Data Retention: 12 Best Practices for Enterprise Risk Reduction

February 3, 2026

Collaboration

Every day, enterprise companies create, collect, and store a great amount of data from their customers, clients, employees, and partners. When this data isn’t properly retained, it can lead to significant risks, such as noncompliance with data regulations, reputational damage from breaches, high storage costs, and legal liability.

While enterprises often have a data retention policy on paper, it breaks in practice because:

Data lives across too many systems
Collaboration creates copies
Exceptions too often override rules

If you’re implementing a new data retention policy or updating an existing one, you can lower your risk by following a few best practices that increase your chance of properly enforcing the policy across real systems and collaboration workflows.

What Is Data Retention?

Data retention is the set of rules and controls that define what information you keep, where it lives, who can access it, how long you keep it, and how you prove disposal when the retention period ends.

As data retention helps prevent fines, data breaches, and the loss of essential files, these policies sit at the intersection of compliance, security, and operations. Strong data controls are particularly important for successful data retention policies, as enforcement depends on consistently locating, restricting, preserving, and disposing of data.

Data Retention Policy vs Backup vs Archive vs Legal Hold

Many retention programs break down because teams use different data governance controls interchangeably. Since each one serves a different purpose, it’s essential that you know the differences between data retention policies, backups, archives, and legal holds to avoid gaps in your data governance strategy:

Data Governance Controls	Purpose	Typical Risk If Misused
Data retention policy	Applies lifecycle rules to business data, such as retain, delete, or retain and delete later	Over-retention and inconsistent deletion across systems
Backup	Restores systems after loss or corruption	Backup copies outlive policy, so “deleted” data still exists
Archive	Long-term storage for business, historical, or regulatory needs	The archive becomes a dumping ground with weak governance
Legal hold	Preserves relevant content for investigations or litigation	Automated deletion continues, creating spoliation risk

Data Retention Regulations Enterprises Need to Consider

Enterprise retention requirements rarely come from one law. A retention schedule often needs to account for several regulations at once, depending on industry, geography, and the types of data a company handles.

Common regulations and legal frameworks include:

GDPR: GDPR data retention requirements mandate purpose-based retention for personal data. This requirement means organizations should retain personal data only as long as necessary and support timely deletion or review.
U.S. state privacy laws (e.g., CCPA and CPRA in California): State privacy laws can create retention and deletion obligations tied to consumer rights, plus expectations around limiting retention to what’s reasonably necessary.
SOX: Public companies typically retain financial reporting support, approvals, and controls evidence to satisfy auditability expectations under the Sarbanes-Oxley Act. Note that SEC Rule 2-06 under Regulation S-X separately requires external audit firms to retain specified audit and review workpapers for seven years. Organizations should work with legal counsel to determine their own SOX-related retention requirements based on their specific controls and documentation.
SEC and FINRA recordkeeping: Financial services firms may have strict retention and supervision requirements for business records and communications, including formats and auditability expectations.
HIPAA: Requires covered entities and business associates to retain HIPAA-required administrative documentation—such as policies, procedures, risk assessments, and training records—for six years from creation or last effective date. HIPAA does not set a specific retention period for medical records or PHI itself; those requirements come from separate federal programs (e.g., Medicare Conditions of Participation) and state medical record retention laws, which vary by jurisdiction and record type.
Government and defense-related controlled data rules: Organizations working in public-sector or defense-adjacent environments may need to follow controlled data handling requirements that influence where data can live, who can access it, and how it must be disposed of.

12 Data Retention Best Practices for Enterprise Risk Reduction

1. Start with a data inventory that includes unstructured collaboration

Most enterprise retention failures happen outside the database because the highest volume of business-critical information often lives in unstructured tools and shared workspaces, not in neatly managed tables. To ensure your retention rules cover the places where work actually happens, you should inventory email, chat, files, tickets, wikis, shared drives, exports, and personal storage locations tied to corporate identities.

A workable starting point:

Map systems of record and systems of collaboration
Identify where data is duplicated or exported
Document ownership, admins, and integration paths for each repository

A complete inventory supports reliable retention because it lets you apply rules to the full footprint, not the portion you can see.

2. Classify data by sensitivity, business value, and obligation, including controlled data

Retention schedules work best when they follow the data itself rather than the tool that happens to store it. Classification provides the structure for that approach by grouping information into clear categories based on sensitivity, business purpose, and obligation. Once those categories are defined, retention, access controls, and end-of-life disposal can be applied consistently across systems.

