At ISC2 Strategic Congress 2026 in Denver, cyber‑law specialist Daniel B. Garrie outlined three frameworks that CISOs can use to protect their organizations from AI‑related liability (Dark Reading).
The first framework assigns a quantitative risk score to each AI feature before deployment, letting leaders compare risk levels across projects and prioritize mitigation.
The second framework calls for formal documentation of board‑level decisions about AI risk appetite, creating a paper trail that regulators or litigators can reference.
The third framework centers on preserving specific data sets and model snapshots after an incident, ensuring forensic investigators have the evidence needed to determine cause and responsibility.
Garrie emphasized that these evidence‑preservation steps should be baked into vendor contracts, giving CISOs the right to request model snapshots and access logs promptly after a breach.
He also advised that contracts spell out risk‑allocation terms—including warranties, insurance coverage, liability caps, and clear dispute‑resolution mechanisms—to avoid ambiguity when a problem arises.
“Can the AI vendor provide you with a snapshot of the model at the time of an incident?” Garrie asked CISOs, highlighting the practical question that can make the difference between proving negligence and demonstrating due diligence.
By negotiating these provisions early, security leaders can secure the forensic data needed to defend their actions and reduce the chance of costly legal exposure.
The three‑step approach suggests that CISOs who treat AI governance as a documented, contract‑driven process will be better positioned to meet emerging regulatory expectations and defend against liability claims. While the frameworks are still nascent, their focus on measurable risk, board oversight, and evidence rights aligns with broader trends toward accountability in AI deployments.
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