What are the common challenges in AI compliance?

AI compliance means you can show that risky AI uses are identified, controlled, and reviewed.

Why it matters

Buyers and regulators don’t expect perfection. They expect boundaries, owners, and evidence.

Business impact

Tighter AI hygiene reduces rework, speeds approvals, and protects brand equity.

Core components (and fixes)

• Unclear ownership → Make a RACI for AI uses (who requests, approves, monitors).

• No‑go data → Publish a table of forbidden data types and examples.

• Vendor sprawl → Add a quick AI tool intake with privacy and certification framework checks.

• Weak evidence → Keep a central register, sampling notes, and exceptions with expiry dates.

• Bias & accuracy → For high‑risk flows, define pass/fail metrics with human review.

Implementation basics

• Start a monthly review of high‑risk uses; rotate reviewers.

• Track defections to unsanctioned tools; offer safer alternatives.

Common pitfalls

• Announcing rules without training.

• Focusing only on models, not processes.

Next steps

Ship the register and one‑pager; schedule your first review.