Legal and compliance teams and general counsel are rightly interested in the potential of artificial intelligence. The technology can accelerate document review, support regulatory checks, and surface risk patterns at scale.
However, alongside these advantages is a huge risk posed by AI hallucinations. What seems to be confident outputs may actually be incorrect. This introduces material risk in high-stakes environments that require all information to be accurate and defensible.
However, the question today is not whether these teams and leads should use AI. It is more about how to use it safely.
AI adoption across legal and compliance functions continues to grow. A 2024 survey by Deloitte found that 95% of chief legal officers have used AI in their functions, while 93% saw AI’s potential value in the industry within the next year. Teams are experimenting with large language models for tasks like contract summarisation, regulatory research, compliance assessments, and policy guidance.
Early results often look encouraging. However, hallucinations remain a persistent limitation of these models.
In low-risk contexts such as brainstorming, internal drafts, or creative tasks, hallucinations are inconvenient but manageable. In legal operations, compliance, and regulatory workflows, they become liabilities:
In each case, the output appears credible and the conclusions reasonable, but the information was wrong. By the time the issue surfaced, the output had already influenced decisions and potentially exposed the organization. This could have been avoided with proper governance overseeing how AI produced the output.
Not all AI use cases carry the same risk profile. An occasional error by a drafting assistant can be corrected. A mistake in a compliance or regulatory workflow often cannot. That’s why firms like Foley & Lardner LLP have built their FCPA compliance solution on Neota’s platform — to deliver consistent, auditable decision-making aligned with U.S. government standards.
Sensitive workflows operate under a higher standard similar to this. These are processes that inform regulatory decisions, classify risk, guide legal strategy, or establish organisational liability. In these contexts, the output must be objectively factual and accurate.
Examples include:
The stakes are well understood, as hallucinations can result in:
At some point, an auditor, regulator, or opposing counsel will ask questions: How was this decision made? What information informed it? Who reviewed it? Can the process be reproduced?
If the only answer is along the lines of, “AI generated the output,” the organization may be exposed for its lack of documented controls.
Many organizations encounter risk not through deliberate shortcuts, but through incremental adoption. AI tools are introduced to solve immediate problems, often outside structured workflows. One team uses a general-purpose model to draft policy summaries. Another deploys a chatbot to answer compliance questions. A third integrates AI into a review process without defining approval gates or audit requirements.
The pattern is familiar:
This leads to a fundamental question: Who is responsible when the AI gets it wrong?
Without traceability and versioning, this can be difficult to answer. In a compliance review or audit, uncertainty is not an acceptable position.
Reducing hallucinations in sensitive workflows requires intentional design. AI does not have to be completely removed, but organisations must define how and where it operates so that risk is controlled.
Generative AI is well-suited to drafting, summarisation, and early-stage analysis where some variability is acceptable. Decisions that trigger material consequences should rely on deterministic logic. Rules-based pathways are predictable and auditable. An explicit decision rule can be tested. A generated classification cannot.
This is why Neota separates generative and deterministic components at the architectural level. Generative AI is used for drafting and surface-level analysis, where variability is acceptable. Meanwhile, classification decisions that trigger material consequences should run through explicit, rule-based pathways that can be tested, versioned, and reproduced for audit.
Hallucinations often occur because models generate plausible statements when they cannot find verified information. Retrieval-based approaches reduce this risk by limiting AI outputs to approved documents, databases, or internal knowledge sources. The system cannot cite information that it has not been provided.
AI cannot replace human oversight. Define escalation triggers for uncertainty or high-risk outputs. Require approval at specific decision points. In sensitive workflows, review should be mandatory.
Every step should be recorded:
These details ensure everything is defensible during audits, investigations, or regulatory review.
Ongoing monitoring helps identify drift and anomalies. If reviewers consistently override certain outputs, or if classifications begin to shift unexpectedly, those signals should prompt review and refinement.
