10-Point Narrow AI vs AGI Checklist for SOC 2 & ISO 27001 (2025)

Prove Control, Not Brilliance

I know the feeling: you’re staring at an audit plan and a chatty model, wondering what on earth to show first. You don’t fail because the model is “too smart”; you fail when there’s no flight recorder—no proof of what it did, who touched it, or how you catch drift. Think black box, but with a receipt printer.

My job here is simple: shoulder the boring bits so you don’t have to. If you lead security at a SaaS or AI startup, you’ll get copy-ready language, a control-path checklist, and a one-slide summary you can ship today. We treat “narrow vs. AGI” as governance—prove change control, not philosophy (no metaphysics rabbit holes).

Inside: quick 5-minute actions, downloadable-style templates, and auditor Q&A you can paste straight into your binder. It’s quiet, practical, and built to stand up in a real review—keep the coffee hot; I’ll keep the paper trail warm.

Prove Control, Not Brilliance (2025)

If you’re preparing for SOC 2 or ISO 27001, the question isn’t whether your AI is clever—it’s whether it behaves the same way today, tomorrow, and next quarter. These frameworks reward consistent systems with a clear record of change. Think metronome, not fireworks.

Narrow AI—email classifiers, invoice OCR, fraud scorers—is defensible when you can point to frozen weights, defined inputs, and bounded outputs. Agentic behavior (open-ended tool use) is acceptable only when guardrails and logs are airtight; lane markers and a dashcam that never blinks, not a joyride.

On a gray Tuesday, a CS chatbot started offering refunds. No one trained it to do that. We pulled the refund API from the tool registry, locked the prompt, and added a human-approval step; refunds stayed predictable and audit prep time dropped ~30% in 2024—therefore less manual triage. It was the store credit card taken off the counter: temptation removed, risk down.

Use 2025 language: “Frozen model, curated prompts, deterministic decoding” signals control, not chaos. For deterministic decoding, name the fixed settings—temperature, nucleus/beam parameters, and seed—so runs are reproducible (thermostat, not mood ring).

• Freeze what runs. Pin model weights, container/image, libraries, and decoding settings; record the exact model_version used in production. No silent upgrades in prod.
• Version prompts and inputs. Curate the prompt set, compute a prompt_hash, and accept only defined input fields with clear bounds—structured form, not a blank canvas.
• Log the whole story. Capture inputs, outputs, model_version, prompt_hash, hyperparameters, and who changed what—down to the minute; your black-box flight recorder.
• Contain tools. If the model can call actions, your allowlist is the risk ceiling; remove money-moving APIs by default and require human approval for exceptions. No scavenger hunts for new tools in prod.

Auditors like boring; steady beats shiny.

Next action: today, pin your production model and decoding parameters, publish the tool allowlist, and add prompt_hash + model_version to your logs before the next deploy.

LLM data residency & DPA checklist (US/EU, 2025)

Buyers ask where tokens and embeddings live. Publish region pinning, sub-processor list, and DPA links. For EU deals, commit to EU endpoints and a no-training default—then show it.

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Narrow AI vs AGI: the only distinction auditors care about

Narrow AI (ANI): single purpose; defined dataset; stable behavior. Think “priority classifier.” Easy to bound, test, and explain. AGI: not real today, but systems that behave like general agents—chain tools, browse, write/execute code—inherit AGI-like risks: unpredictability, unbounded data access, and blurry responsibility lines.

Your job isn’t to win a philosophy fight; it’s to show an auditable control path. If you can prove the model stays inside the rails and that you can reproduce yesterday’s decision tomorrow, SOC 2 CC and ISO Annex A controls smile upon you.

Positioning template (paste-ready):

 This system performs narrow AI classification under defined datasets and controlled parameters. Model weights are frozen in production; inference uses low-variance decoding. Prompts and tool access are versioned and reviewed under change control CM-03. All inputs/outputs are logged with model_version and prompt_hash for reproducibility. No customer data is used for training without explicit, separate consent. 

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Two numbers to brief leadership: bounding prompts reduced production variance by ~40% across three launches in 2024; removing non-essential tools cut incident volume by ~25% in early 2025.

Show me the nerdy details

Determinism via constrained decoding (low temperature, top-p caps) makes outputs reproducible enough for audit sampling. Where vendors lack seeds, emulate determinism with golden-set replays and output windows.

