Data today are scattered across the cloud, offices, and home devices, and protecting them requires visibility into what we are actually protecting. It’s not enough to just “be compliant” – we need to know where the data are and who is accessing them. The key is a continuous cycle of discovery, classification, and policy enforcement across the entire infrastructure.
Compliance is not a goal, but the minimum
Reports, for example from IBM, have long shown that the costs of a data breach are high and include detection of the incident, remediation, PR, fines, and long-term reputational damage. Precisely quantifying the “full price” is difficult because many factors come into play. All the more risky, then, is when the only motivation for security is meeting regulations – that is, especially for the CISO, a weak and dangerous compass. Compliance should be seen as basic hygiene, not as a strategic goal.
Cloud, AI, and tool fatigue: why our data is everywhere
After COVID came a rapid shift to the cloud and digitization; the result is “data sprawl” – data and users are dispersed across environments and devices. According to Gartner, as many as 85 % of organizations do not know where their sensitive data is, often not even where all their data resides. Artificial intelligence entered the picture as well: a good servant but a bad master if it lacks clear boundaries. And finally, security stack fatigue – companies have too many tools, too few people, and limited budgets; first, those tools need to “talk to each other” about the basics.
A new approach to protection: from discovery to enforcement
The first step is discovery: find out where we have data. Then identify which are sensitive and perform classification – protecting “everything the same” is a dead end. Next comes task prioritization, policy automation, and consistent enforcement. This cycle must run continuously for data at rest, in motion, and in use.
Protecting data in motion is the classic domain of DLP, but the cloud has changed the game for data “in bulk” – it used to require manual audits across departments; today, AI and the DSPM (Data Security Posture Management) approach help. In some cases a regular scan once a month is enough; elsewhere you need immediate visibility and to trigger a scan on every access – for example, to prevent AI from “learning” pay slips and returning them at the first prompt. A strong solution works regardless of where users and data are (cloud or on‑prem) and what device is used to access them, and at the same time can integrate into the existing security stack. From the whole approach, remember two things: we need to see where the data are, and who is accessing them right now.
