Continuous Compliance Platform

On-prem AI retrieval for evidence, controls and audits with an audit trail.

Industry	Level	Difficulty	Recommended mode
Governance, risk & compliance	5 of 5	Frontier	hnsw

Coming soon

A complete, runnable Continuous Compliance Platform reference implementation is in progress. The architecture and core code below already work with SrvDB 0.3.

The problem

• Compliance is periodic and manual.
• Evidence lives everywhere.
• Auditors need traceability.

How SrvDB powers Continuous Compliance Platform

1. Databases for controls, evidence and findings.
2. Continuous ingestion with upsert.
3. Every retrieval logged.
4. Crash-safe evidence index.

What changes

Today	With Continuous Compliance Platform on SrvDB
Annual scrambles	Continuous readiness
Scattered evidence	Indexed evidence
No traceability	Logged retrievals

How it works

1. Connect evidence sources.
2. Index continuously.
3. Check gaps.
4. Report.
5. Audit.

SrvDB features it uses

• Several databases: each database is a directory, so different data types or tenants stay separate.
• Crash-safe persist(): after it returns, data survives a crash or power loss and reopens exactly as committed.
• Thread-safe handles: one database object can be shared between threads; searches run in parallel.
• HNSW graph (hnsw): approximate search that visits a small part of large collections; tune with set_ef_search().

Use cases

• Evidence gap checks
• Audit preparation
• Control monitoring
• Issue clustering
• Board dashboards
• Multi-entity groups

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("./continuous-compliance-platform-db", dimension=768, mode="hnsw")

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

Limits and honest notes

• Does not certify compliance.

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	Assesses which policies and controls a new regulation affects.
5 · Frontier	Continuous Compliance Platform (this page)	On-prem AI retrieval for evidence, controls and audits with an audit trail.

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