Secure Storage And Access Control

Why this matters

As a Computer Vision Engineer, you handle sensitive images, videos, labels, and model artifacts. These may contain personal data (faces, license plates), proprietary layouts, or regulated healthcare and financial information. Secure storage and access control protects people, your company, and your models from data leaks, tampering, and compliance violations.

• Real tasks you will do: set up encrypted storage for datasets and models; define least-privilege roles for labeling vendors; implement retention and deletion rules; audit object access; protect embeddings and logs that include biometric signals.
• Outcome: a repeatable, documented security baseline for data, models, and pipelines.

Who this is for

• Computer Vision Engineers shipping training/inference pipelines
• ML/AI practitioners handling datasets, labels, embeddings, and model artifacts
• Team leads who must pass security reviews and audits

Prerequisites

• Basic understanding of datasets, training pipelines, and model artifacts
• Familiarity with environment variables and credential handling
• High-level knowledge of encryption (at rest and in transit)

Concept explained simply

Secure storage and access control means two things: the data is unreadable to anyone without permission (encryption), and only the right people/services can obtain that permission (access control). You decide who can see what, log every access, and remove data safely when it is no longer needed.

Mental model

Think of your vision platform as three locked boxes:

• Box 1: Data (images, videos, labels, embeddings, logs)
• Box 2: Keys (encryption keys and secrets)
• Box 3: People/Services (users, service accounts, vendors)

Keep the keys separate from the data. Only specific people/services get temporary keys for specific boxes. Everything is recorded in a logbook.

Core principles

• Least privilege: give the minimum access needed, for the shortest time
• Encryption everywhere: at rest (e.g., AES-256) and in transit (TLS 1.2+)
• Separation of duties: different roles for key management vs. data access
• Short-lived credentials: time-bound tokens over static long-lived keys
• Auditability: object-level access logs and change history
• Data minimization: store only what you need; delete when done

Worked examples

Example 1: Healthcare image dataset

1. Classify: Sensitive (contains PHI indicators, even if filenames hint at identity).
2. Encrypt at rest: enable strong encryption for storage. Keep keys in a managed keystore; rotate regularly (e.g., every 90 days).
3. Access control: define roles:
   • Data Steward: full control
   • ML Engineer: read-only to training subset
   • Labeler: read-only to de-identified tiles
4. De-identification: remove overlays; crop or blur faces/identifiers.
5. Network boundaries: restrict storage to private networks; deny public exposure.
6. Logs: enable object-level access logs and alerts on anomalies.
7. Retention: auto-delete staging copies after 30 days; archive training set after project end.

Example 2: Labeling vendor onboarding

1. Subset the data: provide only the necessary frames, not full videos.
2. Pseudonymize: replace filenames with random IDs; store mapping separately with stricter access.
3. Vendor role: read-only, no listing of unrelated folders; watermark preview images.
4. Short-lived access: time-limited credentials; disable when sprint ends.
5. QA: sample 5% of vendor accesses in logs; verify no mass downloads.

Example 3: Face embeddings store

1. Classify: Sensitive biometric data.
2. Encrypt at rest with dedicated keys; restrict key admins from data access and vice versa.
3. Separate identifiers: keep person-to-embedding mapping in a different storage container with stricter access.
4. Retention: delete embeddings for opted-out users within a defined SLA (e.g., 7 days).
5. Inference access: model service account read-only to embeddings; no human read by default.

Secure setup: step-by-step

1. Classify your assets
   Data inventory: datasets, labels, embeddings, model binaries, logs, configs.
2. Decide roles
   Examples: Data Steward, ML Engineer (read-only), Labeling Vendor (subset read), Training Job (service account), Inference Service (service account).
3. Encrypt at rest
   Enable strong encryption; store keys in a keystore; rotate keys; restrict key usage by role.
4. Encrypt in transit
   Require TLS for all transfers; avoid plain HTTP and shared network drives without encryption.
5. Access policies
   Use RBAC or ABAC; scope by path/prefix, tag, and data classification.
6. Short-lived credentials
   Use expiring tokens for human and machine access; avoid hard-coded secrets.
7. Network isolation
   Private networks, allowlist service endpoints, deny default public reads.
8. Logging and alerts
   Enable object-level access logs; alert on bulk reads, unusual hours, or unknown regions.
9. Retention and deletion
   Lifecycle rules for staging, training, and archival; verify secure delete on request.
10. Review
    Quarterly permission review; document approvals, exceptions, and incident playbooks.

Practical projects

• Secure Dataset Dropbox: create an intake bucket/folder for raw uploads with automatic rules: quarantine, virus scan placeholder step, move to encrypted storage, auto-tag sensitivity, and notify Data Steward.
• Policy-as-Text: write a minimal access policy document for your vision team (roles, permissions, expiration, escalation) and store it alongside the dataset README.
• Redaction Pipeline: implement a preprocessing step that detects and blurs faces/license plates before exporting data to labeling.

Common mistakes and how to self-check

• Over-broad access: multiple teams have write access to production datasets.
  Self-check: list who can write to each sensitive path; shrink to the minimum.
• Long-lived static keys in code repos.
  Self-check: scan code for tokens; rotate and move to environment-based secrets.
• No object-level logs.
  Self-check: simulate a read; confirm it appears in logs with subject, time, and object path.
• Embedding store treated as non-sensitive.
  Self-check: reclassify embeddings as sensitive biometric data; apply stronger controls.
• No retention policy.
  Self-check: define lifecycle rules; verify one file auto-deletes as expected.

Exercises

Do the tasks below. You can compare with the solutions. Everyone can take the exercises and quick test; only logged-in users get saved progress.

Mini challenge

Review a pipeline that ingests raw CCTV footage, runs face detection, stores embeddings, and serves search. Identify at least 5 improvements in access control, encryption, and retention. Prioritize fixes you can implement this week.

Learning path

• Before this: Data classification and privacy basics
• Now: Secure storage and access control (this lesson)
• Next: Incident response, monitoring, and compliance evidence

Next steps

• Document your current roles and permissions; get a peer review
• Turn on object-level access logs if not already
• Pilot a redaction step and measure vendor data minimization