Regulatory Change Agent

Assesses which policies and controls a new regulation affects.

Industry	Level	Difficulty	Recommended mode
Governance, risk & compliance	4 of 5	Expert	flat

Coming soon

A complete, runnable Regulatory Change Agent reference implementation is in progress. The architecture and core code below already work with SrvDB 0.3.

The problem

• Regulations change constantly.
• Impact analysis is slow.
• Ownership is unclear.

How SrvDB powers Regulatory Change Agent

1. New regulatory text is matched against policies and controls databases.
2. The agent drafts an impact summary.
3. Every link cites sources.
4. Decisions remembered.

What changes

Today	With Regulatory Change Agent on SrvDB
Manual impact analysis	Drafted impact
Unclear ownership	Linked owners
Forgotten decisions	Remembered

How it works

1. Ingest regulation.
2. Search policy and control databases.
3. Draft impact.
4. Assign owners.
5. Record decisions.

SrvDB features it uses

• Several databases: each database is a directory, so different data types or tenants stay separate.
• search_batch: runs many queries at once, in parallel inside Rust.
• Crash-safe persist(): after it returns, data survives a crash or power loss and reopens exactly as committed.
• upsert: inserts new items and replaces changed ones, so the knowledge base stays current.

Use cases

• New data-protection rules
• Sector regulations
• Standards updates
• Internal policy changes
• Board reporting
• Horizon scanning

Core code

embed() is your embedding model (for example sentence-transformers, or a function that turns your records into feature vectors).

import json
import srvdb

db = srvdb.Database("./regulatory-change-agent-db", dimension=768)

def ingest(items):
    """items: list of (id, text, metadata dict)"""
    db.upsert(
        ids=[i for i, _, _ in items],
        embeddings=[embed(t) for _, t, _ in items],
        metadatas=[json.dumps(m) for _, _, m in items],
    )
    db.persist()                      # crash-safe checkpoint

def affected_items(regulation_text, k=5):
    hits = db.search(embed(regulation_text), k)
    return [(json.loads(db.get(i)), round(score, 3)) for i, score in hits]

Limits and honest notes

• Legal review required.

Caution

SrvDB supports compliance work; it does not certify compliance with any framework.

Build roadmap

1. Prototype: load a sample of real data with flat mode and check that searches return what an expert expects.
2. Measure: if you move to an approximate mode, compare its results with flat on the same queries (recall).
3. Pipeline: add continuous upsert and decide when to call persist().
4. Harden: test restarts and crashes, add logging, review every output path with domain experts.
5. Deploy: ship the database directory with the application or service.

All Governance, risk & compliance projects

Level	Project	What it does
1 · Beginner	Policy Search	Find the right policy passage from a plain question.
2 · Intermediate	Questionnaire Autopilot	Answers security and vendor questionnaires from past approved answers.
3 · Advanced	Control Mapper	Suggests mappings between controls across frameworks.
4 · Expert	Regulatory Change Agent (this page)	Assesses which policies and controls a new regulation affects.
5 · Frontier	Continuous Compliance Platform	On-prem AI retrieval for evidence, controls and audits with an audit trail.

See also: Use cases · Live examples (coming soon)