Mockly vs SQL Audit Checklist

Quick verdict: Mockly vs SQL Audit Checklist

If you want fast clarity, start with Mockly. If you want maximum control and are willing to invest time, choose SQL Audit Checklist — but use a checklist to avoid missed edge cases.

Feature matrix: Mockly vs SQL Audit Checklist

Use-case recommendations (Mockly vs SQL Audit Checklist)

• Choose Mockly when you need quick signal, clear outputs, and a guided remediation path.
• Choose SQL Audit Checklist when you want deep visibility and already have a structured review process.
• Combine them when possible: scan for fast signal, then verify and harden with manual review/checklists.

Where Mockly shines

• Fast feedback loop for indie hackers and small teams
• Outputs fixes and verification steps (not just findings)
• Backend-only posture by default (reduces client-side risk)
• Clear labeling when scan coverage is limited
• Best when you need momentum: clear next actions and verification steps that reduce guesswork.

Where SQL Audit Checklist shines

• Highly repeatable and automatable
• Makes drift between environments easier to spot
• Great foundation for CI checks after migrations
• Doesn’t require any third-party tool
• Best when you want deep understanding and can invest time to avoid edge-case misses.

Pricing & operational tradeoffs

Blind spots to watch for (Mockly vs SQL Audit Checklist)

• Mockly blind spots: Deep checks may require additional access (coverage can be limited) / Not a full pentest; focuses on common Supabase exposure paths / You still need to apply and verify fixes in your own project
• SQL Audit Checklist blind spots: Requires SQL fluency to interpret correctly / Doesn’t automatically propose application-layer fixes / You must keep the checklist updated as your schema evolves
• Regardless of choice: Storage and RPC are common places teams forget to audit.

Verdict summary

Verdict: Start with Mockly if you need speed and guided fixes. Choose SQL Audit Checklist if you need control and are confident in your review process. For most teams, a hybrid workflow is best.

Implementation plan after choosing (Mockly vs SQL Audit Checklist)

1. Schedule a first run this week (put it on the calendar).
2. Pick one concrete surface to fix (one table, one bucket, or one function).
3. Apply a template/conversion and run verification (direct access fails).
4. Add a drift guard: re-run after migrations and compare results over time.

Hybrid workflow (often the best answer)

1. Run Mockly for fast signal and prioritize the top exposures.
2. Use SQL Audit Checklist to validate edge cases and confirm grants/policies/buckets/functions are truly locked down.
3. Apply templates/conversions and verify direct access is blocked.
4. Re-run both after migrations for drift detection (or pick one repeatable process and stick to it).

Next steps

1. Pick one option and run it on your project this week.
2. Fix one high-risk issue (tables, storage, or RPC).
3. Verify direct client access fails.
4. Re-scan after the fix and after the next migration.

Verification checklist (for whichever option you pick)

• You can reproduce one risky behavior before the fix via direct access tests.
• After the fix, that same direct access attempt fails (401/403).
• Your backend endpoints still work for authorized users.
• A re-run of your chosen tool/workflow confirms the exposure signal is gone.

Edge cases to consider (Mockly vs SQL Audit Checklist)

• Storage and RPC often behave differently across environments; verify in production, not only dev.
• A “working” UI can still be exposed via direct REST/RPC/Storage calls — always test the direct path.
• If you intentionally keep client access, keep policies small and add tests for cross-user/tenant access.
• If you move to backend-only, make sure you also remove broad grants so the boundary is enforced by the database.

Switching cost (Mockly ↔ SQL Audit Checklist) and how to de-risk it

Most teams don’t pick one approach forever. The risk is switching without keeping verification consistent.

• Keep the same direct access tests across both approaches; they are your stable signal.
• Store boundary statements and evidence artifacts in one place so tool changes don’t reset your process.
• If you switch for speed, add one deeper checklist run periodically to catch blind spots (Storage/RPC drift).
• If you switch for depth, keep a fast scan in your routine so you still get early warnings before releases.

The goal is not tool loyalty — it’s a repeatable process that keeps exposures from returning after migrations.