A 2026-ready guide to data security best practices: discovery, classification, encryption, key management, MFA/RBAC, DLP, monitoring, and incident response across cloud, mainframe, and on-prem.
Breach response has a familiar rhythm: lock down access, preserve evidence, figure out what data was touched, contain the spread, and rebuild safely. The price tag is just as familiar. IBM’s latest published Cost of a Data Breach report (2025 edition) puts the global average at USD 4.44 million and the U.S. average at USD 10.22 million.
This guide translates data security best practices into an operating set of controls you can implement, measure, and audit across cloud, mainframe, and on-prem environments.
Most security programs already know the “classic” controls. The pressure in 2026 comes from three shifts.
Identity sprawl is accelerating. By some measures, non-human identities (NHIs) outnumber human users by 82 to 1, expanding the number of credentials, tokens, and service principals that can be abused.
SaaS incidents continue to rise, even as confidence remains high. AppOmni’s 2025 State of SaaS Security report says 75% of organizations experienced a SaaS security incident in the past 12 months, while 91% express confidence in their SaaS posture.
Phishing resistance is moving from “nice-to-have” to baseline. Passkeys (FIDO credentials) are designed to replace passwords with cryptographic keys for phishing-resistant authentication. NIST’s digital identity guidance also defines and emphasizes phishing resistance as a property of authenticators and protocols.
Those shifts don’t replace the fundamentals. They change where programs break first: identity, SaaS access paths, and uncontrolled data movement.
A useful definition is operational: best practices reduce the number of places sensitive data can be accessed or copied, and they produce evidence you can audit.
Below is a consolidated 2026-ready model that maps cleanly to those sources.
Discovery answers the first problem you actually have: sensitive information exists in more places than your architecture diagram shows.
Modern environments create copies by default: backups, snapshots, analytics exports, ticket attachments, test datasets, and “temporary” shares. If your discovery program only scans primary databases, you’ll miss the data stores most likely to be exposed.
Start with an inventory that spans structured and unstructured repositories: file shares, databases, cloud buckets/blobs, data warehouses, SaaS content stores, developer object storage, and endpoint sync folders. Operationalize that with a programmatic capability like data discovery, so discovery stays current as new repositories appear.
In 2026, include non-human identity paths in the discovery scope: service accounts, workload identities, API keys, integration tokens, and automation bots. They often have the broadest access and the weakest review discipline.
Classification turns an inventory into policy. It decides what requires stronger access controls, what must be encrypted, what must be tokenized, and what must never leave controlled environments.
Keep tiers simple enough to survive rollout. Public, Internal, Confidential, and Restricted is enough for most organizations.
Tie each class to mandatory controls. Restricted data usually needs MFA, least privilege, encryption at rest and in transit, strict sharing rules, and higher-fidelity logging.
If you want a practical way to implement this at scale, use data classification to connect labels to enforcement points instead of treating labels as documentation.
Once labels are in place, establish a review cadence around them. The program fails quietly when classifications drift, and nobody notices.
Encryption is only effective when it’s consistent and backed by key discipline. Microsoft’s encryption best practices focus on protecting data across its states and using appropriate platform capabilities to apply encryption correctly.
At-rest encryption reduces exposure from lost devices, stolen storage media, snapshot leakage, and misconfigured cloud storage permissions. Azure’s guidance discusses encryption-at-rest options and key control choices (provider-managed vs customer-managed).
Standardize encryption at rest for endpoints, databases, object storage, file systems, and backup repositories that hold sensitive information.
For transit, TLS 1.3 is widely treated as the modern baseline. Microsoft notes TLS 1.3 restricts cipher suites and provides modern encryption properties such as perfect forward secrecy.
Enforce encryption for browser-to-app, app-to-API, service-to-service, and administrative access traffic. If microservices handle sensitive data, mutual TLS is a practical option for internal traffic where identity verification is as necessary as encryption.
Keys are the control plane for encryption. Weak practices erase the value of strong algorithms.
Microsoft’s guidance emphasizes choosing appropriate key management and maintaining control of encryption keys.
For 2026, three key management decisions should be explicit.
First, key ownership. Decide where provider-managed keys are acceptable and where customer-managed keys are required.
Second, access separation. Key administrators should not be the same people who administer the data stores that those keys protect.
Third, auditability. Key creation, rotation, access, and deletion should generate logs routed to centralized monitoring.
Rotation cadence should reflect sensitivity. Restrict “annual rotation everywhere” by default if you have high-risk datasets or access paths.
Data exposure still routes through access. Strong access control reduces the chance that one compromised identity becomes broad access to sensitive information.
Role-based access control scales better than individual permissions. Least privilege keeps RBAC from turning into role sprawl.