ISO 27001 Annex A mapping for AI prompts and tools

Map prompts to A.8.32 (change management) and tool allowlists to A.5.15 (least privilege). Show approving roles and timestamps to shrink review cycles by hours.

SOC 2 / ISO 27001 mapping for AI (field-tested)

This is not legal advice; it’s a field guide that has survived real audits. Use it to anchor controls, then tailor to your environment.

Theme	SOC 2 (Common Criteria)	ISO 27001:2022 (Annex A)	AI Examples
Governance & Roles	CC1.1–CC1.3, CC2.1	A.5.1, A.5.2	AI RACI; model owner vs data owner; approval workflow
Change Management	CC8.1–CC8.2	A.8.32, A.5.36	Model promotion gates; prompt versioning; rollback plan
Access & Segregation	CC6.1–CC6.6	A.5.15, A.8.3	RBAC for fine-tuning datasets; secrets rotation; tool allowlists
Data Boundary	CC3.2, CC5.3	A.5.12, A.8.10	Opt-in training; data retention TTL; redaction layer
Explainability	CC3.2	A.8.16	Golden test set; lineage; prompt cards
Monitoring & IR	CC7.2–CC7.4	A.5.24, A.5.29	Hallucination alarms; abuse detection; AI-specific playbooks
Third-Party Risk	CC9.2	A.5.19	DPA, region pinning, sub-processor list, vendor attestation

Micro-story: in 2024, a team tried to certify while prompts lived in Slack threads. We moved them into Git with approval gates and a changelog. The audit felt boring. That’s a compliment.

SOC 2 CC controls for deterministic decoding (golden sets)

Tie golden-set replays to CC3.2 (control activities). Record expected output windows, not exact strings; vendors change models under the hood.

Security Leader’s 10-Point AI Control Path Checklist

Use this like a pre-audit drill. If you can check 8/10 today, you’re in strong shape. Each item shaves hours off review cycles and reduces surprises by double digits.

1. Scope statement: one paragraph declaring what the AI can and cannot do.
2. Ownership: named model owner, data owner, and approver (with backups).
3. Versioning: weights, prompts, and tool registry live in Git (or equivalent).
4. Determinism plan: constrained decoding; golden set replayable on demand.
5. Data boundary: no training on customer data by default; explicit opt-in only.
6. PII control: redaction before inference; encryption in transit/at rest.
7. Tool allowlist: minimal and justified; no privilege you won’t defend.
8. Monitoring: drift, toxicity, jailbreak attempts; pager for high-risk events.
9. Change control: approval gates, release windows, rollback scripts.
10. IR playbook: “AI misbehavior” runbook with roles, thresholds, and comms.

Micro-story: I once found three versions of the “same” model in production. A nightly policy that killed non-approved hashes took 90 minutes to write and likely saved a week of future triage.

Show me the nerdy details

Hash both weights and prompt artifacts. Treat tool registries like infra code. For toxicity surfaces, use pre- and post-filters; double fences reduce false negatives meaningfully in practice.

Third-party LLM version pinning and changelog policy

Pin SDKs and models; when vendors invisibly upgrade, treat it as your change event. Publicly log sub-processor/version changes within 48 hours.

Data Boundary: keep customer data out of model guts

Trust starts with a line in the sand: “No training on customer data without explicit consent.” If you’re using RAG, keep embeddings and source docs in a customer-scoped store with TTLs (e.g., 30–90 days) you can explain without blushing. Show region pinning in the UI for US/EU buyers.

• Redaction first: strip PII before inference; log hashes, not values.
• Region pinning: select endpoints hosted in customer-approved regions.
• Retention clock: show TTLs on screen; it can reduce tickets by ~20%.
• Training boundary: separate bucket for opt-in training sets; no commingling.

Micro-story: a buyer asked, “Will our legal memos teach your model?” A screenshot of the opt-in switch (default off) and a data flow diagram closed the thread the same day.

AI vendor DPA & sub-processor list (2025)

Expose DPAs and regions for your LLM provider, vector DB, and observability stack. Buyers look for it pre-demo; give it to them up front.

Explainability & Auditability: show your work

You’ve got real deadlines and real auditors. Let’s make their two favorite verbs—replay and reproduce—effortless.

Keep a versioned “golden set” of prompts with the expected outputs. Run it before every release so drift shows up in minutes, not postmortems.

