The Case for Data Asset Value Management

Notes from Cyber Leaders Summit Miami 2025

The ballroom buzzes with another presentation on “AI-driven DLP.” Slides scroll past full of neural nets flagging exfiltration events and claims of effortless compliance for all the relevant acronyms. The applause at the end of the session rings polite but somewhat hollow. What no one admits is that the entire pitch rests on a bankrupt assumption: that the enterprise owns its data in a single, undifferentiated mass, so any control that reduces leakage must be good. That assumption is incorrect, and until it is abandoned every conversation about data protection will fail at inception.

Data itself has no intrinsic worth. Its value is contingent, prismatic, and negotiated moment by moment among the people who depend on it. Payroll figures mean continuity for employees, exploitable leverage for ransomware crews, and an audit trigger for regulators. A sales forecast is operational oxygen for a CRO, upside fuel for investors, and a liability surface for customers if mishandled. The locus of value shifts with every observer and every use. Treating data as a monolith collapses those distinctions and guarantees negligent stewardship.

The framework I placed on record earlier this year names five distinct value classes (operational, upside, valuation, trust, and resilience), then asks defenders to map each data asset to the stakeholders who anchor those classes. Only by seeing data through these five lenses can a leader measure materiality, surface true criticality, and allocate scarce defensive capital with any rigor. The exercise exposes implicit promises the organization has made to people who will be harmed if data value is corrupted, withheld, impinged, or abused. Once those promises are visible, the CISO inherits an explicit fiduciary duty to safeguard their fulfillment. Failure to honor that duty accumulates trust debt that drags on sales velocity, valuation multiples, and resilience during crises.

Against that backdrop, the standard AI-DLP narrative looks primitive. It optimizes pattern-matching accuracy against the acronym frameworks while ignoring whether the patterns guard anything that matters to a stakeholder. It reports blocked transfers instead of demonstrating preserved value. It celebrates reduced incident counts while silently accruing trust debt through false positives that stall collaboration and false negatives that empty the vault. Most fatally, it never asks whether the vault contents were worth guarding in the first place. Compliance is achieved, yet value is squandered.

A genuine data-value program pivots on a harder discipline. First, enumerate the masters of every significant data asset. Second, quantify what each master stands to gain or lose in every material risk scenario. Third, invest defensive effort proportionate to that exposure and produce evidence that the promises are being kept. The tooling flows from the analysis, not the reverse. Sometimes AI-enhanced DLP will belong in the arsenal; other times a lighter-weight control paired with a transparency artifact will return greater trust per dollar. The calculus changes daily because stakeholder expectations evolve and the context of use never sits still.

This is the uncomfortable position the reader must occupy: until you can state, in plain terms, whose value you are defending with each byte of effort, you are managing noise. You do not know which alerts to trust, which losses to triage, or which controls to retire. You possess dashboards, yet you lack orientation. The next vendor demo will not solve that deficit. Only the paradigm shift will. Begin there or keep repainting the hull while the water rises.