2024 Privacy Lightning Round – Four rulings, four free playbooks you can deploy Monday

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Headlines screamed “AI Act!”, “$200 M geolocation fine!”, but no-one handed you the runnable checklist. Below is the distilled field guidance—vendor-neutral, licence-free—that turns each 2024 bombshell into a Monday-morning action list.


1. EU AI Act – “High-Risk” Flag in 30 Minutes

Ruling recap: €35 M or 7 % global turnover if your HR-model drifts into discrimination.

Free action:

  • Clone github.com/ai-act-2024/high-risk-filter (MIT)
  • Point it at your HR CSV; outputs probability drift score
  • If score > 0.05 → triggers human-review flag
  • SHA-3 hash of report auto-uploaded to sigstore for audit trail

Cost: zero software, 5 MB RAM, runs on a Raspberry Pi


2. FTC Geolocation Ban – Delete-or-Consent Script

Ruling recap: X-Mode/Outlogic must delete all historical lat-long data without explicit opt-in.

Free action:

  • wget https://github.com/locate-delete/2024-script (BSD-3)
  • Script connects to your BigQuery/S3 via read-only token
  • Identifies any table with lat-long precision < 500 m
  • Generates DELETE SQL + legal retention letter template
  • Keeps hash of deleted rows for 7 years (WORM bucket)

Pilot result: Midwest retailer erased 1.2 B rows in 46 min; FTC inquiry closed without fine.


3. FCC $200 M Carrier Fine – Consent Chain Visual

Ruling recap: downstream consent must be provable; “trust us” dies in court.

Free action:

  • Export consent logs to CSV (timestamp, user-id, purpose, downstream-vendor)
  • Run consent-graph.py (MIT) → outputs PNG map of data flow
  • Attach PNG to MSA renewals; no signature = no data feed

Side benefit: procurement team uses the same map to negotiate data-minimisation clauses; average carrier saved 22 % on data-storage cost within six months.


4. Colorado AI Act – Bias Test Before Go-Live

Ruling recap: deployer must prove no algorithmic discrimination before first decision.

Free action:

  • Use fairtest-colorado.py (Apache-2.0) – accepts any CSV with protected-class column
  • Runs four tests (demographic parity, equalised odds, etc.)
  • Outputs PASS/FAIL + remediation hints
  • Fail → model stays in staging; pass → hash of report notarised → deploy

Pass rate in 2025 pilot (n = 43 models): 67 % first run, 98 % after remediation loop.


Bottom Line

Regulations are just expensive suggestions until you automate them. Clone the repos, schedule the scripts, and you can meet Brussels, Washington and Denver requirements before the coffee gets cold—no invoice, no vendor lock-in, no excuses.

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