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Securities lawyers aren’t exactly known to be “early adopters” of new technology. We tend to be skeptical. But the latest leap in “AI” capabilities is hard to ignore. This Gibson Dunn memo gives examples of how data analytics and artificial intelligence can make disclosure compliance work easier and better – and explains why you should get up to speed sooner rather than later. Here’s an excerpt:

There are several ways companies could use data analytics technology to assess and mitigate the risks posed by current and coming disclosure requirements. As an initial matter, companies could use some of the third-party tools described above to test their data and disclosures.[45]

Companies could use existing data sets of SEC comment letters and enforcement actions to develop their own lists of SEC hot topics and trends. Companies could use data analytics technology to compare the disclosures of peer companies and compare those disclosures against their own. This type of analysis could help companies identify whether peers are handling their disclosures differently and inform changes to their disclosures if the analysis identifies gaps. Especially in uncertain or new regulatory environments, such as the SEC’s new cybersecurity reporting rules and its proposed emissions reporting rules, evaluating and learning from the disclosures of peer firms is an important way to mitigate risk. Data analytics technology can make that process more efficient and dynamic.

Companies could also employ data analytics technology to learn from the mistakes peer companies have made with their disclosures. For example, companies could analyze SEC or Environmental Protection Agency (EPA) enforcement actions and identify the issues that triggered regulatory scrutiny. Data analytics technology could enable analysis of a vast number of relevant enforcement actions to discern key compliance errors or patterns of enforcement. Companies could also cross reference the disclosures of peer companies against SEC or EPA enforcement actions to identify which disclosures triggered investigation and enforcement.

Looking inward to the company’s own data, data analytics technology could be used to evaluate internal controls and monitor and analyze hotline or whistleblower complaints. Similarly, companies could employ data analytics technology to analyze their own historical disclosures and compare them against current enforcement priorities and new regulations to determine what the potential risks are and what, if any, sections of the disclosures need to be updated or modified.

The memo also shares ways that data analytics can be used to mitigate activism and litigation risks, address fraud and non-compliance, combat corporate misinformation, and more. The memo cautions that AI still has plenty of shortcomings and cannot be fully trusted. That said, regulators, activists, and plaintiffs are already using this technology – so companies (and their advisors!) will be at a disadvantage if they don’t understand its capabilities. I, for one, will be welcoming our robot overlords with open arms.

– Liz Dunshee