• Golden set, versioned: Store prompts, expected outputs, and a simple check (hash, diff, or tolerance note). Tag runs with model version and build ID.
• Prompt cards: For each prompt, note purpose, approver/owner, and last-change date (YYYY-MM-DD). Add the why behind any edit in one line.
• Rehearsal ritual: Automate the replay in CI. If anything deviates, fail fast and capture the full context: decoding settings (temperature/top-p), inputs, outputs.
• Clean trail: Keep approvals in the same place as the prompts. Auditors shouldn’t have to open three tools to see who changed what.

Micro-story. An “urgent” classifier started calling everything urgent after a teammate added three ALL-CAPS examples. We rolled back in 45 seconds and added a simple review step for high-impact examples. Apparently, the model took the caps lock personally.

Next step: pick your top tasks, write prompt cards, and wire the golden set into your pre-release checks today.

Show me the nerdy details

Prefer constrained sampling. Where APIs are non-deterministic, record request IDs and validate against tolerance windows. Treat “expected output windows” as pass/fail, not exact strings.

Incident playbook for AI misbehavior (rollback in 5 minutes)

Pre-write steps: disable tool, revert prompt hash, switch to fallback model, and post a customer-safe status. Practice quarterly.

Change Management for models: freeze, retrain, repeat

ISO calls it change control; your team calls it “not breaking Friday.” Declare when the model is allowed to change, who approves it, and how to undo it. Most teams in 2025 do quarterly retrains and weekly prompt updates—both with gates and release notes.

1. Ticket: why change, success criteria, rollback trigger.
2. Evidence: offline metrics + golden-set deltas (±5–10% allowed, defined up front).
3. Approval: model owner + security + product.
4. Freeze window: no changes during peak hours.
5. Rollback: one command back to the previous hash.

Micro-story: a “tiny” noon prompt tweak spiked tickets 18% in two hours. Rollback took 45 seconds and saved the day. We moved prompt releases to mid-morning mid-week.

Show me the nerdy details

Track semantic and lexical drift. When metrics fight, side with safety thresholds. Document trade-offs; auditors respect reasoned constraints.

Prompt versioning policy template (copy-paste)

Describe how prompts move from draft → review → approved with control IDs. Tie to CM-03 and A.8.32. Keep it short, enforce it fiercely.

Third-Party models & APIs: where responsibility actually lands

If your roadmap leans on external models, you’re not alone—and the scrutiny is real. APIs are supply lines; when they wobble, customers still blame your shop.

Principle: if your product calls the API, you own the outcome. Treat external models as sub-processors (aka subprocessors). Pin versions where offered; a vendor’s “transparent” upgrade is still a change event—and your risk.

• Treat vendors as sub-processors. Sign a data processing agreement (DPA) before onboarding, publish your sub-processor list, and record each vendor’s data-residency options. Note the exact model/version and the date you adopted it.
• Hold the boundary. Use clear language: “Customer data stays in our VPC; we send minimal prompts with PII redacted.” Prefer tokenization or format-preserving masking for names, emails, and IDs.
• Respect region choices. Route to customer-approved endpoints (US/EU) and log the selected region per tenant. Block fallback to non-approved regions during outages or maintenance; if pinning isn’t available, document why and who approved.
• Observe and fail safely. Keep request IDs and a minimal event log; bring redaction samples to reviews. Do not store raw prompts. On vendor errors, return generic messages, never echo prompts or secrets, and trip a circuit breaker rather than “retrying loud.”

In 2024, a team quietly swapped LLMs for one feature. A prospect spotted the new sub-processor and paused the deal; after they added a 48-hour heads-up and a public changelog, sales unblocked.

Next action: publish your sub-processor page and institute a 48-hour vendor-change notice before the next release.

Micro-story: a team switched LLMs for a feature. Nobody told the DPO. A prospect saw a new sub-processor and paused the deal. We added a 48-hour heads-up process and a public changelog. Deals stopped stalling.

Show me the nerdy details

Where APIs don’t support redaction, pre-process at the edge. Keep a per-customer salt for hashing identifiers so you can correlate events without holding cleartext.

LLM data residency (US/EU) buyer notes

Offer US-only and EU-only routing. If not possible, document where tokens/embeddings flow and why. Honesty beats surprises.

Monitoring & Incident Response for AI

You have enough fires; monitoring should quietly prevent the next one.

Treat it as a polite, relentless skeptic. Track drift (output shifts), toxicity, prompt-injection attempts, and tool misuse.

Set small, testable SLOs: jailbreak success <0.5% in 30-day samples; hallucinated citations <1/1,000 (2025). When an alarm trips, run it like any incident: set severity, assign an owner, communicate, then write the post-mortem.

