Object Storage Readiness for AI: A Mini-Assessment

AI is changing the game plan for enterprise storage. As organizations move beyond pilots and into real-world inferencing and analytics, they need storage platforms that can support AI workloads, not just store data.

The problem is that the most valuable data for AI is often the least accessible. Unstructured data (text files, video, audio, images, and other unformatted content) accounts for roughly 80% of corporate data, and it’s a rich source of input for AI applications. But in many enterprises, that unstructured data is still held captive in departmental silos, living on file servers and NAS environments that were never designed for fast retrieval, broad reuse, or AI-era governance.

Object storage is built for this exact mismatch. Instead of burying unstructured data in complex folder hierarchies, it stores it as discrete objects with rich metadata and unique identifiers. Access happens through S3-compatible APIs, which have become the common language across providers. Put that together and you get a foundation that’s built to organize, secure, and retrieve unstructured data in a way AI and analytics can consistently consume.

But object storage alone doesn’t guarantee AI readiness. AI exposes gaps fast: data that isn’t centralized or easy to find, workflows that slow down when multiple teams and automated jobs access the same datasets at once, critical data that isn’t protected or easy to recover, and integrations that break or need custom fixes. A quick readiness check can help you spot those pressure points before they turn into stalled initiatives and before unpredictable cloud consumption costs turn AI from an innovation program into a budget containment exercise..

That’s exactly why we built this scorecard: to give you a fast, practical read on whether your object storage setup is ready for AI. The questions point you to the specific areas to focus first so AI projects can scale without constant rework or risk.

How to use this scorecard

• For each question, choose the option that best reflects your current state: 0, 1, or 2 points.

• Add up your total score (maximum 20 points).

• Use the scoring bands and recommended next steps at the end to prioritize improvements.

Theme 1: AI-ready data foundation

1) Our AI datasets are centralized in S3-compatible object storage with a clear, repeatable access path (not scattered across file shares or drives).

• 0 - Data is scattered; teams copy data around to make AI work.

• 1 - Some data is centralized, but there are still multiple sources and manual moves.

• 2 - Most AI-relevant data lands in a consistent S3-compatible location with a repeatable ingestion pattern.

2) We use consistent dataset naming and object or bucket metadata (tags) so teams can reliably find the right data and apply the right policies.

• 0 - Naming and metadata is inconsistent or missing; discovery is hit-or-miss.

• 1 - Some conventions exist, but only for a few teams or sources.

• 2 - Standard naming and required metadata or tags across key datasets, tied to governance (owner, sensitivity, retention).

3) We can curate "AI-approved" datasets (and keep them current) with repeatable rules for what is included, updated, and versioned.

• 0 - No real curation or ownership; teams rely on one-off copies.

• 1 - Some filtering exists, but it is manual and breaks easily.

• 2 - Clear inclusion rules, dataset owners, and a refresh cadence, with versioning that supports repeatable outcomes (reproducibility).

Theme 2: Speed and scale as usage grows

4) Our AI workflows can read and write large volumes without constant storage re-architecture (scales from TB to PB using the same interface).

• 0 - Storage becomes a constraint quickly; frequent redesigns or migrations are required.

• 1 - Works for a few workloads, but scaling requires significant tuning or redesign.

• 2 - Scales predictably (capacity and throughput) using standard S3 patterns (multipart, parallelism) without frequent rework.

5) Multiple users, jobs, or agents can access the same datasets concurrently without constant performance babysitting.

• 0 - Concurrency causes slowdowns or timeouts; teams avoid shared access.

• 1 - Works with special tuning or workarounds and ongoing attention.

• 2 - Concurrency is reliable; parallel access patterns work without constant intervention.

6) We measure and manage the storage data path so expensive AI compute is not waiting on data.

• 0 - We do not measure; issues show up as "mysterious slowness".

• 1 - We measure sometimes or only for select projects.

• 2 - We measure consistently (latency, throughput, request patterns) and treat it as a core readiness KPI.

Theme 3: Cyber resilience and trust

7) Critical AI data (training sets, embeddings corpora, model artifacts, backups) is protected from tampering or deletion using immutability controls.

• 0 - Protection is ad hoc; deletes or changes can happen quietly.

• 1 - Some immutability exists, but it is inconsistent across teams or datasets.

• 2 - Standard immutability policy for critical buckets (for example, Object Lock), with clear ownership and enforcement.

8) We can withstand ransomware or insider risk at the storage layer with strong admin controls and a recovery plan we have tested.

