Salesforce Rescue: 6 Disasters We Solved in 2024

2024 by the Numbers

Salesforce Rescue launched in July. Since then:

• 42 emergency responses: Everything from 90-minute fixes to multi-week recoveries
• 8.2M records recovered: Mass deletions, corrupted data, failed migrations
• Average response time: 2.4 hours from contact to assessment complete
• Recovery success rate: 97% (3 incidents had partial data loss)
• Prevented business impact: Estimated $12M in lost revenue/compliance penalties

Below are the 6 most instructive disasters—the ones where everything that could go wrong, did.

Disaster 1: The Flow Loop Production Outage

The Incident

Company: Manufacturing, 1,200 users
Time: Tuesday, 9:47 AM
Symptom: Salesforce completely frozen. CPU limit errors on every page load.

What Happened

Developer deployed a new Flow to production Monday night. Flow updated Account.Industry field based on Website domain analysis.

Tuesday morning: Flow triggered on Account update. Flow logic error caused it to update Account again. Which triggered the Flow again. Infinite loop.

Within 30 minutes: Every Account save operation triggered CPU limit errors. Sales team couldn't update records. Service team couldn't log cases. Operations halted.

The Call

9:47 AM: Panicked VP of IT on the line. "Our entire org is down. Nobody can work. Sales is losing deals."

Response Timeline

• 9:52 AM: Connected to org, identified Flow causing CPU spikes
• 9:58 AM: Deactivated problematic Flow via Metadata API
• 10:04 AM: Org operational, users able to save records again
• 10:30 AM: Root cause analysis: Flow lacked recursion prevention logic
• 11:15 AM: Fixed Flow delivered with static variable recursion check

Total outage time: 90 minutes
Recovery time: 11 minutes (from engagement to org operational)

The Fix

Added recursion prevention pattern to Flow:

// Static variable to track if Flow already ran in this transaction
if (!FlowRecursionPreventionHelper.hasRun('Account_Industry_Update')) {
    // Execute Flow logic
    FlowRecursionPreventionHelper.setRun('Account_Industry_Update');
}

Lesson Learned

All Flows that update records must include recursion prevention. This should be enforced via peer review and automated testing before production deployment.

Disaster 2: The Friday Deployment That Broke Revenue

The Incident

Company: SaaS, $80M ARR, 450 users
Time: Friday, 4:32 PM
Symptom: Opportunity Close Date changed from required field to optional. Validation rules broken.

What Happened

Consulting firm deployed metadata changes Friday afternoon. Change set included Opportunity object metadata update. Accidentally removed "required" flag from Close Date field.

Validation rules assumed Close Date was always populated. With field now optional, rules threw null pointer errors. Opportunities couldn't be created or updated.

Friday evening: Sales team trying to close deals before weekend. All Opportunity saves failed.

The Call

4:32 PM: CRO called. Voice tight. "We can't close opportunities. Quarter ends Monday. We need this fixed now."

Response Timeline

• 4:38 PM: Assessment started, identified Close Date field changed from required to optional
• 4:55 PM: Recovery plan: Revert field to required, validate no null Close Dates exist
• 5:10 PM: SOQL query confirmed all Opportunities have Close Date populated
• 5:22 PM: Metadata deployment to restore required flag
• 5:35 PM: Validation complete, Opportunities saving correctly
• 6:00 PM: Sales team confirmed they could close deals

Total impact time: 128 minutes
Deals at risk: $2.4M (end of quarter pipeline)

The Root Cause

Consultant used a change set that included "full" Opportunity object metadata. Intent was to deploy one new custom field. But change set included entire object definition—and someone had unchecked "required" on Close Date in sandbox weeks prior.

Lesson Learned

Never deploy on Fridays. Especially not Friday afternoon. Especially not end of quarter.

And never use "full object" metadata deployments. Use targeted field-level metadata. Reduces risk of unintended changes.

Disaster 3: The HIPAA Compliance Violation

The Incident

Company: Healthcare provider, 300 users
Time: Monday, 8:15 AM
Symptom: Internal audit discovered PHI (Protected Health Information) visible to unauthorized users

What Happened

Permission set update Friday changed sharing rules. Contact.Medical_Record_Number__c became visible to all users with "Read Contact" permission.

Medical Record Number is PHI. HIPAA requires PHI access limited to authorized personnel only. But sales team could now see it.

Exposure window: Friday 2 PM to Monday 8 AM (62 hours).

The Call

8:15 AM: Chief Compliance Officer. Calm but serious. "We have a HIPAA breach. Need immediate remediation and audit trail."