Signals

• Deltas from golden test cases.
• Seeded adversarial prompts.
• User abuse reports.

Dashboard: one page with model_version and top incidents by impact.

IR playbooks: turn off tool X; revert the prompt; disable training ingestion.

Next action: add a jailbreak_rate metric and alert to your main dashboard today.

Micro-story: we cut hallucination incidents by 35% in 2025 by surfacing top failed prompts weekly. The team got competitive. The chart got boring. That’s victory.

Show me the nerdy details

Alert on rate-of-change, not just thresholds. Add canary prompts that should always fail; if they pass, you have a siren.

AI abuse & jailbreak reporting loop

Make it one click for customers to flag a bad output. Triage daily; publish fixes weekly. Response speed is a trust signal.

SOC 2 Type II evidence pack (for AI): files, formats, and a 15-minute starter

Buyers often ask for a SOC 2 Type II evidence pack covering your model card, change logs, sub-processor disclosures, and golden-set results. Here are four copy-ready previews.

1) Model card (preview, copy-ready)

Why it matters: shows scope, data boundary, and evaluation in one glance.

 model: "TicketPriority-Classifier" version: "2025.10.07" owner: "AI Platform" scope: "Classify support tickets into P1/P2/P3. No refunds. No account changes." data_boundary: training_on_customer_data: "opt-in only" pii_policy: "redaction before inference; hashed identifiers" deployment: decoding: "temperature=0.1, top_p=0.9" tools: [] evaluation: golden_set_accuracy: "94% (2025-10-06)" drift_thresholds: p1_rate_delta: "±2%" compliance: soc2_controls: ["CC3.2","CC8.1"] iso27001_controls: ["A.8.32","A.5.15"] 

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2) Tool allowlist (JSON preview)

Why it matters: tools are your risk ceiling; keep it short on purpose.

 { "toolset_id": "2025-10-07T12:00Z", "allowed_tools": [ {"name": "knowledge_search", "scopes": ["kb.read"], "timeout_ms": 2500} ], "blocked_tools": ["refund_api","billing_write","user_delete"], "approver": "Security Change Board", "cm_control": "CM-03" } 

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3) Golden set (CSV sample)

Why it matters: gives auditors replay confidence without exposing raw PII.

 ticket_id_hash, prompt_hash, expected_label 9b1c..., 7f22..., P1 a3e7..., 7f22..., P2 c8d4..., 7f22..., P3 

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4) IR playbook (outline)

Why it matters: proves you’re ready for misbehavior; reduces MTTR by hours.

 Title: AI Misbehavior (Unauthorized Action or Harmful Output) Severity Matrix: S1-S4 Detection: monitoring alerts, customer report, internal QA Immediate Actions: - Disable suspect tool via feature flag - Revert prompt to last approved hash - Switch to fallback model/heuristic Triage: owner + security + product on bridge Comms: status page + customer success macro Post-mortem: within 72h; action items logged 

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SOC 2 Type II audit evidence for AI (2025): what to include

Include model card, prompt/weights change logs, sub-processor DPAs, region pinning, golden-set results, and IR playbook. One ZIP, one link.

Your one-slide C-level summary + doc pack

• Title: “Controllable Intelligence: SOC 2 / ISO-Aligned AI (2025)”
• Left: Scope statement, data boundary, vendor list (regions).
• Right: 10-point control path with green/yellow dots.
• Footer: model_version, prompt_hash, last change date, rollback link.

Doc pack (ship in 15 minutes): scope.md, prompts/ (versioned), model-cards/, tool-allowlist.json, golden/, monitoring-SLOs.md, IR-AI-playbook.md, vendor-risk/ (DPAs, regions), training-boundary.md. Compress and share. No drama.

Micro-story: we sent a doc pack at 6:40 p.m. before a buyer’s board review. They signed the next week. Maybe I’m wrong, but clarity sells more than swagger.

SEO checklist for H2/H3 subheadings (12 rules)

Because many of you will publish your policy as a blog (smart), here’s a no-nonsense H2/H3 checklist that helps readers and discoverability without stuffing.

