Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
OK, so here's something that drives me crazy: every AI platform claims to be "secure and private" but nobody actually explains what that means in practice.
Last month, I was evaluating AI automation platforms for multiple client projects. You know what I found? Marketing pages full of security badges and compliance certifications, but zero transparency about how these systems actually handle your business data.
The problem isn't just with AI tools - it's that most businesses don't know the right questions to ask. They see "SOC 2 compliant" and think they're covered. Meanwhile, their customer data is being processed in ways they never agreed to.
I spent 6 months diving deep into AI security practices - not just reading marketing materials, but actually testing platforms, reading terms of service, and talking to security teams. What I discovered changed how I evaluate any AI platform for business use.
Here's what you'll learn from my research:
Why security certifications don't tell the whole story
The specific questions that reveal real data handling practices
How to audit AI platforms beyond their marketing claims
Red flags that indicate poor data governance
A practical framework for AI security evaluation
Industry Reality
What every compliance officer will tell you
The AI security industry has created a perfect storm of confusion. Every platform throws around the same buzzwords: "enterprise-grade security," "zero-trust architecture," "end-to-end encryption."
Here's what the industry typically recommends when evaluating AI platforms:
Check for SOC 2 Type II compliance - The gold standard certification
Verify GDPR compliance - Essential for European customers
Look for encryption in transit and at rest - Basic security hygiene
Review data retention policies - Know how long data is stored
Ensure proper access controls - Who can see your data
This conventional wisdom exists because these are real security requirements. SOC 2 compliance means a third party has audited their controls. GDPR compliance means they understand data rights. Encryption protects data in motion and storage.
But here's where this falls short: these certifications tell you about their infrastructure, not their business model. A platform can be SOC 2 compliant while still using your data to train their models. They can be GDPR compliant while sharing anonymized data with third parties.
The real question isn't "Are they secure?" - it's "How do they make money from your data?"
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Here's the thing that sparked my deep dive into AI security: I almost recommended a platform that was secretly training on client data.
I was working with a SaaS startup that needed to automate their customer support workflows. They had sensitive customer conversations, financial data, and proprietary business information flowing through their support system.
The AI platform we were evaluating had every security badge imaginable. SOC 2? Check. GDPR compliant? Check. Enterprise security page with impressive technical diagrams? Double check.
But something felt off during the demo. When I asked about model training, the sales rep gave a vague answer about "continuous improvement." When I pressed for specifics about data usage, they pointed me to the terms of service - always a red flag.
So I did something most people don't: I actually read the entire terms of service and privacy policy. Buried in section 47.3.2 was a clause that essentially said: "We may use customer data to improve our services and train our models."
The platform was SOC 2 compliant, GDPR compliant, and completely transparent about using customer data for training. They just didn't advertise it on their security page.
That's when I realized: the problem isn't that AI platforms are lying about security - it's that we're asking the wrong questions.
Here's my playbook
What I ended up doing and the results.
After that wake-up call, I developed what I call the "Data Reality Audit" - a framework that goes beyond marketing claims to understand how AI platforms actually handle your data.
Step 1: Follow the Data Flow
Most security audits focus on perimeter security - who can access your data, how it's encrypted, where it's stored. That's important, but it misses the bigger picture: what happens to your data once it enters their system?
I started mapping complete data flows for every AI platform I evaluated. Not just "data goes in, results come out" - but the entire journey. Where is data processed? What servers does it touch? How many times is it copied? Who has access at each stage?
For Lindy.ai specifically, I discovered they use a multi-layered approach: data processing happens in isolated environments, with different security zones for different types of information. But the key insight was understanding their business model - they make money from subscriptions, not from data licensing.
Step 2: The Business Model Test
This is the question that cuts through all the marketing noise: "If I stopped paying you tomorrow, what happens to my data, and how does that affect your revenue?"
The answer reveals everything. If they hesitate or give vague responses about "data retention policies," that's a red flag. If they immediately explain deletion procedures and seem uninterested in keeping your data, that's a good sign.
Step 3: The Granular Permission Audit
I developed a specific set of questions that reveal real data handling practices:
Can you show me exactly which team members can access customer data?
What triggers automatic data access logging?
How do you handle data in error logs and debugging?
What happens to cached data after processing?
Do you use customer data for any form of model improvement?
Step 4: The Technical Deep Dive
Beyond the marketing materials, I request technical documentation about their architecture. Not to understand every detail, but to see if they can explain their security model in specific terms rather than buzzwords.
The best platforms can draw you a diagram of exactly how your data flows through their system, show you their encryption keys management, and explain their zero-trust implementation in plain English.
Technical Verification
Always ask for architectural diagrams and specific implementation details rather than trusting marketing security pages
Business Model Alignment
Platforms that make money from subscriptions rather than data licensing have fundamentally different privacy incentives
Operational Transparency
The best security indicator is when platforms proactively explain their limitations and edge cases rather than claiming perfect security
Granular Controls
Look for platforms that offer data residency options and can demonstrate real-time data deletion rather than just policy promises
What I discovered through this process was eye-opening: the platforms with the most security badges weren't necessarily the most secure.
The framework revealed that smaller, focused AI platforms often had better actual data practices than enterprise giants. Why? Because their business model aligned with privacy - they made money from solving your problem, not from harvesting your data.
For the SaaS startup I was helping, we ended up choosing a platform that had fewer certifications but could demonstrate complete data isolation and offered real-time deletion capabilities. Six months later, they've processed over 100,000 customer interactions without a single privacy incident.
The most surprising result? Platforms that were transparent about their limitations actually performed better than those claiming perfect security. When a platform says "we can't guarantee 100% security but here's exactly what we do to minimize risk," that's infinitely more trustworthy than "enterprise-grade security with military-level encryption."
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons that completely changed how I evaluate AI platform security:
Business model beats certifications every time. A platform that makes money from your success has different incentives than one that makes money from your data.
Transparency about limitations is more valuable than perfect marketing. Platforms that admit their constraints are usually more honest about everything else.
Data flow matters more than data storage. How your data moves through their system reveals more about security than where it sits.
Technical documentation quality correlates with actual security. If they can't explain their architecture clearly, they probably don't understand it fully.
Real-time deletion capabilities separate serious platforms from pretenders. Anyone can promise to delete data "eventually" - few can do it on demand.
The best security question is always "How do you make money?" Everything else flows from their revenue model.
Small, focused platforms often outperform enterprise giants on actual privacy practices. Size doesn't equal security.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies evaluating AI platforms:
Focus on platforms where your data improves your results, not their models
Prioritize real-time deletion and data portability features
Test data isolation by running multiple customer segments through the platform
For your Ecommerce store
For Ecommerce stores implementing AI tools:
Ensure customer PII is processed in isolated environments
Verify that purchase data isn't used for competitor analysis
Look for platforms offering on-premise or private cloud deployment options