Automated Information Lifecycle Management
Enforce data retention rules across files in your file shares, email, and cloud drives, without migrating anything.
- Paid by 11 firms in a single SEC action for failing to keep required records.
- $88M+
ILM explained
What Is Information Lifecycle Management?
Information lifecycle management is an approach to managing an organization's information and metadata from creation through secure deletion. ILM classifies information by business value, then applies policies that govern how it is stored, accessed, retained, and disposed of.
ILM exists because not all information is created equal. Mission-critical information must stay secure and accessible. Information that loses value quickly should move to cheaper storage, or be disposed of as soon as regulations allow. ILM aligns information management with organizational goals, risk tolerance, and compliance obligations.
Shinydocs is information lifecycle management software that enforces your data retention policy across file shares, email, and cloud drives, without migrating files.
What ILM Determines
The question
What ILM establishes
What information do we have?
✓A complete inventory of information across every system
Where is it stored?
✓The location of every repository holding it
Who owns it?
✓Named ownership and accountability for each system
Why are we keeping it?
✓A business, legal, or regulatory reason for retention
How long must we keep it?
✓A defined retention period, and a disposal date
Challenges and how it works
How Shinydocs Enforces Your Data Retention Policy
ILM programs often stall on four challenges: complexity, cost, information protection, and execution. Shinydocs answers each one by turning retention schedules into rules that run across every connected system. It classifies files, scans daily, and flags expired files for disposition.
Common challenges of ILM
Complexity
Large information environments lead to sprawl, higher storage costs, and more risk.
Shinydocs: one index across every connected repository.
Cost
ILM needs investment in tools and in the policies that govern them.
Shinydocs: no migration project, so existing systems stay as they are.
Information protection
Risk rises when sensitive information sits on low-security storage.
Shinydocs: PII, PHI, and privileged content are flagged automatically.
Execution
Classification, integration, retention, and deletion must work together.
Shinydocs: all four run from one platform, with daily scans.
How Shinydocs ILM Works
Shinydocs ILM runs as a continuous cycle across every connected repository. It connects to your systems, loads your retention schedule, and classifies each file against it. Expired files are flagged for disposition, and new files enter the cycle as they are created. Files stay where they are, and each action is logged.
Connect Repositories
Index files in place across file shares, email, SharePoint, and cloud drives. Nothing moves.
Load Retention Schedule
Turn your schedule into custom rules. Align them with industry and government records requirements.
Classify Files Against Schedule
AI matches each file to its retention category. Your team reviews only the edge cases.
Dispose of Files on Schedule
Expired files are flagged for disposition under your rules. Every classification and disposition action is logged.
Compliance
Shinydocs ILM Supports Compliance
ILM enforces retention, access, and disposal policies across the lifecycle. Clear rules and accurate classification let organizations prove compliance to a regulator or a court. ILM keeps the audit trail current and defensible.
Canadian federal institutions
ATIA, the Privacy Act, the LAC Act, and the Directive on Service and Digital.
US federal agencies
FOIA, the Federal Records Act, and NARA disposition authorities.
Records management
Retention schedules and disposition rules apply to every record in your enterprise records management program.
Legislation reviewed: October 2026
Why it matters
Benefits and Features of Modern ILM
Modern ILM turns retention policies into action, continuously reducing unnecessary storage, minimizing legal and security risks, and simplifying information management. Explore how each feature drives business value through faster decisions, stronger compliance, and more defensible audits.
| Feature | What it does | Business benefit |
|---|---|---|
| Cross-repository visibility | Governs information in separate systems without migration. | Teams see where information is, who uses it, and what happens next. |
| Automated classification | Machine learning analyzes metadata, content, and context to classify files. | The right policy applies from creation, so reports and decisions get cleaner. |
| Automated retention and disposal | Applies regulatory rules without manual effort. | Removing redundant, obsolete, and trivial information shrinks storage costs and legal liability. |
| Audit trails and version control | Records every action and keeps document history current. | Audits and legal discovery move faster, with less administrative overhead. |
| Encryption and access controls | Protects sensitive information in storage and in transit. | Breach, loss, and corruption risk drop at every stage. |
| Value-based storage tiering | Places each file on the storage tier its value justifies. | Expensive storage goes only to critical information, and recovery plans know what matters most. |
Old way vs. lifecycle way
ILM vs. Traditional Data Management
Traditional data management treats every file the same, wherever it sits and however old it is. ILM weighs each file's business value, legal obligations, and access needs. It then adjusts storage, protection, and retention as that value changes. Here is where the traditional approach falls short.
Treats all information equally
Critical records and junk get the same storage and rules.
One tier for everything
Expensive storage fills with files nobody uses.
Static rules
Policies stay fixed while information value changes.
Storage-only automation
Use, protection, and disposal stay manual.
Basic sorting
Age and access counts miss legal and business context.
One stage covered
Gaps open everywhere else in the lifecycle.
The lifecycle
The Six Stages of the Information Lifecycle
Every file moves through six predictable stages, from creation to disposal. Predictable does not mean easy to manage. Knowing which stage a file is in controls its cost, its access, and how long you keep it. Ignoring the stage invites risk.
- 1
Creation and capture
The lifecycle begins when information is created, received, or ingested. That includes emails, contracts, spreadsheets, chat messages, and logs.
Challenge: classifying information at creation, not months later.
- 2
Classification
Each file is tagged by type, source, or sensitivity. Those tags decide where it is stored, who can access it, and how long it is kept.
Challenge: early mistakes cause compliance gaps and security risks later.
- 3
Storage and management
Storage depends on access frequency, business criticality, and sensitivity. Regulated information may need encryption, restricted access, or geographic controls.
Challenge: protecting information without disrupting productivity.
- 4
Use and sharing
Information delivers its value here, in reporting, monitoring, business intelligence, and AI. Decaying information signals it is ready for archiving.
Challenge: preventing improper sharing while keeping workflows efficient.
- 5
Archiving and retention
Most information loses daily relevance but cannot be deleted yet. Retention periods range from months for personal information to decades for tax records.
Challenge: keeping archives searchable, secure, and governed.
- 6
Disposition and deletion
Disposal must follow verifiable processes so information cannot be recovered. Audit trails or certificates of destruction prove compliance.
Challenge: deleting across redundant systems without leaving copies behind.
Information Lifecycle Management Is One Part of the Platform
Information lifecycle management is one of four capabilities in Shinydocs automated information governance software. Enterprise Search, AI Data Classification, and Data Remediation share the same index and connectors. Together they find, label, clean, and govern content where it lives, with no second deployment.
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Enterprise Search
Find any file across every connected repository from one search bar.
Explore → -
AI Data Classification
Label content with high-confidence AI, so governance and AI tools stay accurate.
Explore → -
Data Remediation
Dispose of redundant, obsolete, and trivial files with a full audit trail.
Explore → -
Information Lifecycle Management
Apply retention schedules automatically, from creation through to disposition.
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