← 返回
AI智能 中文

GRC-Agent | SOC 2 Quality Review

Evaluate SOC 2 report quality using the SOC 2 Quality Guild rubric (Structure, Substance, Source). Use when reviewing a vendor SOC 2 Type 1/Type 2 report, tr...
依据 SOC 2 质量公会评分标准(结构、实质、来源),评估 SOC 2 报告质量。适用于审查供应商 SOC 2 Type 1/Type 2 报告。
mangopudding
AI智能 clawhub v1.0.0 1 版本 99888.9 Key: 无需
★ 0
Stars
📥 899
下载
💾 11
安装
1
版本
#latest

概述

SOC 2 Quality Review

Project Background & Acknowledgment

This skill was built using the SOC 2 Quality Guild resources at s2guild.org as a baseline for quality-focused SOC 2 vendor attestation reviews.

This project was the first GRC agent I wanated to try creating with OpenClaw after setting up across multiple environments, including Raspberry Pi, Intel NUC, several LXC containers, and a cluster setup of 3 Mac Studios using EXO.

Big thanks to the SOC 2 Quality Guild community for sharing excellent, practical guidance that helped shape this agent.

Maintainer

  • Author: Simon Tin-Yul Kok
  • LinkedIn: https://www.linkedin.com/in/simonkok/
  • GitHub: https://github.com/mangopudding/

Review SOC 2 quality before trusting conclusions.

When NOT to use this skill

Do not use this skill for:

  • Legal advice or legal conclusions about regulatory compliance.
  • Formal certification decisions (this is a quality review aid, not an issuing authority).
  • Deep technical penetration testing or exploit validation.
  • Historical incident forensics requiring endpoint/network-level evidence collection.
  • Vendor contract drafting as a substitute for legal/procurement review.

Workflow

  1. Confirm review profile (audience, risk posture, strictness).
  2. Confirm scope.
  3. Score all 11 signals.
  4. Run S12+ advanced diligence.
  5. Summarize critical gaps.
  6. Produce decision + follow-up requests.

Review profile (required)

Before scoring, capture these user-selectable settings:

  • Primary audience: Security, Procurement, Customer Trust, or All
  • Risk posture: Conservative / Balanced / Lenient
  • Data sensitivity baseline: High / Medium / Low
  • Evidence strictness: Escalate on Unknown / Conditional acceptance with deadline / Case-by-case
  • Output style: Executive memo, Full analyst report, or Both

Default to user-provided settings when available. If not provided, ask once before final verdict.

1) Confirm scope

Capture:

  • Report type: Type 1 or Type 2
  • Period covered
  • Trust Services Categories in scope
  • In-scope system boundary
  • Auditor firm + signer
  • Qualification status (unqualified/qualified/adverse/disclaimer)

If key sections are missing, stop and request a full report.

2) Score all 11 signals

Read references/rubric.md and score each signal:

  • 2 = strong evidence
  • 1 = partial or ambiguous
  • 0 = missing, contradictory, or weak

Use a strict standard for Section 4 testing detail and source credibility checks.

2b) Run S12+ advanced diligence questions

After S1–S11 scoring, run references/advanced-diligence.md and collect answers for the additional diligence set.

Rules:

  • Treat S12+ as decision-strengthening checks, not replacements for S1–S11.
  • If an answer is unavailable, mark it explicitly as Unknown and create a follow-up request.
  • Elevate risk when multiple S12+ items remain unknown for high-sensitivity data use cases.

3) Flag hard fails

Treat these as high-severity findings by default:

  • Missing required auditor report structure (S1)
  • Missing/incomplete unsigned management assertion (S2)
  • Unlicensed or unverified CPA firm (S8)
  • Pervasive testing vagueness on critical controls (S7)

If one or more hard fails exist, recommend compensating evidence even if the opinion is unqualified.

4) Produce outputs

Always return three artifacts.

A) Executive verdict (short)

  • Overall confidence: High / Medium / Low (use references/confidence-rubric.md)
  • Decision: Accept / Accept with conditions / Escalate / Reject
  • Top 3 reasons

B) Scorecard

List S1–S11 with:

  • Score (0/1/2)
  • Evidence citation (use references/evidence-citation-format.md)
  • Why it matters
  • Follow-up request (if score <2)

C) Follow-up request pack

Create a vendor-facing request list using references/vendor-request-templates.md:

  • Direct evidence needed
  • Clarifications required
  • Deadline recommendation
  • Decision gate (what must be resolved)

Scoring guidance

  • Prioritize evidence quality over report polish.
  • Penalize boilerplate language that could apply to any company.
  • Penalize weak control-to-criteria logic.
  • Penalize mismatch between exceptions and opinion severity.
  • Separate auditor credibility concerns from control design concerns.

Decision rubric

Use references/decision-matrix.md with the selected risk posture and evidence strictness.

Baseline outcomes:

  • Accept: no hard fails, most signals strong, no unresolved critical gaps.
  • Accept with conditions: limited gaps, clear compensating evidence path.
  • Escalate: mixed evidence, source credibility concerns, or unclear testing sufficiency.
  • Reject: fundamental structure/source failures or severe unresolved substance failures.

Required response format

Use this exact section order:

  1. Executive verdict
  2. Signal-by-signal scorecard (S1–S11)
  3. Advanced diligence (S12+) findings
  4. Critical risks
  5. Vendor follow-up questions
  6. Interim compensating controls (what your org should do now)

For structure and quality calibration, mirror references/output-example.md.

Calibration rules

Apply thresholds using selected profile:

  • High sensitivity (PII/PHI/financial, including candidate resume and employer/company data): require strong minimums on S4/S6/S7/S8 and tighter follow-up deadlines.
  • Medium sensitivity: allow limited partials with compensating evidence.
  • Low sensitivity: tolerate minor source/substance weaknesses with conditions.

Apply evidence strictness setting:

  • Escalate on Unknown: unknowns on critical areas force Escalate.
  • Conditional acceptance with deadline: permit temporary acceptance only with explicit due dates and owners.
  • Case-by-case: weigh unknowns by control criticality and data sensitivity.

版本历史

共 1 个版本

  • v1.0.0 当前
    2026-03-29 10:12 安全 安全

安全检测

腾讯云安全 (Keen)

安全,无风险
查看报告

腾讯云安全 (Sanbu)

安全,无风险
查看报告

🔗 相关推荐

ai-intelligence

ontology

oswalpalash
类型化知识图谱,用于结构化智能体记忆与可组合技能。支持创建/查询实体(人员、项目、任务、事件、文档)及关联...
★ 714 📥 244,054
ai-intelligence

self-improving agent

pskoett
捕获经验教训、错误和纠正,以实现持续改进。使用时机:(1)命令或操作意外失败;(2)用户纠正……
★ 4,060 📥 798,785
ai-intelligence

Self-Improving + Proactive Agent

ivangdavila
自我反思+自我批评+自我学习+自组织记忆。智能体评估自身工作、发现错误并持续改进。
★ 1,362 📥 318,871