When Openai It does not always respond: Business accidents after AI agents based on AI
“Wait … Do you send journal entries to Opelai?”
The first thing my friend asked when I show him FeltAi-Powered Alogy app that I have been exchanged during Hackathon in San Francisco.
I was frustrated.
“It was a_the Themed Hackathon, had to create something immediately.”
He misses the beat:
“Certainly. But how I hope you have built? Why don't you get your llm?”
That stopped cold.
I proud that the app was how quickly the app was asked. But that one question, as well as those who followed all I thought I knew about building hope about Ai. The judges of Hackathon also sparked him.
That moment has made me see how to trust each other when we build a Ai, especially with tools that treat sensitive data.
I saw something great:
We do not adequately speak in relation when we build with AI.
His answer was sticking with me. Georgia Von Minen is a Data Scientist in ACU, where they work closely with problems around the details of the Legal and community conditions. I've always appreciated her understanding, but this conversation is a different hit.
So I asked him to be very clear What does trust in this context mean? Especially when AI systems are holding personal data.
He told me:
“Trust can be difficult to throw down, but data rulership is the first place.
When it comes to information about personally, the principles and the common sense points to the need for strong data rulers. Sending a PII to API Calls is not only dangerous – it can also break those laws and produce people to harm. “
It reminded me that when we built a AI, especially programs that affect the sensitive data, we are not just writing code.
We make decisions confidently, energy, and trust.
You currently collect user data, especially personal existing such as textbooks, log in to a load space. Not only what your model can do. It's about what happens to that data, where it goes, and whoever has access to it.
Fraudulent
Today, it is easier than before printing something that looks smart. With Opena or other llms, enhancements can create AI tools in hours. The Startups can start to open the “Power An Entrant” All the night. And businesses? They are quick to combine these agents on their travel.
But in all the joy, one thing is often negligible: trust.
When people talk about AI agents ai, they often refer to the seller of LLMS. These agents can answer the questions, not stream of jobs, or make decisions. But many are immediately built, with the small thought-given to safety, compliance or accountability.
Just because the product using Openai does not mean that you are safe. That really trusts the whole pipe:
- Who creates a wrapper?
- How is your data added?
- Is your information stored, logged in – or worse, mature?
I've been using Opelai API for customer use myself. Recently, I was offered free access to api – up to 1 million daily tokens until the end of April – If I agree to share my fastest data.
(Photo by writer)
I nearly entered the personal project, but then hit me: If the solutions provider welcomes the agreement to cut costs, their users will not see that their information has been stolen. At the personal level, which can seem dangerous. But in the case of business? That serious violation of privacy, and perhaps the contract or control bonds.
All you need is one engineer that says “Yes” in coping like such, and your customer data is in someone else's hands.
(Photo by writer)
Enterprise AI suggests poles
I see many Saas companies and the DevTool Quartups Construp Templen Agents AI. Some find it well. Some AI workers allow them to bring their own llm, and give them control when the model is also working and how the data can be handled.
That's how to think: Describes reliable bounds.
But not everyone is so careful.
Many companies connect to the OPE of Openai, add a few buttons, and call the “businesses ready.”
Spoerer: Not.
What can go wrong? More.
If you include AI agents in your cell without asking difficult questions, here is a cruel:
- Data leak: Your encouragement includes sensitive customer data, API buttons, or internal logic – and if that is sent to the third party model, it can be disclosed.
In 2023, Samsung engineers gathered the internal source code and notes to ChatGPT (Forbes). That data now may be part of future training sets – a major risk of ownership property.
- Breaking of Compliance: Shipping Personal Information (PII) with a model such as Opelai without proper control may break GDPR, HIPAA, or your contracts.
Elon Musk Company Nx felt that hard. Silent their “Grok” Ai Chatbot through all user posts including the EU users to train you, without login. Reululers entered immediately. Under pressure, they set up Grok in the EU (Politico training).
- Opaque behavior: Unreading agents are difficult to abuse or explain. What happens when the client asks why Chatbot has given the wrong recommendation or express something confidential? You need to be clear to answer that – and many agents today do not give you.
- The confusion of data ownership: Who is the output? Who lists the information? Does your provider again receive your input?
Zooming to Make that right in 2023. They have changed their service policies to allow customer meeting data to be used for AI (Instant Company) training. After the public backlash, they reinstated the policy, but a reminder of the fact that trust could be lost all night.
- Security Safety in Wrappers: In 2024, Hleze – a famous Lllim Orchestistration tool – found that the number of online items, many without authentication (cyberercere mating). Investigators receive API buttons, Database credentials, and user details are formulated. That is not an open problem – that builder The problem. But the last users currently pay the amount.
- AI features are very far awayMicrosoft 'Microsoft “- Part of their Copilot Rollitot – took automatic screenshots for users to help the Ai Assistant Reply (Doublepulsar Questions. It sounded to help you … until the safety experts of safety as a secret night. Microsoft should have used back quickly and acted only in the entry.
Not all the item requires Openai
Opena has great power. But not always the right answer.
Sometimes there is little, the local model is more than enough. Sometimes a logic based on the restoration of work is better. And often, the safest option is the most effective for your infrastructure, under your rules.
We should not link to the llm and put the “wise helper.”
To businesses, trust, clarity, and control is not selected – They are important.
There is a growing number of platforms that enable this type of control. Salesforce's Einstein 1 Studio Now supports bring your modelallowing you to connect your llm from AWS or Azure. IBM's Watson allows businesses to send models within complete audit routes. Databricks, with the Mosaicml, allows you to train llms in your cloud, so your sensitive data has not left your infrastructure.
That's the Real Enterprise AI to look like.
A bit of bitterness
Agents Ai are strong. They open a work flow and the defaults we cannot do before. But Easily Development does not mean safe, especially when handling sensitive data on a scale.
Before you get rid of that shiny new agent, ask yourself:
- Who is controlling the model?
- Where is the data traveling?
- Do we obey?
- Can we check what they do?
In Ai age, the greatest risk is not a bad technology.
Honestly reliable.
About the writer
I am Ellen, a 6-year-old experience engineer, currently working at the beginning of the FTTETCH IN SAN Francisco. My background is destroying data data science in oil and a consultation gas, and AI and the leading systems in the Apac, in the Middle East, and Europe.
I am currently filling my Lord in Data Science (degree in May 2025) and I'm actively looking for my next chance as a mechanical engineer. If you are open to reference or connect, I really appreciated!
I love to create a real impact on the world with AI and I am always open to project-based collaboration.
View my portfolio: Liviaallen.com/portfolio
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