Series 2 : Decision, not dashboards

The first series built the foundation: a data-driven approach to cyber risk analysis, connected to enterprise governance. Series 2 takes it into the room where decisions are made.

CISOs today face three high-stakes conversations that their current toolset was not built for. Defending a security budget in front of a CFO who evaluates every request in expected return per euro not maturity scores. Negotiating cyber insurance coverage when the policy was sized against sector benchmarks rather than the organisation's actual loss curve. And governing GenAI deployments when the risk surface is changing faster than any policy document can track.

In each case, the problem is the same: technical risk data that stops short of the financial translation the decision requires. A maturity score is not a return. A peer benchmark is not an exposure profile. A governance policy is not a risk estimate. The gap between what security teams produce and what business leaders need to act is the gap this series is designed to close.

Across three sessions, we work through each use case with a live model grounded in FAIR methodology and C-Risk's DDRM approach. We’ll show exactly how quantified risk analysis changes the conversation, the decision, and the outcome.

2.3 - GenAI and cyber risk: putting numbers on what you're deploying

Only 25 to 30% of enterprises are using GenAI at industrial scale and those that are encounter compliance, security, and data confidentiality issues almost immediately. The majority are accumulating shadow AI exposure without the measurement infrastructure to see it, let alone govern it.

The risk surface GenAI introduces is not incremental. It changes the vulnerability landscape at speed: new attack paths generated at machine scale, dependencies on third-party model providers that were never formally risk-assessed, and data flows that existing classification and DLP policies were not designed to cover. When a provider goes offline through technical failure or political decision, the operational impact is immediate. Sovereignty and concentration risk have moved from theoretical to operational.

Most organisations respond with a governance policy. Policies do not quantify exposure. They do not tell the CISO whether a specific GenAI initiative sits inside or outside risk appetite. They do not produce a number the CFO can evaluate or the board can act on. In this session, we show how to make that translation — applying the FAIR framework to GenAI-specific risk scenarios so that every initiative carries a financially expressed risk estimate before deployment.

What you'll learn:

-A structured map of the risk vectors GenAI introduces that existing frameworks do not cover: shadow AI exposure, model dependency, data leakage, and sovereignty risk

-A practical method for translating GenAI initiative risk into FAIR scenarios — data exposure events, model disruption, DORA and AI Act compliance breach — each expressed as an ALE range

-A format for positioning GenAI risk inside organisational appetite: comparable across initiatives, defensible to the board, and ready for the CFO conversation

-Concrete deployment experience from regulated European enterprises — what the first-movers encountered, and how quantified risk analysis changed their decision-making