Why This Matters

Adoption of large language models (LLMs) is accelerating rapidly—not just because of ChatGPT, but because the broader landscape of generative AI has matured. Capabilities are expanding across text, image, audio, and video generation, and a growing number of vendors are offering high-performance, enterprise-ready models. Often referred to by the name of one of their most well-known variants—generative pre-trained transformers or GPTs—these large language models are moving from experimentation to integration, becoming part of real product experiences, customer service workflows, and internal decision support systems.

Unlike traditional software, LLMs don’t follow fixed logic. They attempt to reason by synthesizing information, inferring intent, and generating outputs that appear intelligent based on patterns in massive training datasets. That reasoning can be astonishingly effective—but it also introduces unique risks. It can surface confidential information, produce convincingly wrong answers, or change behavior silently over time.

Most leaders are aware that these systems come with risk. But the full range of risks isn’t always clear or known—especially as LLMs are deployed into new and evolving use cases. In this post, we’ll unpack three key risks you need to understand to lead well in this space: data leakage, hallucinations, and model drift. And more importantly, we’ll outline clear principles for mitigating those risks so your teams can harness the power of LLMs responsibly, safely, and effectively.

Three Hidden Risks of LLMs

Data Leakage: When Smart Models Spill Secrets

One of the most significant—and often underestimated—risks in working with LLMs is data leakage. Unlike traditional systems, generative models are trained on vast corpora of text and learn patterns in ways that are difficult to audit. While these models don’t intend to memorize sensitive information, under the hood, they sometimes do—especially when fine-tuned on narrow datasets or prompted in specific ways.

There are two primary forms of leakage that enterprise leaders should be aware of:

  1. Model Training Leakage – When sensitive data is included in a model’s training set, there’s a risk that this information could be echoed verbatim under the right prompting conditions. This has been demonstrated in recent research, where LLMs were manipulated to reproduce training examples from publicly available web data [1]. While that study used public sources, it highlights the real potential for leakage of private or proprietary information in fine-tuned systems. For organizations considering custom training on internal datasets, this raises critical questions about exposure risk—especially when relying on third-party infrastructure or tools.
  2. Prompt Input Leakage – Even without training a model, using LLMs via APIs or SaaS platforms can pose risk if employees submit sensitive data, such as customer records, legal documents, or strategic plans, via prompts. Once submitted, that data leaves the organization's boundaries and may reside in third-party systems where it could be exposed through logs, backups, or internal access by contractors or employees. The alleged OmniGPT breach [2], which, if validated, would have exposed over 34 million user interactions, underscores the danger of weak security and data governance around AI tools.

The implications of leakage are far-reaching: compromised intellectual property, regulatory violations (e.g., GDPR, HIPAA), and erosion of customer or partner trust.

While many leaders are familiar with the basic rule: “don’t paste private data into ChatGPT", the risk surface is broader and more nuanced. Leakage can occur not only through careless input, but through deeply integrated workflows where LLMs access internal systems, ingest private documentation, or serve as copilots for high-stakes tasks.

Hallucinations: Confidently Wrong, Dangerously Convincing

LLMs are incredibly good at generating fluent, persuasive text, but fluency is not the same as accuracy. One of the most persistent risks with these models is hallucination—the confident generation of information that is simply not true.

Unlike traditional systems that retrieve facts from a database or follow explicit logic, LLMs generate responses based on probabilities across vast training data. When prompted for information that isn’t in its training data, or when the prompt is ambiguous, a model may fabricate plausible-sounding answers that are incorrect, outdated, or entirely invented.

These hallucinations can be subtle, but the consequences can be serious. Consider a chatbot that cites a non-existent policy to a customer, an internal assistant that invents a regulatory guideline in a compliance context, or a product requirements generator that fabricates features or technical constraints. In high-stakes or regulated environments, hallucinations can lead to misinformation, legal exposure, or poor strategic decisions.

The challenge is compounded by the confidence with which LLMs deliver these responses. Because the language is often polished and authoritative, it's easy for users to miss that something is fabricated, especially when the model fills in gaps with contextually relevant but unverified content.

Hallucination isn’t a bug, it’s a byproduct of how these models work. Even the most advanced systems, including GPT-4, have been shown to hallucinate under certain conditions. For example, recent studies have found GPT-4 fabricating academic citations [3]. In fact, hallucinations are so common across large language models that Vectara maintains a live leaderboard tracking hallucination rates for many of the most widely used systems.

