For the past five years, the narrative around Artificial Intelligence has been defined by human-in-the-loop assistance. We built copilots to help us write code, draft emails, and summarise documents. In this paradigm, the human remained the final arbiter. The liability stayed where it always was: with the human operator.
This paradigm is ending.
The next frontier is Agentic AI. These are systems designed to operate asynchronously, execute workflows, interact with third-party APIs, and make financial decisions on behalf of their operators. They are not copilots; they are synthetic employees.
When a human employee makes a catastrophic error—or maliciously overrides policy—the legal and financial frameworks of the modern world absorb the shock. We have professional indemnity insurance, directors and officers liability, and established legal precedents.
When an autonomous AI agent hallucinatedly spends your entire marketing budget, authorises a fraudulent refund, or leaks sensitive customer data via an indirect prompt injection attack... who pays?
Right now, the enterprise pays. This unquantifiable risk—which we call Rogue Agent Liability—is the single greatest bottleneck to the enterprise adoption of Agentic AI. CEOs are excited by the productivity gains, but their Risk and Compliance officers are terrified of the unbounded liability.
Large Language Models are fundamentally probabilistic. Their outputs are statistical guesses. You cannot mathematically guarantee that an LLM will never output a harmful command. Trying to solve AI safety purely through better model alignment is a Sisyphean task. The attackers will always find a new jailbreak.
At Neuravant, we believe the solution is not to make the models perfect, but to make their environment fail-safe. We do this by applying rigorous, adversarial testing to the boundaries of the agent system.
Our approach to securing and covering Agentic AI rests on three pillars:
We did not start Neuravant merely to build another cybersecurity tool. We started it to unblock the future.
By transforming unquantifiable AI risk into a managed, covered operational expense, we allow enterprises to finally take the training wheels off their AI systems. We are building the trust layer that the AI economy desperately needs.
Join us in building systems that aren't just intelligent, but provably safe, accountable, and covered.
London, UK • 2026