Agentic AI Risks and Responsibilities Every Legal and Privacy Team Should Know

What is Agentic AI? A Primer for Legal and Privacy Teams

As businesses move beyond simple AI assistants powered by large language models (LLMs), fully autonomous agents are entering the scene. These AI systems can plan, act, and adapt independently, without constant human oversight. For legal and privacy professionals, understanding the capabilities and risks of these agentic AI systems is becoming essential.

What is Agentic AI?

Agentic AI describes AI systems—often built with LLMs but not limited to them—that independently perform goal-driven actions across digital platforms. These agents can plan tasks, make decisions, adjust based on outcomes, and interact with software or systems with minimal or no human input.

They combine LLMs with features like memory, retrieval systems, APIs, and reasoning modules to operate semi-autonomously. Unlike chatbots limited to conversation, agentic AI can initiate actual workflows, modify records, and engage directly with enterprise applications, databases, or external platforms.

Examples include:

• An agent that processes incoming emails, categorizes requests, files tickets, and schedules responses autonomously.
• A healthcare agent that transcribes provider dictations, updates electronic health records, and drafts follow-up communications.
• A research agent that searches internal knowledge bases, summarizes findings, and suggests next steps in regulatory analysis.

These systems don’t just assist with writing or summarizing—they actively initiate workflows, alter records, make decisions, and connect with a variety of internal and external systems.

Key Issues for Legal and Privacy Teams

System Terms of Use Are Still Built for Humans

Most third-party platforms—cloud apps, SaaS tools, enterprise software, APIs—were created with human users in mind. Their terms of service often restrict or prohibit automated tools or autonomous agents from accessing or modifying data.

Takeaway: Review all system and licensing agreements carefully for automation restrictions. If you plan to deploy agentic AI, negotiate explicit permissions or update contracts to avoid breaches.

Liability Flows Through You

If an agent causes harm—such as deleting records, misusing credentials, or violating policies—your organization remains fully responsible. Existing contracts rarely cover AI acting autonomously on your behalf.

Takeaway: Treat these AI agents like high-privilege users. Clearly define their permitted actions and enforce accountability for their behavior.

Privacy Impacts Are Underexplored

Agentic AI can introduce new privacy risks. These systems may access sensitive data, combine multiple data sources, or make inferences that your current data processing agreements don’t address. Often, logging is insufficient, complicating audits and breach responses.

Takeaway: Classify agentic AI as data processors. Conduct data protection impact assessments, map data flows, restrict access scopes, and ensure all actions are logged and traceable.

Regulators Expect You to Control Agents’ Decisions and Data Processing

When AI agents make decisions affecting consumers, process personal data, or impact fairness and transparency, various laws apply. This includes the FTC Act, state-level unfair practices laws, privacy laws like GDPR and CCPA, and AI-specific regulations such as the Colorado AI Act and EU AI Act.

While the U.S. federal approach to AI regulation currently favors a lighter touch, pending legislation like the “One Big Beautiful Bill Act” (H.R. 1) could restrict state-level AI regulation for years.

Key enforcement risks include:

• AI agents making significant decisions without proper notice or privacy safeguards.
• Misleading consumers about whether decisions are human or AI-driven.
• Using sensitive data without appropriate consent or notice.
• Lack of accountability for outcomes arising from automated systems.

Takeaway: If your agent interacts with consumer data or influences key decisions, treat it as a high-risk algorithm. Implement monitoring, regular testing, and transparent disclosure.

Audit and Explainability Gaps Are Real

Agentic AI is goal-directed rather than rule-bound, making it hard to explain its actions. Many enterprise systems do not distinguish between human and agent activity, and logs may be incomplete or insufficient.

Takeaway: Apply audit and observability controls beyond just the endpoint systems the agent touches. Ensure mechanisms for rollbacks, alerts, and human overrides are in place.

No One Owns This Yet

Agentic AI crosses legal, privacy, InfoSec, and engineering boundaries. Without clear ownership, these tools risk deployment without proper legal oversight.

Takeaway: Establish simple policies for agent approval, access control, and post-deployment reviews. Assign clear responsibility to a designated individual.

The Bottom Line

Agentic AI is no longer theoretical. It’s quietly entering business operations through pilots, prototypes, and embedded platform tools. Legal and privacy teams must step in early, set boundaries, and guide responsible use to manage risks effectively.

For legal professionals seeking to deepen their understanding of AI technologies and compliance strategies, resources such as Complete AI Training's courses for legal professionals can offer practical guidance.