Why Your ChatGPT SOC 2 Policies Will Fail the Audit

You opened ChatGPT, typed "write me a SOC 2 access control policy," and got back something that looked surprisingly professional. Formal language, numbered sections, references to the Trust Services Criteria. It reads like something a compliance consultant would charge you $5,000 to produce.

The problem isn't that it looks bad. The problem is that it looks good enough to trust. And it describes a company that doesn't exist.

What ChatGPT Actually Gives You

ChatGPT generates SOC 2 policies by predicting what a SOC 2 policy should look like based on thousands of examples in its training data. The output is a composite of every compliance document it has ever seen, averaged together into something generic and plausible.

That's the opposite of what your auditor needs. Your auditor doesn't want to read what a SOC 2 policy should look like in theory. They want to read what your company actually does, described accurately enough that they can test it against your real environment.

Here's a concrete example. Ask ChatGPT to write an access control policy and it will probably include language like:

"Access reviews are conducted quarterly by system owners to verify that user permissions align with current job responsibilities. Terminated employees are deprovisioned within 24 hours."

Sounds reasonable. But does your company actually do quarterly access reviews? Is there a system owner assigned to each application? Do you actually decommission access within 24 hours of termination, or does it happen sometime the following week when someone remembers?

If the answer to any of those is "not really," you just created the exact evidence gap your auditor will find.

The Four Ways ChatGPT Policies Create Audit Exceptions

1. They include controls you don't have

ChatGPT has no idea what your company does or doesn't do. It generates the most statistically likely version of a SOC 2 policy, which includes controls that are common across companies generally but may not exist in your environment.

A ChatGPT-generated incident response plan might reference a "Security Operations Center (SOC) team" that monitors alerts 24/7. If you're a 6-person startup where the CTO checks PagerDuty between meetings, that policy is fiction. Your auditor will ask to see the SOC team's escalation procedures, shift schedules, and monitoring dashboards. You'll have nothing to show.

This isn't a hypothetical. The most common audit exceptions come from policies that promise more than the company delivers.

2. They make commitments you can't keep

ChatGPT defaults to best-practice language because that's what dominates its training data. Best practices sound impressive in a document but become liabilities when your auditor holds you to them.

Examples that show up constantly in ChatGPT-generated policies:

What ChatGPT writes	What your auditor tests	Where it falls apart
"Vulnerabilities are remediated within 30 days of discovery"	Your actual remediation timelines from scan reports	You have 4-month-old critical findings sitting in a backlog
"All employees complete security awareness training annually"	Training completion records for every employee	Three people never finished the module and nobody followed up
"Backup restoration is tested quarterly"	Evidence of quarterly restore tests with results	You've never actually tested a restore
"Changes require approval from two authorized reviewers"	Your Git history showing approvals on merged PRs	Half your changes are self-merged by the same person

Each of these becomes a deviation or exception in your audit report. A few exceptions are survivable. A pattern of exceptions across multiple controls tells the auditor that your policies are decorative, not operational.

3. They use language that doesn't match your environment

SOC 2 policies need to describe your specific systems, your specific tools, and your specific processes. ChatGPT doesn't know any of that, so it generates generic placeholders or makes assumptions.

Your policy says "the organization uses a centralized identity provider for single sign-on." Your company actually uses Google Workspace for some apps, local accounts for others, and a shared admin password for that one legacy tool nobody wants to touch. Your auditor sees the policy, asks for SSO configurations across all in-scope systems, and discovers the reality is messier than the document suggests.

This mismatch is the single most common reason first-time SOC 2 teams end up scrambling during the audit. The auditor builds their test plan around what the policies describe. When reality doesn't match, every test that relied on those assumptions has to be reworked or flagged.

4. They miss the context your auditor actually needs

Good SOC 2 policies are specific about scope, boundaries, and exceptions. They say which systems are in scope, who is responsible for what, and how edge cases are handled. ChatGPT doesn't include any of this because it doesn't know your environment.

For example, your change management policy should describe how your specific team handles changes: which repository, which branch strategy, what approval workflow, how emergency changes differ from standard changes. ChatGPT will give you a generic SDLC policy that could describe any company. Your auditor wants one that describes yours.

The system description narrative in your SOC 2 report has specific criteria (DC Section 200) for what it must include: the nature of your services, principal service commitments, system boundaries, and the specific components of your system. ChatGPT has no way to generate this accurately.

What Your Auditor Actually Does With Your Policies

Here's the part most people miss: your policies aren't just documents you hand over and forget. They're the auditor's test plan.

Your auditor reads your access control policy and builds specific tests around what it says. If your policy states that access reviews happen quarterly, the auditor's control matrix will include a test for quarterly access reviews. They'll ask for evidence of every review during the observation period. If those reviews didn't happen, the control fails.

This means a more ambitious policy creates a harder audit. A policy that accurately describes what you do, even if it's modest, is better than a policy that describes a security program you don't have. Your auditor would rather see "access reviews are conducted semi-annually" and find evidence of two reviews than see "access reviews are conducted quarterly" and find evidence of one.

The counterintuitive truth: simpler, honest policies pass audits. Aspirational policies fail them.

