AI for Compliance

Description

Use AI to map regulations to controls, identify policy gaps, test compliance automatically, or answer regulatory questions at scale.

When to use

You need to map regulations to controls, identify policy gaps, test compliance automatically, or answer regulatory questions at scale.

Usage

• Parse and map regulations to internal controls.
• Identify policy gaps across jurisdictions.
• Automate control testing and sampling.
• Cite exact provisions for findings.

Steps

1. Parse and map regulations to internal controls.
2. Identify policy gaps across jurisdictions.
3. Automate control testing and sampling.
4. Cite exact provisions for findings.
5. Maintain audit trails and human oversight.
6. Deploy in client engagements, capture requirements from discovery calls, and measure time-to-insight and decision quality (Task-GenAI Fit-style).

Code pattern

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB

# Classify regulatory text excerpts by requirement category
vec = TfidfVectorizer(ngram_range=(1, 2))
X = vec.fit_transform(regulatory_texts)
clf = MultinomialNB().fit(X, requirement_labels)

Tuning notes

• Cite exact regulatory provisions for every automated finding.
• Keep humans in the loop for interpretation and enforcement decisions.
• Track regulatory changes and re-evaluate compliance continuously.
• Build audit trails that explain how conclusions were reached.

Verification

1. Map a regulation to internal policies and report coverage gaps.
2. Classify regulatory requirements and measure accuracy vs legal review.
3. Automate a control test and compare results to manual sampling.

References

• https://doi.org/10.1007/s44163-026-01196-1
• https://doi.org/10.48550/arxiv.2601.04474
• https://link.springer.com/article/10.1007/s43681-025-00708-6
• https://arxiv.org/abs/2406.14758v2