### AI Agent Access Controls Need a New Model

AI agent access controls are becoming a priority as enterprises move from experimentation to deployment. In this Techstrong.ai Leadership Insights episode, Mike Vizard and Tony Grout, Chief Product and Technology Officer at M-Files, examine why unmanaged agents can create unexpected security, data, and workflow risks.

Grout compares an AI agent to a smart new employee who has not yet learned the business. The agent may have broad intelligence, but it does not understand context, priorities, or policy boundaries unless those limits are designed into the environment. That makes access control, training, and supervision essential.

### Legacy Data Problems Surface Quickly

The conversation explores how AI agents expose hidden complexity across IT environments. Agents can discover relationships, dependencies, and backend systems that humans may overlook. That weakens older approaches that relied on security through obscurity.

AI agent access controls must also account for long-standing data management issues. Grout notes that concepts such as master data management and single sources of truth are becoming relevant again. If organizations do not address data quality and ownership first, agent deployments can reach the “trough of despair” quickly.

### Deterministic Workflows Still Matter

Grout also explains why deterministic workflows remain important. Many business processes are designed to run the same way every time. AI agents are probabilistic, so they may not repeat the same steps twice. Enterprises need to decide where creativity is useful and where standard operating procedures should remain in control.

One practical approach is to combine deterministic workflow logic with non-deterministic AI actions. The workflow can define the guardrails, while the agent performs a specific activity inside those limits. The system can then check whether the action met the required criteria.

### Accountability, Auditability, and Oversight

The episode also covers agent-to-agent conflict, arbitration layers, auditability, and rogue agent behavior. As companies deploy more agents, they will need automated permission models that can keep pace with agentic speed. They may also need anomaly detection and human escalation when risk increases.

For IT and security leaders, the message is clear. AI agent access controls should be planned before agents spread across the enterprise. Strong permissions, audit trails, orchestration, and human oversight will help organizations scale automation without losing control.