Understanding that hallucinations are an inherent feature, not just an occasional glitch, is key to deploying LLMs responsibly and with realistic expectations.

Model Drift: When Behavior Changes Without Warning

A less visible but equally important risk of working with LLMs is model drift—the change in a model’s behavior over time. Unlike traditional software, which remains static unless explicitly updated, LLMs can evolve in ways that are subtle and difficult to detect, especially when accessed through third-party APIs. 

This can happen in two primary ways:

  1. Provider Updates. When using a hosted LLM from vendors like OpenAI, Anthropic, or Google, changes to the underlying model may be introduced without prior notice. These updates often aim to improve general performance, but they can also alter how the model interprets prompts, responds to questions, or handles specific use cases. If your product or workflow relies on consistent model behavior, even small shifts can cause unexpected results.
  2. Misalignment with Business Context. In custom or fine-tuned models, drift can result from outdated training data, evolving internal processes, or shifting user expectations. A model fine-tuned on last quarter’s documentation might no longer reflect current features or policies, leading to inaccurate or irrelevant responses.

The risk is particularly acute in production environments where LLMs are integrated into customer-facing experiences, compliance-driven workflows, or operational tools. A quiet change in how a model answers questions, prioritizes information, or interprets tone can introduce confusion, reduce trust, or even cause harm, especially if those changes go unnoticed.

Model drift is challenging because it’s often invisible until something goes wrong. Without consistent testing, reference comparisons, or behavior monitoring, it’s easy to miss until the consequences are felt.

Recognizing that LLMs do not remain static is a key part of leading responsible AI adoption. Monitoring for drift and planning for model evaluation over time should be part of any production deployment strategy.

Mitigation Principles: A Risk-Aligned Framework

Mitigating the risks posed by LLMs requires more than generic “AI safety” guidelines. To be effective, your strategy should address the specific failure modes introduced by data leakage, hallucinations, and model drift. The most resilient organizations align technology choices, operating processes, and team structures to each of these risks—proactively, not reactively.

Addressing Data Leakage

To minimize the risk of data leakage, it's essential to control how data enters and exits the model pipeline, and to ensure third-party providers uphold strong security boundaries. For teams working with vendor APIs, services like AWS Bedrock, Azure OpenAI, and Google Vertex AI offer enterprise-grade controls, including network isolation, encryption, logging, and model invocation without exposing your data to public training workflows. These platforms offer a strong alternative to public-facing APIs when privacy or compliance is a concern.

Internally, implement a prompt sanitization layer to automatically redact sensitive inputs and monitor model responses for potential leakage. All prompt-response activity should be logged in a secure and auditable manner. Teams fine-tuning models on proprietary data should perform data classification and content reviews before training—ideally in collaboration with legal, security, or compliance functions.

Policy also plays a critical role. Clear internal usage guidelines should define what types of data are permitted in prompts, under what conditions models can be used, and which use cases require special review. Non-technical teams using LLMs for content generation, analysis, or summarization should be trained on what constitutes sensitive information and how to flag concerns.

To reduce data leakage risk:

  • Prefer secure platforms like Bedrock, Azure OpenAI, or Vertex AI
  • Sanitize and log prompt-response interactions
  • Define and enforce internal usage policies for prompts and training data

Managing Hallucinations

When factual accuracy matters, grounding LLMs in trusted sources becomes essential. Retrieval-Augmented Generation (RAG) offers a practical strategy by retrieving relevant documents at inference time and feeding them into the model’s context window. Research has shown that RAG can reduce hallucinations and increase factual precision [4]. That said, RAG isn’t trivial to implement, it requires well-maintained document stores, efficient embedding-based retrieval, and domain-specific tuning to avoid surfacing irrelevant or outdated material.

Complementary to grounding is response validation, which can range from simple heuristics to sophisticated automated evaluation. Teams with basic LLM experience can start with lightweight approaches such as golden prompt tests—predefined prompts with expected outputs used to catch regressions—as well as format or keyword checks to ensure responses follow expected patterns. Embedding similarity can also be a low-effort way to flag off-topic or semantically irrelevant answers by comparing them to reference responses.

As LLM adoption matures, teams may layer in more structured validation techniques. Prompt evaluation playbooks provide reusable templates for stress-testing outputs, and reference-based comparisons help identify divergence from ground truth when available. For organizations exploring scalable validation, LLM-as-a-judge—a technique where one model evaluates the outputs of another—can help assess quality, coherence, or factual alignment at scale. However, this approach requires thoughtful prompt design, careful model selection, and human oversight to ensure it does not reinforce bias or overfit to superficial features of good responses.

