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P E E R T O P E E R M A G A Z I N E · S P R I N G 2 0 2 6 107 Currently, no technology is shifting that landscape faster than artificial intelligence. But the AI we are dealing with today is fundamentally different from the AI of last year. We have moved from generative AI that simply answers prompts to agentic AI that independently plans, decides, and takes action toward a goal. As autonomous systems enter legal workflows, classic security controls are necessary but no longer sufficient. Legal and ediscovery teams require stringent agentic AI governance and data provenance standards that make outcomes traceable, auditable, and defensible in litigation. This is not an abstract concern. It is a structural one. WHY STANDARD RISK MANAGEMENT IS OUTDATED The standard enterprise risk management approach most organizations rely on today works perfectly fine for classic software, and even for earlier AI tools that functioned purely as chatbots. However, this framework breaks down when the system becomes agentic. Unlike traditional generative AI, which is reactive, agentic AI is proactive. It can invoke tools, move data across systems, and orchestrate complex workflows without continuous human prompting. For legal and ediscovery teams, this autonomous action uniquely exposes the organization to risk, including privilege waiver, sanctions, and compromised discovery obligations. When an AI system can autonomously trigger actions, it effectively becomes a non-human actor and a potential insider threat. Defensibility is not something that is built into the AI model itself. It is built into the architecture surrounding it. Human-in-the- Loop oversight is not a speed bump slowing down innovation. It is the accountability mechanism that makes autonomy usable and safe for corporate legal departments. The shift from assistance to autonomy is precisely where legal leaders must pause and apply a disciplined evaluation framework. A PRACTICAL FRAMEWORK FOR EVALUATING AI IN REGULATED WORKFLOWS Before integrating AI into litigation or regulated processes, legal leaders should ask five critical questions: 1. Is this tool the system of record? If not, how are authoritative records maintained? 2. Does it preserve the chain of custody? Can data movement be traced, changed, and accessed? Human-in-the-Loop oversight is not a speed bump slowing down innovation. It is the accountability mechanism that makes autonomy usable and safe for corporate legal departments. 3. Are audit logs immutable and complete? Would they withstand external scrutiny? 4. Can outputs be reproduced? If challenged, can the result that was generated be demonstrated? 5. Is human signoff required at critical decision points in the workflow? Are regulated decisions formally approved and documented? If the answer to these questions is unclear, the AI tool may be appropriate for productivity support, but not for regulated decision workflows. This framework is not anti-innovation. It is a recognition that regulated workflows demand architectural guarantees and not feature lists. The legal industry does not lack enthusiasm for AI; it lacks tolerance for unprovable decisions.

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