SOC 2 Compliance for AI Agent Deployments

SOC 2 compliance for AI agent deployments requires demonstrating that autonomous software agents operate under the same logical access controls, monitoring, and change management as human operators. SafeClaw by Authensor maps directly to SOC 2 Trust Service Criteria by enforcing deny-by-default policies on every agent action, producing hash-chained audit logs for monitoring evidence, and storing policies as version-controlled code for change management traceability.

Quick Start

npx @authensor/safeclaw

SOC 2 Trust Service Criteria Mapping

CC6.1 — Logical Access Security

SOC 2 requires logical access controls that restrict system access to authorized individuals and systems. SafeClaw enforces this at the action level:

version: "1.0"
description: "SOC 2 CC6.1 — Logical access controls"

rules:
  - action: file.read
    path: "src/**"
    effect: allow
    reason: "CC6.1: Scoped read access to application source"

- action: file.write
    path: "src/**"
    effect: allow
    reason: "CC6.1: Scoped write access to application source"

- action: file.read
    path: "secrets/**"
    effect: deny
    reason: "CC6.1: Block agent access to credential stores"

- action: shell.execute
    command: "sudo *"
    effect: deny
    reason: "CC6.1: Prevent privilege escalation"

- action: "*"
    effect: deny
    reason: "CC6.1: Deny-by-default baseline"

Every action request is evaluated against this policy before execution. Denied actions never reach the underlying system.

CC6.3 — Role-Based Access

Map agent roles to specific policies:

# roles/production-agent.yaml
version: "1.0"
description: "Production agent — read-only with deployment capability"

rules:
  - action: file.read
    path: "dist/**"
    effect: allow
    reason: "CC6.3: Read built artifacts"

- action: shell.execute
    command: "kubectl apply *"
    effect: allow
    reason: "CC6.3: Deploy to production cluster"

- action: shell.execute
    command: "kubectl delete *"
    effect: deny
    reason: "CC6.3: Block destructive production operations"

- action: "*"
    effect: deny

CC7.2 — System Monitoring

SafeClaw's audit trail satisfies continuous monitoring requirements. Every action evaluation is logged:

npx @authensor/safeclaw audit export --format json --since "30 days"

Log entries include timestamp, action type, target resource, policy decision, matched rule, and hash chain link. Auditors can verify no entries were tampered with using:

npx @authensor/safeclaw audit verify

CC8.1 — Change Management

Policies stored in Git provide complete change history:

git log --oneline .safeclaw/

Each policy change shows who made it, when, and through what review process. Branch protection rules ensure policy changes require approval before merging.

CC7.3 — Evaluate and Communicate

Generate periodic compliance summaries:

npx @authensor/safeclaw audit summary --since "quarter"

This outputs total evaluations, allow/deny ratios, top denied actions, and policy drift indicators — exactly what SOC 2 reviewers need for their evaluation narratives.

Evidence Package for Auditors

When preparing for a SOC 2 audit, assemble this evidence from SafeClaw:

1. Policy files — Current .safeclaw/*.yaml demonstrating access controls
2. Git history — Policy change log showing change management process
3. Audit log export — 90-day action log with hash chain integrity
4. Verification report — Output of audit verify confirming log integrity
5. Summary statistics — Quarterly summary showing enforcement effectiveness

Implementation Checklist

- [ ] Install SafeClaw: npx @authensor/safeclaw

[ ] Define policies per CC6.1 (logical access)
[ ] Create role-specific policies per CC6.3
[ ] Enable audit logging (default: on)
[ ] Configure log retention (minimum 90 days)
[ ] Add policy validation to CI pipeline
[ ] Schedule quarterly audit exports
[ ] Document policy review cadence

Why SafeClaw

• 446 tests covering policy evaluation, audit logging, and hash chain integrity
• Deny-by-default satisfies CC6.1 requirement for explicit access authorization
• Sub-millisecond evaluation ensures compliance does not impact system performance
• Hash-chained audit trail provides CC7.2 monitoring evidence with tamper detection
• Works with Claude AND OpenAI — consistent controls regardless of LLM provider
• MIT licensed — no proprietary dependencies in your compliance scope

See Also

• AI Agent Compliance Reporting: What Auditors Need
• Audit Trail Requirements for AI Agents in Regulated Industries
• Building an AI Governance Framework with SafeClaw
• Hash-Chained Audit Logs for AI Agents