Continuous monitoring is potentially becoming an actuarial substitute for missing AI loss history, by allowing insurers to observe, price, and control autonomous-system risk in real time and reshaping underwriting, claims, and product design in the process.

Autonomous AI systems break the basic premise behind traditional underwriting: that the insured risk can be inspected at inception, priced off historical loss experience, and re-evaluated at renewal. As Nik Kairinos (CEO and co-founder of Raids AI) puts it in Insurance Post, insurers are being asked to cover systems they “can’t inspect, predict, or historically model,” making continuous monitoring critical for emerging AI liabilities. 

Static underwriting fails because AI risk is 'alive'

Modern AI systems (especially agentic/autonomous deployments) change after go-live: model updates, new data, shifting user behaviour, and dependency changes can all alter outcomes. This creates behaviour drift to performance, safety, and compliance properties that were true at inception may not be true next week. NIST’s AI Risk Management Framework explicitly treats AI risk management as lifecycle work, including operational testing and ongoing monitoring as systems run in the real world. 

NIST also underscored in 2026 that post-deployment AI monitoring is “vast and fragmented,” and highlighted challenges such as detecting performance degradation and drift, which is exactly the kind of uncertainty that makes insurers uncomfortable with annualised, questionnaire-driven underwriting. 

What 'continuous monitoring' means in insurance terms

For insurers, continuous monitoring is an evidence engine that can turn an opaque system into an observable risk. Practically, this can include:

1. telemetry on model outputs and error/incident rates;
2. drift detection (data, concept, and behavioural);
3. guardrail and human-override metrics;
4. audit logs proving what the AI did and why; and 
5. controls around updates (change management, rollback capability). 

Vendors positioning themselves as this 'safety net' explicitly frame monitoring as catching unintended behaviour before it causes harm. The challenge for insurers is learning to evaluate the quality of that monitoring, not merely its existence.

Regulation is pushing the market toward runtime oversight anyway

The EU AI Act hardwires lifecycle thinking into compliance. Article 72 requires providers of high risk AI systems to run a post market monitoring system that actively collects and analyses performance data “throughout their lifetime” to evaluate continuous compliance. That obligation effectively normalizes the idea that AI systems require ongoing observation, not one time approval. However, the EU AI Act was only recently adopted (2024) and is subject to phased implementation timelines; significant interpretive uncertainty remains as to how these obligations will be applied in practice. For a breakdown of the Act’s requirements and their practical implications, see Browne Jacobson’s guide.

Separately, regulators are also raising expectations on insurers as users of AI. New York’s Department of Financial Services, for example, calls for governance over insurers’ AI use in underwriting and pricing, and explicitly asks for a described process for monitoring AI usage and performance. DFS guidance in this area is still evolving, and the precise scope of these expectations may change. Browne Jacobson’s insurance sector team regularly advises on the intersection of regulatory change and insurance market practice.

What this means for insurers: Underwriting, claims, and product design

Underwriting may become more conditional and data driven

'AI observability' may come to look more like a minimum control (like MFA in cyber): no monitoring, higher retentions/sub limits or no deal. But insurers need to go beyond binary questions. The competitive advantage will, we expect, belong to underwriters who can rate the quality of monitoring, and not just whether it exists. 

Policies may evolve toward measurable warranties and triggers

Monitoring data could support clearer definitions of when an AI-related 'event' begins (e.g., drift beyond thresholds; incident declarations) and provide claims-critical evidence trails (what version ran; what prompts/data were used; who approved changes). 

Monitoring enables new products but also new disputes

As monitoring becomes standard, insurers could explore performance or control linked pricing and even parametric style structures. At the same time, it may create new contention points: gaps in logging, poor alert response, or 'known issues' visible in telemetry could become battlegrounds in coverage and misrepresentation arguments. For guidance on managing AI related risk across the technology lifecycle, see Browne Jacobson’s artificial intelligence services.