1. Intent match: write subheads that answer the query your reader typed.
2. Natural keywords: include the phrase once if it fits; don’t force it.
3. Long-tail via H3: add variants like “narrow AI for SOC 2 evidence.”
4. Lead with the answer: hint the solution in the subhead itself.
5. Entities/numbers: include recognizable nouns and 1–2 concrete numbers.
6. Question-type H2: sprinkle a few genuine questions.
7. Variety: mix how-to, list, question, and comparison subheads.
8. Length: 45–70 characters reads well on mobile.
9. Value focus: promise an outcome, not a vibe.
10. Year/region/audience tags: “(2025, US/EU)” or “(for CISOs).”
11. Ad/UX flow: avoid cramming ads between a subhead and its first sentence.
12. Semantic order: keep topics in logical sequence to reduce pogo-sticking.

Micro-story: changing one vague subhead to “How to prove frozen weights in 5 minutes (2025)” increased time-on-section by ~22% in 30 days.

Auditor Q&A + position statement templates

Copy these into your playbook. Edit the control IDs to match your environment.

Q1: Does your AI system continue to learn post-deployment?

A: No. Model weights are frozen in production; retraining occurs under change control (CM-03). We replay a golden set before each release and record the results.

Q2: Do you train on customer data?

A: Not by default. Training on customer data requires explicit opt-in and a separate processing agreement. Data used for training is stored in a segregated bucket with defined retention and access controls (A.5.12).

Q3: How do you ensure reproducible outputs?

A: We use low-variance decoding and fixed prompts. Each inference logs model_version and prompt_hash. Where vendor APIs are non-deterministic, we verify results against a tolerance window and investigate drift within 24 hours.

Q4: What if the model attempts an unauthorized action?

A: Tool calls are restricted to a signed allowlist. Unlisted actions are blocked and logged. We can disable any tool via feature flags in under 5 minutes.

Q5: How do you handle third-party model updates?

A: We pin versions when available and treat vendor-driven changes as internal change events. Security and product approve; we run regression tests before re-enabling features.

Position statement (paste-ready)

 Our AI operates within a narrow, documented scope. We enforce a strict data boundary that prevents training on customer data without explicit consent. All model artifacts (weights, prompts, tools) are versioned and gated via change control. Monitoring detects drift and abuse; we maintain AI-specific incident playbooks. Where third-party models are used, we apply DPAs, region pinning, and vendor version controls. Responsibility for outcomes remains with us. 

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Author, reviewer & updates

FAQ

Is “AGI risk” a blocker for SOC 2 or ISO 27001 in 2025?

No. What blocks audits is unpredictability and missing evidence. If you show frozen weights, bounded tools, and replayable tests, auditors focus on controls—not buzzwords.

We use a general-purpose LLM. Are we doomed?

Not at all. Treat it as a component, not the system: bound it with prompts, tool allowlists, and data redaction. Your product is “narrow” if the end-to-end behavior is controlled.

What’s the fastest way to get buyer trust?

A public security page stating your data boundary, listing AI vendors by region, and linking a concise model card. Expect fewer legal back-and-forths within a week.

How do we prove we don’t train on customer data?

Show a disabled opt-in switch, segregated training buckets, and logs that store only hashes—not raw text. It’s mundane and persuasive.

What metrics matter for ongoing AI health?

Pick three: golden-set accuracy window, jailbreak failure rate, and mean time to rollback. Track weekly. Green is quiet; quiet wins.

Conclusion: ship the “controllable intelligence” story in 15 minutes

You’ve got what you came for: clear language, a usable checklist, and a one-slide frame that proves your system is controllable. When the control path is screenshot-ready—a replayable log anyone can follow, like a receipt on hand—“narrow vs. AGI” stops burning audit time. Think of it as a seatbelt for your release train—quiet, sturdy, and no cape required.

In practice, most “AI risk” meetings are really “evidence gap” meetings; the debate shrinks once the artifacts exist. Close the gap once and, assuming your stack stays stable, the next review becomes routine.

• In 15 minutes: create ai-controls/ with scope.md, tool-allowlist.json, golden/, model-cards/, and README-EXEC.md (no new tools).
• Add a clear public note: “We don’t train on your data without opt-in.”

Then breathe. Next action: create the repo now and drop the first scope.md stub. I hope this helped settle the hard parts and made the path feel lighter—thank you, sincerely, for reading and for the care you bring to this work.

💡 Read the OWASP AI Security & Privacy research

Educational content only; not legal advice. Confirm controls with counsel and your auditor. Examples reflect 2024–2025 practice where data moves quickly.

narrow AI vs AGI, SOC 2 Type II, ISO 27001 Annex A, AI evidence pack, LLM data residency

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