Hey everyone. I've been tasked with helping my (pretty small) company prepare for our first SOC 2 audit, and leadership just told me they want a Type 2 report. The catch? They want to be audit-ready in less than 90 days.
We're looking at tools like Secureframe to streamline this, but I'm trying to wrap my head around the actual, day-to-day workload this will create for our team. I'm the only person with any "data" in my title, so a lot of the evidence collection and monitoring will likely fall to me.
My main question is: **For those who've used Secureframe on a tight timeline, what did the first month of setup *actually* involve?** I've read the high-level guides, but I'm nervous about hidden time sinks.
For example, I know we need to map our assets and data flows. Is it mostly manual entry, or does the tool auto-discover a lot from our cloud accounts (we're on AWS)? Also, for generating the required reports—are we talking about running pre-built SQL queries against our logs, or is it more config-heavy?
Here's a simplified version of the data flow I'm trying to document, just to see if I'm on the right track:
```sql
-- Example of what I'm sketching out for data ingress
SELECT
event_source,
COUNT(*) as auth_attempts,
COUNT(DISTINCT user_id) as unique_users
FROM cloud_trail_logs
WHERE event_time > CURRENT_DATE - 30
AND event_name LIKE 'ConsoleLogin%'
GROUP BY 1;
```
Any practical advice on the initial evidence-gathering phase would be huge. How much of your engineering team's time was needed to get things connected and producing the right reports?
That first month with Secureframe is definitely a full-time job. For AWS integration, it does auto-discover a good chunk of your assets and services, which saves time. But the real time sink we found was mapping the control requirements to our specific internal processes - the tool gives you a framework, but you still have to build and document those processes yourself.
Your SQL example is the right track for thinking about data flows, but you'll likely need more than logs. Think about screenshot evidence for access reviews, HR onboarding/offboarding checklists, and vendor risk assessments. The reports in Secureframe are mostly pre-built, but you'll spend a lot of time gathering the actual evidence to upload or connect.
Biggest hidden sink for us was getting other teams (like engineering and HR) to consistently provide evidence on schedule. Start those conversations now, because chasing people for screenshots in month three is stressful. Good luck, it's a grind but doable in 90 days if you're focused.
> screenshot evidence for access reviews
Nothing screams "secure" like a JPG of a spreadsheet you updated last Tuesday. 😑
Look, Secureframe is fine if you've got the budget and a team that actually cares about deadlines. But if you're the only person with "data" in your title, automating evidence collection with a few bash scripts and Ansible will save you more time than any SaaS tool. Those auto-discovery integrations? They still miss half your shadow IT. And chasing HR for screenshots is a nightmare I've lived through.
You can script access reviews from your IdP logs. You can automate evidence collection for config changes, deployment logs, even that onboarding checklist. It's not sexy, but it beats begging people for screenshots in month three.
What's your actual tech stack? If you're mostly AWS, I'd rather spend two weeks building a pipeline that dumps evidence into a shared drive than pay Secureframe's per-seat price.
Deploy with love
You're right to dig into the specifics of mapping assets and data flows. While Secureframe's AWS integration will enumerate services and configs, it often can't automatically infer the *business context*. That's the manual part you can't avoid.
To your question about SQL for reports, it's a mix. You will absolutely write custom queries, but the configuration overhead comes from defining what "normal" looks like for your specific environment so the tool can flag anomalies. Your data flow sketch is the correct starting point, but consider building it as a lineage model in something like dbt or a simple graph database, as auditors frequently ask for traceability from ingress to storage to egress.
My caveat on a three-month timeline for Type 2: the tool handles the framework, but you still must design, implement, and *operationally follow* the actual processes for a full audit period. That's the real constraint. You might be "ready" for a Type 1 on schedule, but a Type 2 requires demonstrated operational effectiveness over time, which is fundamentally at odds with a 90-day total lead time. Have you clarified if leadership expects the audit to *begin* in 90 days, or to have the report in hand?
Migrate slow, validate fast.
Yeah, that SQL sketch is the right instinct, but the reality is you'll be manually stitching data sources together. Secureframe's AWS integration will list your S3 buckets and IAM roles, but it won't automatically draw the arrows between them for your data flow diagram. You're still the one who has to say "this Lambda reads from this bucket and writes to this RDS instance, and here's the business reason."
For reports, it's less about writing raw SQL and more about configuring the connectors to pull the *right* logs from CloudTrail, your IdP, etc. Then you're building "tests" in their system to confirm those logs meet the control requirement. The config overhead is real - defining what a "failed login" looks like across different services took us a solid week.
My biggest month-one time sink? Chasing engineers to confirm that the auto-discovered assets were actually in use and not just legacy junk. That "asset inventory" step is 30% automated, 70% human coordination.
pipeline all the things