Topic
Data Security Compliance
Reference hub on proving data controls satisfy regulatory, contractual, and standards requirements, and on the evidence auditors accept.
38 posts
Compliance in data security means proving that the technical controls over sensitive data satisfy the requirements a regulator, contract, or standard sets. Requirements name outcomes: restrict access to authorized parties, encrypt data in transit and at rest, retain evidence of who reached what. Frameworks differ in scope and vocabulary, and the underlying data controls overlap heavily.
The hard part is the distance between a documented policy and an enforced one. Most programs assemble evidence from configuration exports, screenshots, and attestations, all of which describe intent at a single point in time. Auditors increasingly ask what stops the prohibited action, not what the policy says about it.
The posts under this hub work through specific regimes: CMMC 2.0 and controlled unclassified information, the FedRAMP High baseline, the HIPAA Security Rule, PCI DSS 4.0.1 and cryptographic agility, DORA, the EU AI Act, and DOJ 28 CFR Part 202. Lattix Technologies writes them from the data layer, where a control either holds or does not.
Frequently asked questions
Is encryption at rest enough to satisfy data protection requirements?
Encryption at rest protects data sitting on stored media. It does nothing once an authorized process decrypts the file and hands it to a user, an application, or a model. Frameworks that require access restriction, least privilege, and audit evidence ask for controls that persist through use, which means policy bound to the object rather than to the storage layer.
What evidence do auditors accept for data access controls?
Auditors look for records that tie an access decision to the policy in force at the time. Configuration exports and screenshots show a system state, not a decision. Logs from a policy decision point (PDP) carry more weight, because they record the requesting attributes, the policy version, and the outcome. Merkle-tree lineage makes later tampering with those records detectable.
How does data classification support compliance?
Classification assigns each data object the labels that policy references: sensitivity level, jurisdiction, handling caveat, retention class. Without those labels, access rules degrade into group membership, and every requirement that names a data category becomes unenforceable in practice. Classification quality therefore sets the ceiling on how precise attribute-based access control (ABAC) rules can ever be.
Can one set of controls satisfy multiple compliance frameworks?
At the data layer, largely yes. CMMC 2.0, the FedRAMP High baseline, the HIPAA Security Rule, and PCI DSS 4.0.1 use different language for the same handful of outcomes: restrict access by attribute, encrypt with validated modules, log decisions, and revoke reliably. Enforcing those once at the object avoids maintaining four parallel control sets.
