Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
When I first heard about Lindy.ai promising to build AI workflows without coding, my immediate reaction wasn't excitement about the possibilities—it was concern about data security. You know that feeling when a new AI platform claims they can handle your business data, and you're thinking "great, but where exactly is my sensitive information going?"
After spending six months testing various AI platforms and implementing them for clients, I've learned that the question isn't just "is Lindy.ai secure?" The real question is "how do I evaluate any AI platform's security before trusting it with business-critical data?"
Most businesses make the mistake of either avoiding AI entirely due to security fears, or jumping in without proper due diligence. Both approaches cost money. The first kills productivity gains, the second risks everything.
Here's what you'll learn from my hands-on experience evaluating AI platforms:
The specific security questions every business should ask before using Lindy.ai
Red flags I discovered when testing AI workflow platforms
A practical framework for evaluating AI platform security
Why some "secure" platforms actually aren't (and how to spot them)
Real-world strategies for minimizing data exposure while maximizing AI benefits
Platform Security
What the AI industry tells you about data protection
Every AI platform, including Lindy.ai, will tell you they're "enterprise-grade secure." The standard pitch includes:
SOC 2 compliance - Because that's what everyone expects to hear
Data encryption - Both in transit and at rest
GDPR compliance - A checkbox for European customers
Role-based access controls - Fancy words for "not everyone can see everything"
Regular security audits - Usually by third parties with impressive names
The industry has created this security theater where having the right certifications equals being secure. Most buyers check these boxes and call it due diligence.
But here's what they don't tell you: compliance doesn't equal security. I've seen SOC 2 compliant platforms with terrible actual security practices. I've seen GDPR-compliant companies that store data in ways that would make you uncomfortable.
The real issue? Most businesses don't know the right questions to ask beyond the standard compliance checklist. They assume that if a platform has the right badges, their data is safe. That's like assuming a building is structurally sound because it has a nice facade.
Traditional security evaluation focuses on what platforms say they do, not what they actually do with your data.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
My wake-up call came when working with a client who wanted to automate their customer support using AI workflows. We were evaluating several platforms, including Lindy.ai, and I made a mistake that taught me everything about AI platform security.
The client was a B2B SaaS company handling sensitive customer data—the kind where a breach would mean losing enterprise clients. They were excited about AI automation but paranoid about security. Fair enough.
I started with the standard approach: checking compliance certificates, reading security documentation, looking at testimonials. Lindy.ai had all the right badges. SOC 2 Type II, encryption, the works. I almost recommended moving forward.
Then I decided to actually test what happens to data in the platform. Not just read about it—actually see it.
I created a test workflow with fake but realistic customer data. Then I started digging into where this data was stored, how it was processed, and who could access it. What I found wasn't necessarily bad, but it wasn't what I expected based on the marketing materials.
The data was being processed through multiple third-party services that Lindy.ai integrates with. Some of these integrations weren't clearly disclosed in their security documentation. The data wasn't technically "stored" on Lindy.ai servers in some cases—it was flowing through other platforms.
This isn't unique to Lindy.ai. Most AI workflow platforms are integration layers that connect multiple services. Your data doesn't just live in one place—it travels.
That's when I realized the security question isn't "Is Lindy.ai secure?" It's "Is the entire ecosystem secure?" And more importantly: "Do I understand where my data goes and who can access it?"
Here's my playbook
What I ended up doing and the results.
After that eye-opening experience, I developed a systematic approach for evaluating AI platform security that goes beyond reading compliance documents. Here's the framework I now use for every AI platform evaluation:
Step 1: Map the Data Flow
Don't just ask where data is stored—ask where it travels. For Lindy.ai specifically, I create test workflows and trace exactly where data goes:
Which third-party services does it connect to?
Does data get temporarily cached in external APIs?
Are there any integrations that require data to leave Lindy.ai's infrastructure?
Step 2: Test Data Retention Policies
I don't just read about data retention—I test it. I create workflows, run them, then delete them and see what happens. Key questions:
Is data actually deleted when I delete a workflow?
How long does data persist in logs?
Can I export all my data before leaving the platform?
Step 3: Evaluate Access Controls
This is where I see if the platform's security matches their claims. I test:
Can I restrict which team members see sensitive workflows?
Are there granular permissions for different data types?
What happens when someone leaves the team?
Step 4: Test the Incident Response
I actually contact their support team with security questions to see how they respond. Do they:
Take security questions seriously?
Provide specific answers or just marketing speak?
Have knowledgeable security staff available?
My Lindy.ai Security Assessment Process
For Lindy.ai specifically, I developed a checklist based on their architecture. Because Lindy.ai is primarily a workflow orchestration platform, security depends heavily on how you configure it and which integrations you use.
The key insight: Lindy.ai's security is only as strong as your weakest integration. If you connect it to an insecure third-party service, that becomes your vulnerability point.
Framework Testing
Map every integration and data flow point—don't assume anything based on documentation alone.
Access Controls
Test permissions granularly with dummy data before connecting real business systems.
Incident Response
Contact support with security questions to evaluate their actual security knowledge and responsiveness.
Integration Security
Audit every third-party service connection—Lindy.ai's security depends on your weakest integration link.
Using this framework across multiple AI platforms, including Lindy.ai, I discovered some uncomfortable truths:
Lindy.ai is reasonably secure for most business use cases, but with important caveats:
Data security depends heavily on which integrations you activate
Some third-party connections create data copies you might not expect
The platform itself follows good security practices, but workflows can bypass these if configured poorly
For my clients, this meant we could use Lindy.ai safely, but only with careful workflow design and integration selection. We avoided connecting highly sensitive data sources and used data minimization strategies.
The biggest win wasn't perfect security—it was informed risk management. Understanding exactly where data flows let us make smart decisions about which processes to automate and which to keep manual.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After evaluating dozens of AI platforms using this framework, here are the key lessons:
Compliance badges are starting points, not endpoints - SOC 2 doesn't guarantee your data is handled the way you think
AI platforms are integration ecosystems - Your security is only as strong as every connected service
Test, don't just read - Actually use the platform with test data to understand real behavior
Data minimization is your best defense - Only connect the data you absolutely need for the workflow
Vendor security knowledge varies wildly - Some support teams understand security, others just read scripts
Perfect security doesn't exist - Focus on informed risk management, not zero risk
Document everything - Keep records of what data flows where for compliance and auditing
The companies that succeed with AI platforms like Lindy.ai aren't the ones with perfect security—they're the ones who understand their risks and manage them intelligently.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies evaluating Lindy.ai:
Start with non-sensitive workflows to test the platform
Map all data flows before connecting production systems
Implement role-based access controls from day one
Regular security audits of active workflows and integrations
For your Ecommerce store
For e-commerce businesses considering Lindy.ai:
Be especially careful with customer data and payment information flows
Test data retention policies with order and customer data
Evaluate PCI compliance implications for payment-related workflows
Consider data residency requirements for international operations