Where ChatGPT Can Actually Help (Without Creating Risk)

ChatGPT isn't useless for SOC 2 prep. It's just dangerous as a policy generator. Here's where it can help without creating the mismatch problem:

Explaining concepts. Ask it to explain what CC6.1 (logical access controls) requires in plain English. Use that to understand the requirement, then write your own policy based on what you actually do.

Formatting and structure. If you've written a policy in rough notes and want it cleaned up into formal sections, ChatGPT can restructure without changing the substance. Just review carefully to make sure it didn't add anything you didn't write.

Drafting interview questions. Use it to generate questions to ask your auditor or your team about specific controls. It's good at generating comprehensive lists of things to consider.

Reviewing for completeness. Paste in a policy you wrote yourself and ask "what SOC 2 control areas does this policy not address?" The gaps it identifies are often real, even if the language it suggests to fill them isn't.

The line is clear: use it to understand requirements and check your work, not to generate the actual policies that your auditor will test against.

What to Do If You've Already Submitted ChatGPT Policies

If you've already given ChatGPT-generated policies to your auditor or loaded them into your GRC platform, don't panic. But fix this before the observation window closes.

Step 1: Audit your own policies. Read every policy you submitted and ask, for each statement: is this what we actually do? Mark anything that doesn't match reality.

Step 2: Talk to your auditor. A good auditor would rather you update your policies mid-engagement than discover the mismatch during testing. Tell them you're refining your control descriptions to better match your environment. This is normal and expected, especially for first-time audits.

Step 3: Rewrite from reality, not from templates. For each policy area, start with what you actually do today. Pull your real configurations, export your actual user lists, look at how changes actually move through your pipeline. Write policies that describe those real processes. If there are gaps between what you do and what SOC 2 requires, fix the gaps first, then update the policies.

Step 4: Rebuild your evidence trail. Once your policies match reality, make sure you have evidence for every control they describe. This is where most of the work goes. For a practical breakdown of what evidence auditors expect, see our guide on the 7 documents your auditor actually needs.

The Right Way to Write SOC 2 Policies

The process should go: reality first, then documentation, then gap analysis, then remediation.

1. Document what you do today. How does access actually get granted? How do code changes actually get reviewed? What happens when someone reports a security incident? Write it down as it is, not as you wish it were.

2. Map it to the Common Criteria. Compare your real processes against CC1 through CC9. Where do your existing practices satisfy the criteria? Where are the gaps? For a walkthrough of what each CC category covers, see our SOC 2 for first-timers guide.

3. Close the gaps. Some gaps require new processes (like starting quarterly access reviews). Others just require documentation of things you're already doing (like formalizing your code review process that already exists informally in GitHub).

4. Write policies that describe the new reality. Now your policies match what you actually do, including the improvements you just made. Your auditor can test against these with confidence because the evidence will be there.

5. Collect evidence continuously. Don't wait until the auditor asks. Screenshot your configurations, export user lists, document your access reviews as they happen. Screenata automates this step by capturing evidence from your actual systems on a schedule, so you're not scrambling to reconstruct proof months after the fact.

This takes more effort than asking ChatGPT. It also produces policies that survive the audit.

How Screenata Solves This

The core problem with ChatGPT policies is that they're generated without any knowledge of your actual environment. Screenata works in the opposite direction. It connects to your real systems and builds your compliance package from what actually exists.

Here's what that looks like in practice:

1. It reads your environment first. Screenata connects to your cloud accounts (AWS, GCP, Azure) and code repositories (GitHub, GitLab). It scans your actual configurations, access controls, change management workflows, and infrastructure setup. Your policies get written based on what it finds, not what an LLM guesses.

2. Policies match reality from day one. Because Screenata knows your real MFA settings, your actual branch protection rules, your existing monitoring tools, and your real user access patterns, the policies it generates describe your company. Not a fictional composite. If you don't do quarterly access reviews yet, it won't claim you do. It'll flag the gap so you can close it before the audit.

3. Evidence collection is continuous and automatic. ChatGPT gives you a policy document and walks away. You're left to manually screenshot configurations, export user lists, and assemble evidence packs every quarter. Screenata captures evidence from your actual systems on a schedule, with timestamps and context that match the policies it helped you write. When your auditor asks for proof that CC6.1 controls operated effectively, the evidence already exists.

4. The gap between policy and evidence stays closed. This is the part ChatGPT can never do. Even if you manually fix a ChatGPT policy to match your current environment, your environment changes. Someone adds an admin account, a new service gets deployed without MFA, a firewall rule gets modified. Screenata continuously monitors for drift between what your policies say and what your systems do, so you catch mismatches before your auditor does.

Connect your GitHub and AWS, answer 15 questions, and get an audit-ready package built from your real environment. Try Screenata.

The Bottom Line

ChatGPT can write a policy that looks like a SOC 2 policy. It cannot write a policy that accurately describes your company. That distinction is the entire audit.

Your auditor's job is to compare what you say you do against what you actually do. The wider that gap, the more exceptions you'll get. ChatGPT maximizes that gap by generating the most plausible-sounding version of a policy without any knowledge of your real environment.

Write policies that describe reality. Then make reality better. That's the order that works.

Learn More About SOC 2 Audit Preparation

For a complete breakdown of what SOC 2 actually costs, what to prioritize, and how to get through it without overspending, see The Bootstrapped Founder's Guide to SOC 2.