Autonomous and agent-based AI systems are becoming increasingly relevant for both Norwegian and international organizations.

In contrast to more familiar AI agents, which typically perform simple and often generic tasks and require human review of their outputs, agentic AI consists of a set of AI agents with highly specific responsibilities.

These agents collaborate to achieve more complex outcomes, often with fewer errors and a certain degree of autonomy.

Tesla Autopilot is an example of multiple agents working together by collecting and processing information from various systems to determine optimal routes, traffic conditions, and related inputs.

In practice, this is still happening in a relatively limited manner, most often tied to small and clearly defined tasks. This can be seen in solutions such as Copilot or Access Review agents from Microsoft. These agents still require significant maintenance, experimentation, and continuous oversight, and automated actions are largely limited to reporting or traditional data collection.

Nevertheless, this development represents an important shift and introduces risks that organizations must understand before adopting the technology.

When AI moves from support to action

Historically, AI (Artificial Intelligence) has primarily served as a tool for analysis, reporting, and task automation. Agent-based systems operate differently. They can interact with multiple systems simultaneously, retrieve data from various sources, generate decision recommendations, and execute actions that affect both internal processes and customer interactions.

For organizations, this means that control structures must be reconsidered. Actions that previously required human judgment may now occur autonomously. The consequences of incorrect decisions can be significant—technically, legally, and in terms of organizational reputation.

Understanding this new type of actor is therefore critical before the technology is put into use.

Security and governance challenges

Autonomous agents introduce several categories of risk. Data integrity and data leakage are among the most immediate concerns. When AI agents have access to sensitive information, there is a risk of unintended exposure. Systems that combine information from multiple sources may reveal patterns or insights that the organization does not fully control.

Another challenge is unforeseen actions. Even minor errors in goal definition or missing context can result in actions that modify data, system configurations, or user experiences. Traditional security mechanisms are often designed for humans and do not always adequately cover autonomous digital actors.

This creates a gap in governance and accountability that must be addressed through new processes and controls.

Segmentation as a risk-reduction method

Experience from organizations that have achieved ROI and measurable benefits from AI shows that segmenting both security mechanisms and workflows is effective.

When AI is assigned a single, well-defined, and transparent task—such as summarizing a dataset—it becomes much easier to verify intermediate results and detect errors. In contrast, complex agents that perform many micro-tasks on the way to a comprehensive final output can make it difficult to understand what actually occurred during execution.

Lack of visibility into an agent’s intermediate steps increases both operational risk and the demands placed on control structures.

How organizations can prepare

Organizations considering agent-based AI should first establish clear boundaries for what the systems are allowed to do, which data they can access, and how their actions are monitored. Logging and real-time monitoring enable early detection of deviations and are essential for reducing risk.

The principle of least privilege should also apply to digital agents. Limiting system access to what is strictly necessary for the task minimizes potential damage resulting from erroneous actions.

Before agents are deployed into production, they should be tested and simulated in realistic scenarios to uncover vulnerabilities and unintended consequences.

It is also important to establish collaboration processes across the organization, including IT, security, and leadership. This ensures that decisions made by agents can be traced and that procedures exist for handling unexpected situations.

Responsible use and governance

Implementing agent-based AI is not only a technological matter, but also one of responsibility and transparency. Organizations must have clear routines for how decisions and actions are monitored and documented. This supports both compliance and trust—internally and externally.

Organizations that plan for and establish governance before adopting autonomous AI agents are better positioned to realize the benefits. Proper implementation enables efficiency and innovation while managing risks in a systematic manner.

For organizations lacking internal security expertise, external security advisors—such as CISO capabilities or CISO-as-a-Service—can help ensure that governance and oversight are in place from day one.

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