For many organizations, “controlled data” includes regulated unclassified information that requires safeguarding or dissemination controls. The U.S. government’s Controlled Unclassified Information (CUI) program is one example of how that concept is defined and managed at scale.

Organizations that handle CUI must meet the security requirements in NIST SP 800-171, which includes controls affecting how CUI is stored, accessed, and disposed of in nonfederal systems. Even organizations outside the federal contracting space may encounter similar controlled-data obligations through supply chain requirements, data sharing agreements, or industry-specific regulations.

Classification should determine:

Where the data can live
Who can access it
How long it can be retained
What sanitization or disposal method is required at end of life

3. Assign owners who can make decisions and approve exceptions

A retention policy without decision rights becomes a document nobody can enforce. Assign owners who can set retention periods, resolve conflicts, and approve exceptions without delays.

Define:

The business owner accountable for retention periods by data type
The legal or privacy authority for hold triggers and retention overrides
The security authority for controlled data handling and sanitization approvals

Clear ownership reduces the “extend retention just in case” pattern that quietly increases risk.

4. Build a purpose-based retention schedule, not a system-based one

A reliable retention schedule answers a simple question: what is this data for, and how long is it necessary?

In large organizations, retention can drift into a system-based approach. For example, email gets one timeline, chat gets another, shared drives get a third, and the same record ends up living under multiple rules. Purpose-based retention avoids that mismatch by tying retention to the business purpose and the data type, then applying the same rule everywhere that data appears.

A practical schedule design includes:

Data categories defined in business language
A map of which systems store each category
A retention period and a disposition action
A documented rationale and any legal driver

Enterprises often document a duration but skip the justification. That gap shows up later during audits, investigations, eDiscovery, and privacy requests.

Document the business purpose and legal driver behind each timeline, and keep supporting evidence that you can produce on demand. This step supports privacy laws such as GDPR, recordkeeping expectations tied to SOX, SEC, and FINRA rules for regulated firms, and retention requirements that can apply to healthcare data under HIPAA.

6. Engineer defensible deletion, not manual cleanup

Manual deletion is inconsistent, hard to prove, and easy to bypass. Defensible deletion is a legal and compliance concept referring to deletion practices that can withstand scrutiny in litigation, audits, or regulatory inquiries. This means your organization can demonstrate—with documented evidence—that data was deleted according to policy, at the right time, across every system where it existed, and that no litigation hold or preservation obligation was in effect at the time of deletion.

In practice, defensible deletion relies on automated deletion rules, retention logs, and clear definitions of what deletion means in each tool, such as soft delete versus permanent purge. It also includes guardrails such as legal holds that pause deletion when preservation is required.

Build clarity around:

What “delete” means per system, such as soft delete, hard delete, or irreversible purge
How deletion logs are captured and retained
How duplicates and exports are handled
How deletion is suspended when a legal hold applies

7. Use sanitization methods that match the risk

Deletion is not always sanitization. For sensitive and controlled categories, data disposal methods should match confidentiality requirements.

NIST SP 800-88 is widely used as a defensible standard for media sanitization because it gives organizations a risk-based way to choose the right method for the situation. It explains how to decide between approaches like clearing, purging, or destroying media based on the sensitivity of the data and the type of storage involved. Following a recognized standard also helps you demonstrate due diligence during audits, investigations, and vendor risk reviews, especially when you need to show that data is not realistically recoverable after disposal.

In day-to-day practice, the NIST SP 800-88 guidance translates into three moves:

Use lower-friction methods for low-sensitivity data
Use higher-assurance methods for high sensitivity and controlled data
Validate sanitization processes for the media types your organization uses

8. Treat exceptions as a workflow, not a loophole

Enterprises run on exceptions, and unmanaged exceptions erode policy integrity. Exceptions show up when a record needs to be kept longer for litigation, an investigation, or a regulatory request, or when a team has a legitimate business reason to pause deletion for a specific set of data.

Without a defined process, exceptions spread through informal approvals, last longer than intended, and create inconsistent retention outcomes across systems.

An exception workflow should include:

Who can request an exception and why
Required justification and an approval path
An expiration date and review cadence
System enforcement, so exceptions become controls, not email chains

9. Make collaboration retention enforceable across chat, files, and exports

Collaboration content is high-volume, high-risk, and frequently overlooked in retention programs.

Common failure patterns include mismatched lifecycle rules across content types and uncontrolled copies created by exports and integrations. Treat collaboration as its own data domain, and map where collaboration content is stored and how retention follows it.

