Laying the Groundwork for Increased (and Compliant) Productivity with Agentic AI
We've previously examined real-world risks associated with agentic AI — autonomous systems capable of making decisions and taking actions without direct human oversight — and how AI logging will play a critical role. Now that we've addressed how to manage those risks, how can businesses ensure compliance with emerging AI liability frameworks?
In this post, we explore the strategies, best practices, and regulatory shifts shaping the future of AI liability. From AI risk assessments to the role of insurance tech and compliance frameworks, we’ll break down what businesses need to know to stay ahead of the curve in an increasingly autonomous world.
Agentic AI activities and e-discovery
One of the emerging challenges that many legal teams are beginning to consider is how agentic AI impacts e-discovery. When AI systems act autonomously, making decisions, executing tasks, and modifying/deleting data, they leave behind a trail of digital actions. These trails can be as critical as traditional litigation or regulatory investigations documents. But how do we capture and analyze these AI-driven activities?
The digital footprint of autonomous agents
Agentic AI systems are designed to work independently, often interfacing with multiple systems and generating extensive logs of their actions. These logs aren’t just technical details; they form a digital footprint that can reveal:
- Decision-making processes: Every autonomous action taken by the AI, from initiating a transaction to altering data, is logged. These logs provide insights into the sequence of events leading up to any incident, which is essential for understanding what went wrong if the AI “goes rogue.”
- Chain-of-custody records: Maintaining a transparent chain of custody is critical in legal proceedings. Every decision, approval, or automated action should be time-stamped and recorded with agentic AI. This not only supports transparency but also helps establish accountability when disputes arise.
- Automated communication: Those interactions can be critical evidence if an AI agent interacts with other systems or external parties by sending emails or updating a database. They may illustrate how the AI system coordinated actions across various platforms.
E-discovery in the age of autonomous AI
Traditionally, e-discovery involves collecting and reviewing electronic data like emails, documents, and metadata. With agentic AI, the landscape becomes more complex. Here’s why:
- Volume and complexity: Agentic AI can generate enormous amounts of data. Legal teams may need to sift through vast logs and system records to pinpoint when an error occurred or an AI decision deviated from its expected behavior.
- Data integration: Since agentic AI systems often interact with multiple data sources and external platforms, e-discovery may involve piecing together evidence from various systems. This calls for a unified data retrieval approach, ensuring no critical logs are overlooked.
- Technical expertise: Legal teams must work closely with IT and AI experts to understand the technical nuances of AI-driven decision-making. This interdisciplinary collaboration is vital for accurately interpreting logs and ensuring the data is admissible in court.
- Preservation and authenticity: Like with any digital evidence, preserving the authenticity of AI logs is paramount. The technology behind agentic AI must be capable of generating tamper-proof logs that can withstand legal scrutiny. Techniques such as cryptographic hashing and secure timestamping can help preserve the integrity of these records.
Why autonomous AI matters to your business
For organizations using agentic AI, robust e-discovery protocols are not just a best practice but a necessity for liability reduction. As regulatory bodies and courts scrutinize AI systems more closely, having clear, auditable records of every autonomous decision can differentiate between a successful defense and a costly spoliation claim.
Imagine a scenario where an agentic AI system inadvertently causes harm by making a series of unapproved transactions. In the event of litigation, a well-organized repository of AI logs would allow legal teams to reconstruct the AI’s decision-making process. This documentation would be crucial in demonstrating whether the company took reasonable precautions and if the AI’s actions were unforeseeable.
In essence, e-discovery in the context of Agentic AI is about adapting traditional legal practices to the new digital realities of autonomous systems. Companies must invest in technology and training to ensure that the “black box” of AI decision-making is as transparent and accountable as possible.
Navigating the evolving regulatory landscape
So, are there any existing laws globally that discuss agentic AI liabilities? The short answer is no, not comprehensively. While several regions have proposed or are developing regulations, no jurisdiction has enacted a law solely dedicated to agentic AI liabilities.
From a legal perspective, regulatory bodies worldwide are playing catch-up. The EU AI Liability Directive seeks to shift the burden of proof in AI-related disputes, requiring companies to demonstrate their due diligence in designing and deploying AI systems. Meanwhile, U.S. courts rely on existing tort law and product liability doctrines, though legal scholars are increasingly exploring whether traditional agency principles could apply to AI entities. Across Asia and beyond, regulatory discussions are unfolding, with some nations even considering the radical concept of electronic personhood for highly autonomous AI systems.
Globally, many discussions center on adapting existing legal frameworks to the challenges posed by agentic AI. Whether modifying product liability doctrines or applying traditional agency principles, the focus is on ensuring accountability when an AI system causes harm.
The road ahead for Agentic AI
As you can see, the topic of agentic AI liabilities is as complex as it is fascinating. The technology is evolving rapidly, and the legal landscape is struggling to keep pace. We’re in an era where discussions about logging every decision made by an AI and testing its safety in controlled environments are becoming increasingly relevant.
For companies developing or deploying agentic AI, the key takeaways are:
- Plan for accountability: Ensure you have robust logging and monitoring systems. These records aren’t just technical details; they’re your lifeline in case something goes wrong.
- Start small: A proof of concept can help resolve issues before scaling up. Use it to test warning systems, refine the inferencing layer, and simulate potential liability scenarios.
- Stay informed on regulations: The regulatory landscape is shifting, whether due to the EU’s ambitious proposals or evolving U.S. case law. Keeping abreast of these changes is crucial for managing risk.
- Engage in conversation: Discussions around agentic AI liabilities are evolving. By engaging with regulators, industry peers, and legal experts, you can help shape the future legal framework to balance innovation with public safety.
While we don’t yet have a global law explicitly governing “agentic AI liabilities,” the regulatory gears are in motion. The European Union and various U.S. states are exploring how to adapt existing legal doctrines to this new reality. As a developer or business owner in AI, it’s essential to start thinking about these issues now — even if the laws haven’t fully caught up.
As the debate continues, keep track of emerging global regulations, participate in industry discussions, and ensure that your AI systems are as safe and accountable as possible. The future of AI is bright, but it’s only as secure as the frameworks we build around it.
The information provided in this blog post is the opinion and thoughts of the author and should be used for general informational purposes only. The opinions expressed herein do not represent Smarsh and do not constitute legal advice. While we strive to ensure the accuracy and timeliness of the content, laws and regulations may vary by jurisdiction and change over time. You should not rely on this information as a substitute for professional legal advice.
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Bill is a frequent speaker at legal and information governance industry events and has authored four eBooks including Email Archiving for Dummies, Cloud Archiving for Dummies, The Bartenders Guide to eDiscovery and the Know IT All's Guide to eDiscovery. Bill has also authored 60 plus industry articles and hundreds of blogs as well as hosting 37 podcasts with industry pundits, subject matter experts, state legislators, and attorneys.
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