Telemetry in AI and why it may be a ticking bomb for CTOs and CFOs
Exploding telemetry volumes are creating hidden governance, financial, and operational risks across AI systems.
Telemetry is increasingly central to modern AI systems, with its collection and retention tripling in many enterprises over the past year. As AI systems both produce and consume telemetry, it transforms from a mere infrastructure issue into a form of organizational memory that requires governance, cost control, and clear understanding of its use cases.
The growing value placed on telemetry often outpaces any clear comprehension of its applications, leading to escalating storage, governance, security, compliance, and discovery risks. The retention bias is asymmetrical, with organizations tending to keep data indefinitely due to the uncertainty of what might be lost, despite the ongoing storage, security, compliance, and legal costs associated with each dataset.
To mitigate these risks, CTOs should focus on explaining what their organization knows and the origins of that knowledge, while CFOs need to view telemetry as an ongoing commitment rather than just an infrastructure cost. By clearly defining the business outcomes, value, and expiration dates for each telemetry stream, organizations can better manage its risk and cost.
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