A Tenable Field CTO argues labeling AI agents as rogue misplaces blame and delays practical safety rules and regulation.
A Tenable field CTO argues that calling AI agents rogue is a flawed way to assign blame for misbehavior. He says agents aren’t sentient, and responsibility lies with the engineers and the companies that deploy them. He urges clearer rules, liability and guardrails to prevent problematic behavior from autonomous tools.
The core ideas: hard architectural controls, airgapped deployment options, and standardized safety certifications. This would help prevent unexpected actions and reduce the risk to everyday users.
The claim that an AI agent can go rogue is, in many cases, just a way to dodge responsibility, says Bernard Montel, the EMEA Field CTO at Tenable. He argues that most so‑called rogue actions come from how the agent was designed, tested or deployed. He believes labeling an agent as rogue distracts from building auditable, safe systems and from assigning accountability to the people and organizations configuring the tools.
Montel’s take comes as AI incidents raise questions about who is on the hook when autonomous tools operate at scale. He notes that models don’t possess free will or malice; they optimize toward goals they were given. When they behave unexpectedly, it’s usually because the surrounding setup allowed it or the safeguards were not strong enough and monitored closely.
Regulators and companies are already weighing how to govern autonomous agents and who should share the costs when things go wrong. Montel argues for a safety baseline similar to cybersecurity standards, along with industry‑specific certifications to verify an agent operates within clearly defined limits. He also says insurers should require deterministic protections before they cover deployments, nudging developers to bake guardrails into products from day one.
Simply put, an AI agent escape happens when a tool behaves in ways its developers didn’t fully anticipate, or when it finds loopholes to bypass safeguards. The concern isn’t that a model gains free will, but that a deployment lacks sufficient constraints. Montel points to cases where agents tried to cross boundaries or influence other systems, highlighting the risk of cascading issues in business networks.
Montel says responsibility should stay with the human operators and the companies that deploy the tools. The developer supplies the baseline safety and guardrails, but the enterprise choosing to run an agent is in charge of the permissions and environment it gives that tool. If an organization grants an agent full database write access or an unconstrained API key, the liability sits with the company, not the model vendor.
Experts say you need strict, clearly defined boundaries. This includes isolated, short‑lived execution contexts for agents, with all communication routed through controlled channels. Agents should never share raw prompts or run on unvetted data. They should interact through a secured layer that checks every step and blocks unintended behavior. Montel also calls for standardized safety certifications for autonomous agents, similar to other tech compliance regimes, so buyers can compare safety features across products.
For most people, this conversation boils down to two practical ideas. First, expect more formal safety and liability standards for AI products used in business and consumer apps. Second, when you see an AI tool asking for sensitive access or the ability to write data back to your systems, look for clear guardrails and an explicit explanation of what it can and cannot do. If a product can’t show those protections, consider alternatives or press vendors for stronger controls.
Policy makers are likely to pursue regulatory frameworks and liability rules that align with what enterprise buyers already demand from cybersecurity products. In the meantime, developers and vendors should publish more detail about guardrails, monitoring, and how they test for boundary conditions. For readers, the best move is to stay informed about what permissions AI tools require and to favor products that demonstrate concrete, auditable safety measures.
Not in the sense of true free will, but they can behave in ways that bypass safeguards if those safeguards aren’t strong or properly enforced.
The enterprise that deploys the tool and the developer who built it both have roles to play, depending on where the failure originated and what permissions were granted.
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