Insider Risk Just Multiplied
In a world where agentic AI is gaining agency, insider risk is growing at a speed you cannot control.
When the
Greatest Threat Acts on Your Behalf
Insider risk has long been understood as a human problem. An employee makes a mistake. A consultant is granted overly broad access. A staff member misuses privileges or takes information elsewhere. The risk has been associated with intent, loyalty, and training.
With the emergence of agentic artificial intelligence, organizations have gained a new type of insider. It is not a human with motives of its own, but an autonomous actor capable of doing things on behalf of the organization. It can read, write, analyze, decide, and initiate actions in the organization’s name. It can be connected to systems, data, and processes. At the same time, its judgment is bounded by how it has been trained, configured, and supervised.
The Risk Landscape Changes
According to IBM’s Cost of a Data Breach Report, a significant share of data breaches can be traced back to internal actors, whether through error, misuse, or compromised accounts. The Ponemon Institute estimates that it takes an average of 81 days to contain an insider incident. When autonomous systems can operate continuously and at high speed, without pause and without doubt, both the scope and the consequences of such incidents may increase.
When the System Gains Agency
Agentic AI differs from traditional support systems in that it does not merely provide recommendations but may also execute actions. It can retrieve information from multiple sources, interpret it in context, and then update systems, send messages, initiate processes, or make decisions within defined parameters. This offers significant efficiency potential. Tasks that previously required manual follow-up can be completed faster and more consistently. At the same time, the system may operate with the organization’s identity, delegated authority, and access rights. When an AI agent approves a transaction or shares information, it often does so on the basis of credentials, permissions, or workflows assigned by an employee or by the organization itself.
The agency is real. Therefore, the risk is real.
Traditional security models are built on the assumption that actions are performed by humans. One can ask questions, provide training, and hold someone accountable. One can also expect an experienced employee to pause if something appears unusual or disproportionate. An AI agent does not necessarily have the same intuitive brake as an experienced
employee. Even with strong training and well-designed guardrails, it may not reliably understand what is strategically sensitive in a broader sense. It may not always distinguish between what is technically possible and what is commercially sound. Nor does it consistently assess whether an action could damage reputation, conflict with culture, or create unintended ripple effects. In practice, such agents typically act through the credentials, permissions, and workflows
delegated to them by people. That makes accountability especially important: when an agent acts, responsibility does not disappear. It remains tied to the employees, leaders, and governance structures that configured, authorized, and oversee its use.
When delegated permissions are extensive, the scope of action may also become extensive. If control mechanisms are not adapted to this new reality, the system may execute actions that are formally permitted but still create significant business risk. Data may become accessible to more people than necessary. Processes may be initiated
without a holistic assessment. Accountability may become blurred if the organization has not clearly defined who is responsible when the system acts.
Microsoft’s Digital Defense Report shows that the misuse of legitimate identities and access privileges plays a central role in today’s threat landscape. When identity can also represent an autonomous agent operating on delegated authority, this challenge becomes even more complex.
The Limits of Existing Security Models
Many organizations attempt to respond to this development with more logging, additional alerts, and stricter policies. Such measures are necessary, but they are not sufficient. Identity is no longer uniquely tied to a human being. An AI agent may operate under the same identity context or delegated authority as an executive or a specialist.
The principle of least privilege is challenged when systems must handle varied tasks and adapt to new situations. Pre-approvals do not always account for the dynamics that arise when a system acts in real time.
Agentic AI operates in the space between what is permitted and what is prudent. In this space, organizations must establish visibility, governance, and corrective mechanisms.
It is not enough to ask whether the technology is secure in isolation. The relevant question is whether the organization has established structures that ensure the technology operates within acceptable risk, even when it acts quickly and independently.
Leadership in a New Reality
This issue cannot be reduced to a technical detail. It concerns the organization’s governance model and its understanding of risk.
Organizations that handle agentic AI with maturity treat AI agents as digital employees. They assign them a clear identity, define roles and responsibilities, and manage their lifecycle in the same way as they would for people. Permissions are granted based on specific needs and are reassessed regularly.
In addition, continuous insight into how agents behave over time is established. Emphasis is placed on understanding actual behavior, not only formal access rights. Deviations are identified and addressed promptly. Accountability is clarified so that it is clear who owns
the risk when the system acts.
Before the technology is scaled broadly across the organization, it is anchored in clear governance frameworks. These frameworks must describe risk tolerance, requirements for transparency, and mechanisms for correction. Without such anchoring, efficiency gains can quickly translate into accumulated operational risk.
A control question can reveal the maturity of the approach: Would you grant a new employee the same level of access without training, supervision, and oversight? If the answer is no, you should be equally cautious about granting comparable agency to an AI agent.
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