Don’t allow AI experiments to become quiet business risks

 

The OpenClaw incident highlights failure modes that can also emerge when AI agents operate on behalf of an enterprise. Credentials that grant access to internal systems or external services effectively confer organisational authority, turning security failures into sources of operational and financial risk.

 

Who is responsible for AI risk?

 

As AI applications are released, responsibility is naturally distributed across the organisation. Security teams focus on controls, risk teams define safety thresholds, legal teams interpret regulatory obligations and engineering teams build and deploy systems.

 

That shared responsibility, however, depends on shared visibility. Each function needs a common understanding of how AI applications behave once they are live, particularly as models are updated, prompts evolve, data drifts and usage patterns change in production.

 

Many AI security and safety programs have not yet adapted to this reality. Governance still relies heavily on static reviews, policies or design-time approvals, even as AI systems continue to change after launch. Without live observability and reporting, early warning signs are easier to miss, and meeting regulatory logging requirements becomes more difficult.

 

How to govern probabilistic systems

 

Public-facing AI does not fit neatly into traditional control models. These systems are probabilistic by design and operate in environments shaped by real users, real data and constant change. As a result, deterministic testing and point-in-time approvals offer only limited assurance once applications move beyond the lab.

 

Effective governance takes shape across the AI lifecycle. Before deployment, pre-release testing, including structured red teaming, can help organisations identify likely failure modes and misuse scenarios, establishing a clearer picture of how an application may behave when exposed to customers. Those expectations are then tested in production, where real-time guardrails and observability provide visibility into how AI systems respond as usage patterns shift and data evolves.

 

Over time, that picture continues to change. Models are updated, integrations expand and assumptions made during development begin to erode. Periodic security and safety evaluations help organisations reassess risk in light of how AI applications are actually being used, rather than how they were originally designed.

 

Together, these practices reflect a shift in how control is exercised. For public-facing AI, governance increasingly functions as an ongoing operational responsibility, not a one-time decision made at launch.

 

When success changes the risk equation

 

For many enterprises, the greatest AI risk does not come from failed experiments, but from successful ones.

 

As public-facing AI continues to move quickly from experimentation to production, the challenge for organisations is less about whether these systems work and more about whether they can be governed as they evolve. The transition from pilot to product marks the point at which AI risk becomes business risk.

 

Enterprises that recognise this shift early, and adapt their governance accordingly, are better positioned to scale AI without losing sight of how it behaves in the real world.

---

More on how organisations can approach this challenge is available at alice.io