• 0 - Admin access is a single point of failure; recovery is uncertain or untested.

• 1 - Some controls exist (MFA, permissions), but coverage and testing are uneven.

• 2 - Strong controls for destructive actions (MFA plus multi-person approval such as MUA) and a protected secondary copy for worst-case recovery (logically isolated copy such as Covert Copy), tested regularly.

Theme 4: Fit, portability, and cost predictability

9) Our AI and analytics tools integrate with storage via standard S3 APIs (no heavy custom connectors or lock-in).

• 0 - Integrations are painful; lots of custom glue code is required.

• 1 - Core tools work, but there are meaningful gaps or fragile integrations.

• 2 - Tooling integrates cleanly via S3; onboarding new tools and projects is repeatable.

10) Storage costs stay predictable as AI usage grows (more data and more access), and we can explain the bill to Finance.

• 0 - Costs are hard to forecast; surprises are common as usage increases.

• 1 - Some predictability, but there are still cost "gotchas" as access patterns grow.

• 2 - Costs behave as expected and are easy to model with clear unit economics and minimal surprise fees.

Scoring bands

Use your total score (out of 20) to identify your current readiness level:

Score of 0-7:
Pilot-only (high friction, high risk)
You can demo AI, but scaling will expose gaps: scattered data, weak governance, uneven cyber resilience controls, and unpredictable cost or performance.

Score of 8-14:
Production-adjacent (bottlenecks surface under load)
Real workloads run, but growth triggers concurrency or performance issues, uneven controls, and cost surprises tied to access patterns.

Score of 15-20:
Scale-ready (optimized foundation)
You have the building blocks: centralized data, curated datasets, stable concurrency, enforceable cyber resilience controls, and predictable economics.

Recommended next steps

Pick the guidance for your score band below. The actions are written to align with common S3-compatible cloud object storage best practices and Wasabi security and economics strengths.

If you scored 0-7: Build the foundation (consolidate, govern, protect)

• Consolidate AI-relevant datasets into a consistent S3-compatible object storage landing zone with clear bucket boundaries (by team, project, or data sensitivity).

• Standardize dataset naming and require basic metadata tags (owner, data type, sensitivity, retention) to improve discoverability and policy enforcement.

• Establish baseline cyber resilience: enable immutability for critical buckets (for example, Object Lock) and apply least-privilege access policies from day one.

• Reduce privileged risk: enforce MFA for administrators and add multi-person approval for destructive actions (for example, MUA) to prevent a single compromised credential from causing irrecoverable loss.

• Plan for worst-case recovery: add a protected secondary copy for the most critical buckets (for example, a logically isolated copy such as Covert Copy) and document a recovery runbook.

• Make costs Finance-friendly: create a simple unit-economics model (cost per TB-month and expected access patterns) and set budget or usage alerts early.

If you scored 8-14: Operationalize and harden (scale, test, monitor)

• Validate performance at scale with your real access patterns (parallel reads, multipart uploads, and concurrency) to ensure storage is not the bottleneck for AI compute.

• Operationalize monitoring: track request patterns, throughput, and latency; define target SLOs for key workflows (training set pulls, embedding builds, checkpoint writes).

• Standardize immutability and protected copies for critical data classes and run at least one recovery drill per quarter to prove time-to-restore.

• Improve dataset curation and versioning so teams can reproduce results and roll back quickly after data quality issues or tampering.

• Add guardrails for data movement: configure egress or unusual-activity alerts and align them to incident response playbooks.

• Tighten cost predictability: create showback by project or team and review month-over-month drivers (capacity growth and request intensity).

If you scored 15-20: Optimize for scale (automation, templates, resilience maturity)

• Create reusable templates for buckets, policies, immutability, and admin controls so new projects launch with the right guardrails by default.

• Expand resilience posture: tier critical datasets into stronger protection tiers (immutability plus protected secondary copy) based on business impact.

• Institutionalize governance: maintain an "AI-approved" dataset catalog with owners, refresh cadences, and retention policies that match compliance needs.

• Continuously tune for concurrency and cost efficiency as agentic and retrieval workloads increase request rates.

• Run regular tabletop and recovery exercises that include storage-layer scenarios (credential theft, insider misuse, ransomware attempting deletion or encryption).

• Review readiness quarterly: use this scorecard to track improvement and identify the next highest ROI control or process change.

Note: This scorecard is intended as a directional readiness check. Use it to guide discovery conversations and prioritize improvements, not as a formal audit.