I remember sitting in a pitch meeting a few years back. A sharp founding team, a slick deck, and a product that relied on a novel third-party AI API for its core feature. They were processing sensitive customer data through this API from a company I’d never heard of. I asked a simple question: "What happens if that API gets breached and leaks your customer data?"
They looked at me like I had three heads. "But it's a third-party API," one of them said. "Security is their problem, not ours."
I passed on the investment. That startup folded six months later after a major security incident traced back to that very API.
This isn't a rare story. In the rush to build AI-powered features, I see founders and engineers making the same mistake over and over: treating third-party APIs as infallible black boxes. We’ve become so accustomed to the ease of pip install and fetching a REST API that we’ve outsourced our security thinking along with our infrastructure. As someone who has built two companies (RemoteTeam, acquired by Gusto; MovieLaLa, acquired by Gfycat) and invested in over 200 more, including foundational players like OpenAI, Anthropic, and Scale AI, I can tell you this is one of the biggest unaddressed threats in the current tech ecosystem.
Your code is no longer the only perimeter you have to defend. Your supply chain is the new attack surface, and every third-party API you integrate is a potential backdoor into your systems and your customers' data.
The Illusion of the "Secure" Black Box
When we built MovieLaLa, we were API-first from day one. We pulled in data from a dozen different sources to power our recommendation engine. At RemoteTeam, we integrated with countless HR and payroll systems. Each one of those integrations was a calculated risk. We treated every API endpoint as a potential vulnerability, not a trusted partner.
Many developers today don't share that paranoia. They see a well-documented API from a venture-backed company and assume it's secure. They focus on the features, the usage-based pricing, and the ease of integration. They don't ask the hard questions:
- Data in Transit: Is my data encrypted with strong, modern TLS protocols? Are they protecting against man-in-the-middle attacks?
- Data at Rest: How is the provider storing the data I send them? Is it encrypted? Who has access to it? For how long is it retained?
- Data in Use: This is the tricky one. When the API processes my data, is it happening in a secure, isolated environment? Could a vulnerability in their model or infrastructure expose my data to other customers? This is a huge issue with many smaller, less mature AI service providers.
- Model Security: Could the model itself be attacked? Prompt injection, data poisoning, and model inversion attacks are not just theoretical. A sophisticated attacker could potentially extract sensitive information from your prompts or even poison the model to give you bad results.
I’ve seen the security postures of the best AI companies in the world. The teams at Anthropic and OpenAI are deeply, fundamentally concerned with these issues. But for every one of them, there are a hundred smaller players who are just wrapping a fine-tuned open-source model in a Flask app and calling it a business. Their security page is often just a checklist of buzzwords, not a reflection of a robust security culture.
My Due Diligence Framework for AI APIs
So how do you protect yourself? You can’t just stop using third-party services; that’s not practical. The key is to be intentional and paranoid. Here is the framework I use when evaluating a company for investment, which applies just as well when you're choosing a vendor.
1. The People & Paperwork Audit
Before you write a single line of code, you need to investigate the provider. This isn't a five-minute scan of their homepage.
- Read the Security Page: Don't just skim it. Read it carefully. Do they have SOC 2 Type II, ISO 27001, or other certifications? These aren't a guarantee of security, but they show a level of maturity and process.
- Scrutinize the Privacy Policy & DPA: This is non-negotiable. You need to read their Privacy Policy and their Data Processing Agreement (DPA). This is the contract that governs how they handle your data. Who owns the data you send? Who owns the outputs? What is their data retention policy? What are their breach notification obligations? If they don’t have a clear, comprehensive DPA, walk away. It’s a massive red flag.
- Ask Hard Questions: Get on a call with their sales or engineering team. Ask them directly about their security practices. "Can you walk me through your data encryption standards, both at rest and in transit?" "What is your employee access policy for customer data?" "Have you conducted third-party penetration tests?" Their answers—and their willingness to answer—will tell you everything you need to know.
2. The Technical Mitigation Playbook
Once a vendor passes the paperwork audit, the real work begins. You must operate on a zero-trust basis. Assume the API is hostile and build defenses accordingly.
The Gateway Pattern: Never, ever call a third-party API directly from your main application code. Route all outbound API calls through a centralized gateway or proxy that you control. We did this at RemoteTeam, and it was a lifesaver. A simple AWS Lambda function or Cloudflare Worker can act as this gateway. This gives you a single point of control to:
- Log every request and response.
- Manage API keys and credentials securely.
- Implement rate limiting and circuit breakers.
- Sanitize inputs and validate outputs.
Sanitize Everything: This is security 101, but it’s shocking how often it's missed in the context of AI APIs. Before you send any data to an API, sanitize it. Strip out any PII or sensitive information that isn't absolutely necessary for the API call. If you're sending user-generated content, be especially vigilant for anything that could be interpreted as a prompt injection attack.
Validate Everything: Just as you sanitize inputs, you must validate outputs. Don't trust the response from the API. Check that the data is in the format you expect. Look for any signs of errors or unexpected content. An API that starts returning malformed JSON or, worse, HTML with script tags, is a huge security risk.
Minimize Data Exposure: Follow the principle of least privilege. Only send the absolute minimum amount of data required for the API to do its job. If an AI summarization API only needs the text of an article, don't send the author's name, email, and user ID along with it.
A Final, Provocative Thought
Stop chasing the cheapest, hottest new AI API. The race to the bottom on usage-based pricing has created a market flooded with immature, insecure services. An extra cent per thousand tokens is a small price to pay for a vendor with a dedicated security team and a SOC 2 report.
Your company ’s reputation is built on trust. A single data breach, traced back to a sloppy third-party integration, can destroy that trust in an instant. It can tank your valuation, kill deals, and, in the worst cases, end your company.
I’ve seen it happen. Don’t be the next cautionary tale. Treat every API as a potential threat, do your homework, and build your defenses. That’s how you build a company that lasts.
Frequently Asked Questions
What's the most common pushback you get on this?
People often push back by citing exceptions or edge cases. And they're usually right that exceptions exist. But building a strategy around exceptions rather than patterns is a losing game for most founders.
How can I apply this thinking to my own situation?
Start by identifying the core principle behind the opinion, not the specific example. Then ask yourself: does this principle apply to my context? If yes, test it in a small, low-risk way before going all in.
Do all experts agree with this view?
No, and that's fine. The best ideas in business are often contrarian. I share my perspective based on my experience and data, but I encourage you to seek out opposing viewpoints and form your own conclusions.
How has this view evolved over time?
My thinking on most topics has changed significantly over the years. Early in my career, I held many conventional views that experience proved wrong. I try to update my beliefs when the evidence changes.