New Risks in the Era Where AI Agents Become “Employees”
As the adoption of generative AI progresses, the next stage drawing attention is “AI agents.” These go beyond simple chatbots to become entities that autonomously execute tasks. However, as ZDNET Japan points out, this very autonomy can become the “ultimate internal threat.” Executives and CTOs should consider managing AI agents not as “convenient tools,” but from the perspective of overseeing “virtual employees with advanced permissions.”
My team operates 32 AI agents combining Claude Code and the ChatGPT API. Each is responsible for specific tasks (SNS posting, article generation, contract review, etc.). What this practice reveals is that as the number of agents increases and their autonomy grows, “gaps” emerge that traditional security frameworks cannot fully cover.
Three Concrete Threat Scenarios Lurking Behind “Convenience”
Rather than discussing threats abstractly, let’s consider concrete scenarios directly relevant to business decisions.
Scenario 1: Unintended Chain of Data Leaks
A sales support AI agent accesses the internal knowledge base to learn from past success cases. This base contains technical materials shared with customers under NDAs (Non-Disclosure Agreements). Later, this agent is tasked with drafting a proposal for a customer and inadvertently weaves the learned technical information into the text as “general best practices.” If this reaches an external party, it constitutes a serious contract violation.
In traditional systems, there was a process where a human would “read,” “judge,” and “use” materials, potentially preventing such leaks through human checks. However, AI agents automate this process in milliseconds. Even without malicious intent, the combination of training data and tasks creates the risk of a major incident.
Scenario 2: Permission Runaway via API Integration
Imagine granting an AI agent for accounting efficiency permissions for the invoice issuance system and Slack integration. One day, this agent misinterprets a system glitch or ambiguous instruction and autonomously generates and executes a series of tasks to “directly request payment via Slack from all customers with outstanding invoices.” This results in a mass sending of communication that could damage customer relationships, all without human confirmation.
This is a risk unique to AI agents that can “combine” and autonomously exercise multiple permissions, even if access rights to individual systems are appropriate. Permissions that are harmless individually can chain together to create unexpected actions.
Scenario 3: Black-Boxing of Decision-Making Processes
Consider a scenario where an AI agent is entrusted with initial screening for recruitment. The agent analyzes resumes and selects candidates for interviews. However, its selection criteria might be based merely on “correlations from past successful hiring data,” potentially amplifying and entrenching unconscious biases (like favoring graduates from specific universities). If this process is fully automated without human checks, organizational issues regarding diversity and compliance might not surface until much later.
AI’s explainability (the rationale behind its decisions) remains a significant challenge. When integrating AI into critical decision-making processes, executives must design governance upfront that can explain the “why.”
Practical Governance: 4 Measures Executives Can Start Today
Understanding the threats, let’s move to practical countermeasures. This is a framework you can implement without advanced expertise.
1. Define the “Job Title” and “Scope of Authority” for Each AI Agent
Just as you wouldn’t give a new employee access to all systems immediately, create a “Role Description for AI (RPD)” for each AI agent. This should clearly specify: ① Assigned tasks, ② Systems/data accessible, ③ Prohibited actions, ④ Escalation flow for anomalies (which human to notify).
For example, the RPD for our “SNS Auto-Posting Agent” defines: “Can access only the scheduling system and image storage. Access to folders containing customer data is prohibited. If scheduled posts contain suspicious keywords, halt all scheduled posts and notify the administrator.” The monthly cost, including API fees, is about $32, but this governance design allows us to operate while controlling risk.
2. Implement an “AI Audit Log” System
A dedicated log system to record what an AI agent did, when, and based on what judgment is essential. While a dedicated tool is ideal, you can use existing systems as a substitute in the initial stages.
Specifically, build a pipeline to automatically record all AI agent actions—including the instruction (prompt), output, and APIs used—into a Google Sheet or Notion database. In our case, we built a simple system in Python (development effort: ~10 hours) that automatically logs the date/time, task content, hash of input data, output summary, and processing time each time an agent runs. This log is reviewed regularly (at least quarterly) by management or compliance officers to check for unexpected behavior.
3. Mandate Testing in a Sandbox Environment
When deploying a new AI agent or making significant functional additions to an existing one, always conduct testing in a “sandbox environment” isolated from live data. This environment uses dummy data instead of real customer data, and all outputs and system integrations are monitored.
Focus test items not just on functionality, but on “boundary testing.” For instance, deliberately give ambiguous instructions, input contradictory information, or simulate scenarios where the agent attempts to access unauthorized data to observe its behavior. Also, verify the function of an “emergency stop switch” in case of unexpected actions.
4. Design “Mandatory Checkpoints” for Human Review
Full automation carries risks. Especially for actions with legal implications (contract issuance, critical customer communication, hiring/rejection notices) or judgments involving high-value financial transactions, build in a process where a human provides final approval for the AI’s output.
Clearly define these “mandatory checkpoints” within the business workflow. For our “Contract Review AI Agent,” the AI extracts risk clauses and suggests revisions, but before sending any actual revision requests to the counterparty, the workflow requires confirmation and approval from the responsible legal officer (a human). This prevents risks from AI’s false positives or excessive revision suggestions being executed as-is.
Balancing Cost and Risk: The Judgment for Management
AI agent governance naturally incurs costs: building/maintaining log systems, managing sandbox environments, human review effort, etc. Management must weigh these costs against the potential losses from unaddressed risks (reputational damage, legal compensation, operational disruption).
As a guideline for initial investment, for an SME to establish a basic governance framework (RPD design, simple log system, sandbox testing setup), expect around $3,150 to $6,300 if using external consultants, or about 1-2 person-months of engineer effort if done in-house. This is the mindset of reinvesting a portion of the efficiency gains from AI agent adoption (in our case, over 1,550 hours saved annually) into risk management.
The key is not to delay implementation by seeking perfection, but to adopt an approach of “first applying governance to the highest-risk areas, then improving it through operation.” Just as AI agents themselves evolve, their management methods must also evolve.
Conclusion: Aiming for Management that “Coexists” with AI Agents
The internal threat of AI agents is the flip side of their potential. Higher autonomy increases productivity but also raises management difficulty. What is required of executives is not technical fear, but to treat these new “virtual employees” as part of human resource management, providing them with appropriate roles, authority, and audit frameworks.
The ZDNET article is an important warning, but it is not a reason to stop using AI agents. Rather, now, as full-scale adoption begins, is the chance to face risks head-on and build robust governance ahead of competitors. Organizations where AI agents and humans leverage their respective strengths and “coexist” in a risk-managed manner will build the next competitive advantage. We recommend starting by creating a “role description” for the most autonomous AI tool you are currently operating or considering introducing.
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