Extract from Exterro’s article “The Hard Truth: GenAI Isn’t Built for Legal, Privacy, or Compliance Workflows”
Generative AI has been hailed as a breakthrough technology for knowledge work. In the last two years, systems like GPT-4, Claude, and Gemini have shown that they can draft emails, summarize documents, and even mimic legal reasoning in ways that seemed impossible a decade ago. For many industries, that’s more than enough. For legal, privacy, and compliance professionals, though, the story is very different.
These domains do not operate on “good enough.” They are governed by rules of evidence, regulatory frameworks, and professional obligations that demand precision, transparency, and defensibility. And this is exactly where GenAI, for all its promise, begins to unravel.
The Problem with Black Boxes
At the core of large language models lies a simple truth: they were not designed for environments where accountability matters. LLMs generate text by predicting the most statistically likely sequence of words, not by reasoning or citing evidence. Their outputs may read with confidence, but beneath the surface they are opaque, non-deterministic, and impossible to audit.
That’s not a problem if you’re writing marketing copy or brainstorming creative ideas. It becomes a critical flaw when the output is a contract summary, a breach notification, or a privilege determination. In these contexts, professionals must be able to explain not only what a conclusion is, but why it was reached. A machine that can’t show its work is worse than useless—it’s a liability.
