Artificial intelligence is rapidly embedding itself into legal workflows, but much of the conversation treats all use cases as if they carry the same level of risk, even if they do not. The more useful question is not whether AI works, but where it can be safely applied and where it cannot.
As AI becomes embedded in everyday business and legal operations, organizations are confronting a new expectation: simply disclosing AI use is no longer enough. A critical shift is taking place in the legal industry: transparency is no longer just about disclosure; it’s about comprehension.
The use of AI does not alter fundamental obligations of accuracy, reasonableness and accountability. The legal risk lies not in the existence of hallucinations but in the failure to govern and verify them.
A recent decision from the U.S. District Court for the Southern District of New York (SDNY), United States v. Heppner, has generated outsized commentary suggesting that the use of generative AI tools may jeopardize attorney-client privilege. A closer reading shows something far less dramatic.
Businesses subject to the CCPA now must conduct risk assessments for certain types of processing activities and, starting in 2028, must certify to California regulators that they completed the assessments.
The Verification-Value Paradox states that increases in efficiency from AI use “will be met by a correspondingly greater imperative to manually verify” the outputs. The result is that the net value of AI in many legal contexts may be negligible once verification is honestly accounted for. For low-stakes tasks, verification costs are light. For core legal work, verification costs are heavy. That’s the tension.
“Shadow AI” highlights a growing risk for firms that have yet to develop comprehensive governance strategies for artificial intelligence. In today’s rapidly evolving digital landscape, the question is not whether AI is present in your organization, but whether it is being managed responsibly.
Over the last decade, technology-assisted review (TAR) has become a preferred choice in the e-discovery toolkit. Now, as generative AI gains traction, legal teams face a new challenge: creating a technology stack that offers the best balance of efficiency, cost and usability.
While the amount of AI legislation introduced in various states is relatively limited, the scope of issues being legislated is quite broad. Despite the many uncertainties that remain to be clarified, there are actually many parallels between how data privacy laws took shape five years ago, and how AI legislation is developing today.
Lessons about artificial intelligence (AI), how our discourse about machines has gone awry, and how consulting traditional models can provide clarity — especially to thorny legal issues at the intersection of technology and IP law.
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