Some retention systems differentiate between location-based policies and labels that persist with content as it moves within the governed environment. This distinction can be a useful model for collaboration-heavy enterprises.

10. Design legal holds as first-class controls that pause deletion

Legal holds preserve information that may be relevant to litigation, investigations, or regulatory requests. When legal holds are treated as an afterthought, automated retention can keep deleting while legal or compliance is trying to preserve key records.

In practice, the following often happens when legal holds aren’t integrated into your retention practice: an incident occurs, teams coordinate in chat, share files, and document decisions in tickets. Later, legal or compliance determines that some of that content must be preserved.

If legal holds are not integrated into your retention program, parts of the record may already be gone, or the record may be scattered across systems with different retention timelines.

To prevent this issue, define a hold workflow that is clear and enforceable:

Hold triggers and who can authorize them
Which systems and repositories are in scope
How holds override deletion actions and suspend disposition
How you prove hold status during audits and eDiscovery

11. Align backup retention so backups don’t undermine the policy

A retention policy that deletes content after a set period loses value if backups preserve the same content far longer. Backups exist to restore operations after loss or corruption, so backup copies often live in separate systems with their own retention settings, such as snapshots, immutable backup vaults, tape, or third-party disaster recovery.

The risk shows up when the retention program says “delete after 90 days,” but the backup program keeps full copies for a year. Data may look deleted in the primary system, yet it still exists in backup, and it can return during a restore. A legal hold can also require specific backup sets to be preserved, which raises the stakes for getting backup retention and hold processes aligned.

Align the backup program with retention intent:

Match backup retention periods to the retention policy where possible, or document where and why they differ
Define restore procedures that reapply retention rules and respect legal holds, so restores do not reintroduce data without controls
Collect evidence that supports your deletion claims, including backup retention settings and restore logs

This mismatch is common because an organization’s backup strategy is often owned by infrastructure teams while retention is owned by governance, legal, or privacy.

12. Monitor, test, and maintain an audit trail

Retention isn’t real until enforcement can be demonstrated.

Build monitoring and auditing into the program so you can show, at any point, what policy applied, what happened to the content, and when it happened. Keep audit logs and compliance reports that show policy application, deletion events, legal hold starts and releases, and restore activity that could reintroduce retained data.

Validate continuously with a repeatable cadence:

Run quarterly sampling that checks a few real data categories end-to-end, from creation through disposition or hold
Track retention coverage across repositories, plus any gaps and exclusions
Maintain an exception register with approvals, scope, and expiry dates, so exceptions don’t turn into permanent retention drift

Common Enterprise Data Retention Pitfalls and How to Avoid Them

Over-retention multiplies risk: The more data you retain, the greater the breach impact can be, and the larger the discovery surface area in disputes. Data minimization is often framed as risk reduction because it limits the amount of data retained and reduces the attack surface.
Policies exist, but enforcement doesn’t: If retention depends on user behavior, it will drift. Enterprise-scale programs need system-level enforcement and ongoing validation.
Retention and legal hold collide: A hold program that is not integrated with automated deletion can create preservation risk. Systems that document hold types and preservation workflows show how that separation should look in governance and tooling.
Controlled data is handled like normal data: If your organization manages controlled categories, retention and disposal should align with the safeguarding expectations tied to those classifications.

A Practical Data Retention Implementation Playbook

Phase 1: Discovery and control foundations

Complete the inventory across systems and collaboration
Define classification tiers, including controlled data categories
Assign owners and decision rights

Phase 2: Retention schedule and documentation

Build a purpose-based retention schedule
Document rationale and legal drivers per category
Define disposition actions and evidence requirements

Phase 3: Enforcement and hold integration

Implement retention controls across repositories
Integrate legal hold preservation with deletion workflows
Align backup retention to retention intent

Phase 4: Validation and continuous improvement

Establish quarterly testing and evidence capture
Review exception usage and reduce exception volume over time
Update schedules as business purposes and obligations change

Put Data Retention Controls Where Mission-Critical Work Happens

Enterprise risk reduction depends on enforceable retention where teams actually collaborate, share files, and coordinate incidents. Mattermost supports controlled, auditable collaboration for mission-critical environments, helping organizations apply governance to operational communications while maintaining security and control.Learn more about our sovereign collaboration platform today. If you’d like help setting up a data retention policy in Mattermost, please review our guide to configuring a data retention policy in our platform.