What should teams do when they need to secure both regulated data and intellectual property in cloud systems?

Build the control model around the data itself

When a cloud environment holds both regulated data and intellectual property, the right starting point is not the platform or the storage service, it is the data class and the harm that follows compromise. A data-centric model lets teams apply stricter handling to the most sensitive records while still scaling across SaaS, IaaS, and PaaS.

That matters because regulated data and IP often fail in different ways. Regulated data drives compliance, notification, and privacy exposure, while IP drives competitive loss, insider abuse, and downstream reuse. If teams treat both as a single “sensitive data” bucket, they usually overprotect low-value data and underprotect the assets that would hurt most if copied or exposed.

For this reason, the operating model should start with classification, ownership, and usage context, then move to controls that reduce exposure in transit, at rest, and in use. In practice, that means limiting where sensitive data can be stored, moved, shared, or duplicated, and making exceptions visible rather than informal.

Separate regulatory handling from IP protection, then layer controls

Regulated data and intellectual property require overlapping but not identical controls. Regulated data usually needs stronger retention discipline, access logging, encryption, jurisdiction-aware handling, and auditable policy enforcement. Intellectual property often needs tighter distribution controls, narrower collaboration paths, stronger segmentation, and careful governance over exports, backups, and analytics copies.

The useful design pattern is layered protection. Start with discovery and classification, then add access restrictions, encryption, DLP, monitoring, and workflow controls that reflect exposure. Stronger controls should be reserved for high-value datasets, especially where cloud copy sprawl, shared buckets, unmanaged exports, or cross-account access can quietly widen the blast radius.

Cloud controls should also follow the movement of the data, not just the location of the primary system. If a regulated dataset is replicated into logs, development sandboxes, data warehouses, or third-party tools, the protection model has to follow those paths. The same applies to IP embedded in source repositories, build artifacts, model training sets, or collaboration platforms.

For cloud-specific structure, the CSA Cloud Controls Matrix is a useful reference because it aligns cloud governance, data security, IAM, and supply-chain concerns in one control set.

Reduce copy sprawl and watch the permissions around the data

Most serious cloud exposure problems are not caused by one dramatic failure, but by too many copies, too many exceptions, and too much standing access. The safest policy is to minimize duplication, expire transient copies quickly, and force approval for any new data movement that creates a second control plane.

That control problem is especially visible in the credentialed systems that move and process the data. Secrets, service accounts, API keys, and automation identities often determine whether a dataset stays contained or becomes broadly reachable. A cloud data program is weaker if it ignores those access paths, because the data may be classified correctly while the access layer is still over-permissive.

Teams should verify who can read, export, transform, back up, and recover the data, not just who can open the primary application. They should also review whether the cloud platform itself is creating secondary exposures through snapshots, search indexes, logs, caches, or shared analytics environments. The control goal is to keep exposure proportional to business need, not to assume the first placement is the only meaningful one.

That is why identity and secrets hygiene remain part of the answer. The Ultimate Guide to Non-Human Identities is a useful companion for understanding how machine credentials, service accounts, and other non-human access paths can widen the attack surface around cloud data.

Practitioner Guidance

What to prioritise: Start with the small set of datasets whose compromise would create the largest regulatory, legal, or competitive impact. If the team cannot name those datasets clearly, the control program is probably too generic to defend either category well.

Decision rule: If a dataset contains both regulated information and IP, apply the stricter handling requirement where the controls overlap, but do not force every dataset into the same highest-cost pattern. Keep the controls proportional to exposure, especially for read-only copies, analytics extracts, and temporary collaboration data.

What to verify: Confirm that every sensitive dataset has an owner, a classification, an approved storage location, and a reviewable record of who can move or duplicate it. Also verify that backups, exports, and non-human access paths are included in the same review, not treated as separate exceptions.

Practitioner takeaway: The winning pattern is not blanket restriction, it is disciplined data segmentation, so the most sensitive cloud datasets get the strongest controls while the rest of the environment stays usable.

Related resources from NHI Mgmt Group

• How should security teams secure hybrid data pipelines across cloud, on-prem, SaaS, and OT/IoT systems?
• How should SaaS teams implement DPDP compliance when they process personal data across cloud and GenAI systems?
• How should security teams secure mobile APIs that expose regulated data in cloud storage?
• How should security teams apply zero trust to data estates that span cloud, SaaS, and on-prem systems?