Response Timeline

• 8:22 AM: Connected to org, confirmed Medical_Record_Number__c visible to sales team
• 8:40 AM: Remediation plan: Revert permission set, verify no data was exported
• 9:05 AM: Permission set reverted, field now hidden from sales
• 9:30 AM: Login History audit: identified which users logged in during exposure window
• 10:15 AM: Report export analysis: no users exported Contact data during exposure
• 11:45 AM: Complete forensic report delivered to compliance team

Exposure duration: 62 hours
Unauthorized users with access: 78 (sales team)
PHI records exposed: 14,200 Contacts with Medical Record Numbers
Data exfiltration: Zero confirmed instances

Outcome

Compliance team filed voluntary breach notification with HHS (required for HIPAA violations affecting 500+ individuals).

But because:

• Exposure was internal only (not external breach)
• No evidence of data access or export
• Immediate remediation upon discovery
• Complete audit trail documentation

HHS determined no penalties were warranted.

Their CCO: "The forensic report you provided was critical. We could prove nobody accessed the data. That's the difference between a warning and a $1M penalty."

Lesson Learned

Field-level security for PHI must be enforced via Field-Level Security settings, not just permission sets. And any permission set changes affecting PHI fields should trigger automated compliance review workflow.

Disaster 4: The Mass Data Delete Catastrophe

The Incident

Company: Financial services, 200 users
Time: Thursday, 11:47 PM
Symptom: Admin accidentally deleted 34,000 Account records

What Happened

Admin cleaning up test data in production (first mistake). Built Account report: "Created Date = THIS YEAR AND Type = 'Test'". Intended to delete test Accounts created during 2024.

Report filter was wrong. Actually selected: "Created Date = THIS YEAR" (no Type filter applied due to Report Builder UI confusion).

Admin clicked "Delete All" on report results. 34,000 Accounts deleted. Including 28,000 real customer Accounts.

Realized mistake immediately. But Recycle Bin: empty. Deletion was via API (Data Loader), which bypasses Recycle Bin.

The Call

11:52 PM: Admin, voice shaking. "I just deleted 34,000 Accounts. They're not in the Recycle Bin. I need help."

Response Timeline

• 11:58 PM: Connected to org, confirmed 34,000 Accounts deleted via API
• 12:15 AM: Assessment complete: Backup vendor had snapshot from 8 PM (3.7 hours prior)
• 12:30 AM: Recovery plan: Restore from backup, merge changes from 8 PM to 11:47 PM
• 12:45 AM: Backup data extraction started
• 2:30 AM: 34,000 Accounts extracted from backup
• 3:15 AM: Incremental changes identified (47 Accounts updated between 8 PM - 11:47 PM)
• 4:30 AM: Bulk upsert of 34,000 Accounts using External IDs
• 5:45 AM: Relationship reconstruction (Contacts, Opportunities linked to restored Accounts)
• 6:30 AM: Validation complete: All 34,000 Accounts restored

Total data loss: 3.7 hours of changes (47 Accounts updated after backup)
Recovery time: 6 hours 32 minutes

Post-Recovery Cleanup

The 47 Accounts updated between 8 PM and midnight required manual reconciliation:

• Restored Account had data from 8 PM
• Changes made 8 PM - 11:47 PM were lost
• Admin manually reviewed each, reapplied changes from audit trail

Lesson Learned

Never test deletion operations in production. Use sandbox. Always.

And implement backup solution with hourly snapshots (not just daily). 3.7-hour data loss window was acceptable to this client, but hourly backups would have reduced it to minutes.

Disaster 5: The Integration Cascade Failure

The Incident

Company: Retail, 2,500 users
Time: Saturday, 6:22 AM
Symptom: E-commerce integration creating duplicate orders

What Happened

Shopify → Salesforce integration broke. API authentication expired Friday night (certificate renewal missed).

Integration failed silently (no error alerts configured). Shopify orders from Friday night through Saturday morning: not synced to Salesforce.

Saturday 6 AM: Ops team manually renewed certificate, restarted integration.

Integration replayed all failed orders. But idempotency key logic had a bug. Instead of upserting Orders via External ID, it created duplicates.

Result: 2,400 duplicate Order records in Salesforce. Fulfillment team shipped double orders to 180 customers before catching the error.

The Call

10:30 AM: Director of Operations. "We shipped duplicate orders to customers. Need to identify which ones, recall shipments, fix Salesforce."

