintenance

Once deployed, custom LLMs must be maintained to ensure they continue to perform well. Over time, the data landscape changes, and models can suffer from model drift, where their predictions or outputs degrade because they’re no longer aligned with current realities. This is especially relevant in industries like finance, where market conditions change rapidly, or in sectors like healthcare, where new research continuously emerges.

Example: A custom LLM trained on financial data in 2019 may struggle to adapt to post-pandemic market conditions if not regularly updated with new data.

Mitigation Strategies:

• Continuous Training: Regularly retrain the model with new data to keep it up to date with the latest trends and developments.

• Monitoring Tools: Implement monitoring systems to track the model’s performance in real-time and flag instances where it may be drifting from expected outputs.

• Cross-Functional Teams: Create cross-functional teams of data scientists, domain experts, and IT professionals to manage ongoing updates and adjustments to the model.

6. Vendor Lock-In

When building custom LLMs, organizations may become dependent on specific vendors for cloud infrastructure, AI platforms, or technical support. This can lead to vendor lock-in, where switching to another provider becomes difficult or costly.

Example: McKinsey’s Lilli is hosted on Microsoft’s Azure platform, making it reliant on Microsoft for scalability, data management, and other operational elements. While this may offer benefits, it also creates a level of dependency on Microsoft’s cloud services.

Mitigation Strategies:

• Multi-Cloud Strategy: Use a multi-cloud approach to avoid complete reliance on a single vendor.

• Open-Source Solutions: Where possible, leverage open-source tools or frameworks that allow for greater flexibility and independence from vendors.

• Exit Strategy: Ensure that contracts with cloud vendors include exit strategies and data portability options to avoid being locked in indefinitely.