From AI logger to AI governance layer, what 5,600 downloads taught us about Chron
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From AI logger to AI governance layer, what 5,600 downloads taught us about Chron

What Chron does today

Chron is an MCP server that runs locally alongside Claude Code, Cursor, Windsurf, and Codex. Every message, every file change, every tool call gets logged to a local SQLite database with a precise timestamp and a tamper-evident hash chain.

chron history
8f3a7c9e  claude-code  API refactor          2026-07-19  14 events
b91c2a11  cursor       Contract review       2026-07-18  8 events
3d0a5f22  claude-code  Auth middleware fix   2026-07-17  31 events

chron verify 8f3a
# โœ“ hash chain intact - 14 events, no tampering detected

On top of that, chron review runs a deterministic compliance analysis across four frameworks:

  • SOC 2 - access control changes, secret exposure, infrastructure modifications
  • ISO 27001 - cryptography changes, access management, incident handling
  • EU AI Act - risk management, data governance, human oversight
  • NIST AI RMF - AI policy changes, model evaluation, incident response
chron review --framework=soc2 --output=evidence.html

The output is a printable HTML evidence package - not a compliance opinion, but session evidence a control owner can actually use.

The coverage map: honest about what Chron sees

One of the things we deliberately got right: Chron doesn't claim to "cover" a framework. It covers specific controls that are observable from AI session activity. Everything else - policy documentation, physical access, contractual obligations - requires evidence from other sources.

chron review --framework=nist-ai-rmf --full-map

NIST AI RMF - Full Control Coverage Map

Covered by session evidence (12)
โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
GOVERN 1.1    AI policy and governance changes
              rule: nist-ai-rmf.govern.ai_policy_change
MAP 3.5       Sensitive data in AI sessions
              rule: nist-ai-rmf.map.sensitive_data_in_session
MEASURE 2.7   AI monitoring modifications
              rule: nist-ai-rmf.measure.ai_monitoring_change
...

Needs additional evidence (28)
โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
GOVERN 2.1    Organizational roles defined
              source: policy_docs
MAP 2.2       Scientific findings documented
              source: policy_docs
...

Manual review required (18)
โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
GOVERN 3.1    Feedback mechanisms in place
              human judgement required
...

Out of scope (8)
โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
GOVERN 5.2    Organizational policies current
              not observable from session logs

66 NIST AI RMF subcategories. 12 covered by Chron session evidence. 54 classified honestly. The disclaimer at the top of every report: "Covered does not mean compliant."

What's coming next: Attention Score

At 5,600 installs, the missing piece is triage. When you have 200 sessions logged, you don't need a list - you need a signal. Which of these sessions involved something worth a second look?

The next release adds an Attention Score to every session: a deterministic 0-100 score computed from three signal categories.

  • Secret exposure - was a credential, API key, or token detected in the session? (+30)
  • Sensitive code changes - did the AI modify auth, production config, infrastructure, or AI oversight code? (up to +30, based on path patterns)
  • Open compliance findings - does this session have unresolved findings from chron review? Severity-weighted, capped at +30.

The result is visible everywhere you already look:

chron history
8f3a7c9e  claude-code  API refactor          [ATTENTION: 86] โ—โ—โ—โ—
3d0a5f22  claude-code  Auth middleware fix   [ATTENTION: 72] โ—โ—โ—โ—‹
b91c2a11  cursor       Contract review       [ATTENTION: 12] โ—โ—‹โ—‹โ—‹

chron risk --since=30d
Sessions requiring attention (last 30 days)

1. 8f3a7c9e  claude-code  API refactor          Score: 86
   โ†ณ secret detected in session
   โ†ณ auth code modified (3 files)
   โ†ณ 2 unresolved SOC 2 findings (1 critical)

2. 3d0a5f22  claude-code  Auth middleware fix   Score: 72
   โ†ณ access control code modified
   โ†ณ production config changed
   โ†ณ 1 unresolved ISO 27001 finding

No AI in the scoring path. Pure path matching + database queries. Same session, same score, every time.

Why this matters now

The compliance question used to be "do we have logs?" Most teams can answer that today. The question in 2026 is "did the AI touch anything that warrants a review?" That's a triage problem, not a logging problem. Attention Score is the answer.

After that: a static local dashboard - chron dashboard - that generates an HTML snapshot with session list, scores, top reasons, open findings, and framework coverage. No server, no cloud account, no new dependencies. Same personality as the rest of Chron.

Chron is available on npm:

npm install -g chron-mcp
chron-mcp  # auto-configures Claude Code, Cursor, Windsurf

Source and docs: github.com/SirinivasK/chron

Chron logs every AI conversation to a local SQLite database you own. Nothing leaves your machine.

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