Response Timeline

• 10:37 AM: Connected to org, identified 2,400 duplicate Orders
• 11:05 AM: Analysis: matched duplicate Orders via Shopify Order ID
• 11:45 AM: Determined 180 Orders shipped duplicate (fulfillment team already shipped before duplicate was caught)
• 12:30 PM: Deduplication plan: Delete duplicate Orders, preserve original
• 1:15 PM: Automated duplicate deletion (2,220 duplicates removed, 180 shipped duplicates flagged for manual review)
• 2:00 PM: Salesforce cleanup complete

Duplicate orders created: 2,400
Duplicate shipments sent: 180
Cost to company: $47K (product cost + shipping for recalled orders)

The Integration Fix

Root cause: Integration used Salesforce Record ID as idempotency key instead of External ID (Shopify Order ID).

When integration replayed orders, it couldn't match existing Orders because Record IDs weren't in the payload. Created duplicates instead.

Fix: Changed integration to use Shopify_Order_ID__c (External ID) for upsert operations.

Lesson Learned

All integrations must use External IDs for idempotency. Never rely on Salesforce Record IDs—they're not portable, not predictable, not safe for external systems.

And implement integration monitoring with real-time alerts. This integration failed silently for 12 hours before anyone noticed.

Disaster 6: The Corrupted Formula Field Migration

The Incident

Company: Professional services, 180 users
Time: Wednesday, 3:15 PM
Symptom: Opportunity revenue calculations showing $0 for all records

What Happened

Developer migrated formula field from one org to another using change set.

Formula in source org: Amount * (1 - Discount_Percent__c)

Formula after migration: Amount * (1 - Discount_Percent__c) — looks identical, right?

Except: Discount_Percent__c field didn't exist in target org. Deployment succeeded (Salesforce allows formulas referencing nonexistent fields). But formula evaluated to null for all records.

Revenue reports: $0. Pipeline dashboards: $0. CFO: furious.

The Call

3:15 PM: CFO. Clipped tone. "Our revenue dashboard says zero. This is wrong. Fix it."

Response Timeline

• 3:22 PM: Connected to org, identified formula field referencing nonexistent Discount_Percent__c
• 3:35 PM: Root cause: Field existed in source org but not target
• 3:50 PM: Recovery plan: Create Discount_Percent__c field, backfill with default value (0), recalculate formulas
• 4:10 PM: Field created and deployed
• 4:30 PM: Batch job to backfill Discount_Percent__c = 0 for all Opportunities
• 5:15 PM: Formula recalculation triggered via mass edit (touched all Opportunities to force recalc)
• 5:45 PM: Revenue dashboard restored, values accurate

Dashboard outage time: 2.5 hours
Records affected: 8,400 Opportunities

Lesson Learned

Always validate formula field dependencies before migration. And implement post-deployment testing—this issue would have been caught immediately if anyone had checked the revenue dashboard after deployment.

Common Patterns Across All Disasters

1. Most Incidents Happen During Deployments

4 of 6 disasters involved recent deployments or metadata changes. Deployments are high-risk events.

Prevention: Comprehensive testing in sandbox. Post-deployment validation checklist. Never deploy on Fridays.

2. Automation Without Safeguards Is Dangerous

Flow loops, integration failures, mass deletion via API—all involved automation without proper safety checks.

Prevention: Recursion prevention in Flows. Idempotency in integrations. Confirmation workflows for bulk operations.

3. Monitoring Gaps Allow Silent Failures

Integration cascade failure went unnoticed for 12 hours. HIPAA violation wasn't discovered for 62 hours.

Prevention: Real-time monitoring. Integration health checks. Automated compliance audits.

4. Backup Strategy Determines Recovery Speed

Mass deletion recovery took 6.5 hours because backup was 3.7 hours stale. Hourly backups would have reduced recovery time to 2-3 hours.

Prevention: Frequent backups (hourly for critical orgs). Test recovery procedures quarterly.

5. External IDs Are Non-Negotiable

Integration failure and mass deletion both benefited from External ID strategy. Without External IDs, recovery would have taken days instead of hours.

Prevention: Implement Global_ID__c on all major objects from day one.

The Bottom Line

Every disaster was preventable.

But perfection is impossible. Systems break. Humans make mistakes. Integrations fail.

The question isn't "Will disasters happen?"

The question is "Can you recover when they do?"

That requires:

• Robust backups (hourly, tested quarterly)
• External ID strategy (for relationship preservation)
• Real-time monitoring (catch failures before they cascade)
• Emergency response plan (who to call, what to do)

And sometimes, it requires calling someone who's seen it all before and knows how to fix it fast.

That's why Salesforce Rescue exists.