To ensure these validation techniques are effective in practice, they should be embedded into structured review processes, not left to ad hoc user feedback. In smaller teams, regular review of model outputs can be handled by engineers or product owners using shared test cases or lightweight dashboards. As LLM usage scales across the organization, it’s worth formalizing this process. Larger teams may benefit from dedicated LLM QA functions embedded within product, analytics, or security teams, responsible for prompt testing, hallucination detection, and continuous evaluation. Regardless of size, the key is to treat hallucination monitoring as a first-class quality signal, one that evolves alongside the models and their applications.

To reduce hallucination risk:

  • Use RAG selectively to ground responses in trusted data
  • Apply lightweight techniques such as golden prompts, heuristic rules, or embedding checks when operating without dedicated ML teams
  • Consider validation workflows, including LLM-as-a-judge or structured scoring
  • Integrate hallucination review into release and monitoring processes

Containing Model Drift

Model drift often goes unnoticed until behavior breaks a workflow or causes confusion. To guard against this, LLM deployments should be treated as evolving systems, not set-and-forget components.

When working with vendor APIs, always pin to specific model versions and track changelogs. Even when major versions are announced, smaller behavioral updates may go undocumented. For internal or fine-tuned models, establish scheduled retraining based on evolving data or organizational needs, and validate model performance against a benchmark set of prompts.

It’s critical to monitor changes over time. As described in the previous section, golden prompts and structured output comparison are useful tools, not just for hallucination detection, but also for spotting behavioral drift over time. The key difference is recognizing trends and shifts, rather than isolated errors. Any unexpected drift should trigger review, rollback, or retraining.

Model ownership should be clearly assigned. In smaller organizations, this responsibility may sit with an engineering lead or architect. In larger settings, LLM behavior can be overseen by a cross-functional AI governance group with visibility across data, product, and compliance.

To reduce model drift risk:

  • Pin and monitor model versions; track vendor updates
  • Evaluate behavior regularly using benchmark prompts
  • Assign clear ownership of model performance and retraining cycles

Conclusion

LLMs have moved beyond experimentation. They're powering product features, customer support tools, internal copilots, and decision-support systems across the enterprise. But as this technology becomes embedded in core business workflows, so too do its risks.

Hallucinations, data leakage, and model drift aren’t theoretical, they’re operational realities. And while these risks can’t be eliminated, they can be understood, monitored, and mitigated with the right combination of tools, processes, and organizational habits.

The most successful leaders won't be those who avoid LLMs out of fear, or those who embrace them blindly. Instead, they’ll be the ones who treat LLMs like any other high-impact system: with clear expectations, strong controls, and an evolving strategy for trust and performance.

If you're deploying LLMs, or thinking about it, now is the time to evaluate your exposure, build review mechanisms, and set the right guardrails. If you’re looking for help designing those systems, defining internal policy, or aligning engineering and product teams around responsible adoption, AKF Partners can help! 

Sources

[1] M. Nasr, J. Rando, N. Carlini, J. Hayase, M. Jagielski, A. F. Cooper, D. Ippolito, C. A. Choquette-Choo, F. Tram, and K. Le, "Scalable Extraction of Training Data from Aligned, Production Language Models". The Thirteenth International Conference on Learning Representations, 2025 (https://openreview.net/forum?i...)

[2] "OmniGPT Leak Highlights Security Risks in AI Tools". ZenoxAI 2025 (https://zenox.ai/en/omnigpt-le...)

[3] M. Chelli, J. Descamps , V. Lavoué , C. Trojani , M. Azar, M. Deckert, J. L. Raynier , G. Clowez , P. Boileau , and C. Ruetsch-Chelli. "Hallucination Rates and Reference Accuracy of ChatGPT and Bard for Systematic Reviews: Comparative Analysis". J Med Internet Res. 2024. doi: 10.2196/53164. PMID: 38776130; PMCID: PMC11153973. (https://pubmed.ncbi.nlm.nih.go...)

[4] Y. Zhang, Y. Li, L. Cui, D. Cai , L. Liu, T. Fu , X. Huang , E. Zhao , Y. Zhang , Y. Chen, L. Wang, A. T. Luu , W. Bi, F. Shi, and S Shi. "Siren’s Song in the AI Ocean:
A Survey on Hallucination in Large Language Models". arXiv preprint arXiv:2309.01219 (2023) (https://arxiv.org/abs/2309.012...)