Define roles by job function. Map permissions to specific actions and resources. Review roles regularly, especially for high-risk systems and high-sensitivity data classes.
MFA remains one of the fastest ways to reduce account takeover risk. Salesforce calls MFA “one of the most effective ways” to improve protection against phishing and account compromise in practice.
For 2026, the direction of travel is phishing-resistant authentication. Passkeys are FIDO credentials using cryptographic key pairs to replace passwords and improve phishing resistance. NIST guidance also discusses phishing resistance as a property programs should target.
A practical rollout path is to start with admins and privileged users, then expand across the population that can reach Confidential and Restricted data.
Privileged accounts create an outsized blast radius. PAM focuses on credential vaulting, credential rotation, session controls, and stronger workflows for elevated access.
Zero trust is critical to data security because it eliminates implicit trust and reduces lateral movement.
Treat it as an access model, not a networking slogan. Every sensitive data access path should be verifiable by identity, device posture, and context. Segmentation should limit access for compromised identities.
In practice, zero trust manifests as identity-first controls: tighter MFA, cleaner RBAC, just-in-time privileged access, and enforcement of a “no direct path” to crown-jewel data stores.
Cloud failures often map to ownership and misconfiguration rather than cloud provider weakness. Gartner estimates that, through 2025, 99% of cloud security failures will be the customer’s fault.
For 2026, treat cloud data security as continuous operations.
If fragmentation across multi-cloud and hybrid systems is a recurring issue, a platform approach can unify visibility and policy. That’s the rationale behind data security management as an operating discipline and why teams explore solutions like the DataStealth platform.
SaaS environments now hold customer data, sensitive documents, identity metadata, and operational workflows. AppOmni’s 2025 reporting shows 75% of organizations experienced a SaaS security incident, while 91% remain confident in their SaaS security posture.
To close that gap, treat SaaS like any other sensitive repository.
Containers scale quickly, which means misconfigurations scale quickly, too.
Prioritize guardrails that affect data exposure. Tight RBAC for who can deploy and execute. Network policies that limit east-west movement. Secret handling that prevents credentials from being stored in plain text in manifests and CI logs.
Even if your Kubernetes posture isn’t perfect, these steps reduce the chance that a compromised pod becomes a data exfiltration channel.
10) Data Security in Mainframes Best Practices
Mainframes often contain the most sensitive transactional datasets and long-lived identity records. IBM describes “pervasive encryption” on IBM Z as an approach designed to simplify extensive encryption of data at rest and in flight and support compliance requirements.
Mainframe priorities for 2026 typically include strong access control administration, encryption coverage where required, and integration of mainframe telemetry and audit records into centralized monitoring, especially when mainframe data is exposed to cloud and API layers.
11) Data Security On-Prem Best Practices
On-prem environments fail in predictable ways: flat networks, broad admin access, inconsistent physical controls, and backups that are easy for ransomware to destroy.
Apply segmentation between user networks, server networks, and management planes. Restrict privileged workflows. Make backups resilient through immutability and tested restore procedures.
Retention and minimization also matter more on-prem than most teams expect. Reducing uncontrolled copies reduces the number of places where access and encryption must be flawless.
12) Data Loss Prevention, Masking, and Tokenization Stop Exfiltration and Reduce Exposure
DLP is most effective when it’s policy-driven and label-driven. Netwrix describes DLP systems as monitoring workstations, servers, and networks to prevent sensitive data from being removed, moved, copied, or transmitted without authorization and to detect misuse.
Use classification labels to drive DLP policies so rules stay consistent across endpoints, email, cloud apps, and network egress points.
Masking and tokenization reduce exposure by design. When non-production, analytics, or partner systems don’t handle raw sensitive values, the probability and impact of exfiltration drop.
If you want a deeper platform framework for these controls, connect to The Ultimate Guide to Data Security Platforms (2026) and map platform capabilities to your top “data in motion” risks.
Monitoring is how you detect drift, misuse, and compromise early enough to make a difference.
CISA’s May 2025 guidance provides implementation direction for SIEM and SOAR programs, including principles for planning and operationalizing them.
In 2026, focus monitoring on sensitive data access paths.
For 2026, the best practices for data security converge on a simple objective: fewer uncontrolled access paths to sensitive data, fewer uncontrolled copies, and better evidence when something goes wrong.
The cost and speed data from IBM’s latest published report keep the business case grounded, while identity sprawl and SaaS incident rates indicate where programs are most likely to break next.
Bilal is the Content Strategist at DataStealth. He's a recognized defence and security analyst who's researching the growing importance of cybersecurity and data protection in enterprise-sized organizations.
