Best Practices in AI Security & Risk

---

---

Business owners are anxious about AI, and they should be. AI can genuinely cause problems, and being cautious about a technology you don't fully understand is the right instinct, not a weakness. The issue isn't that owners are afraid. The issue is that the fear is too vague to act on.

---

When someone asks "is AI secure?", that's not actually a security question, it's a feeling. Behind that feeling are five or six very different concerns, each with different consequences, different likelihoods, and different fixes. Are you worried about a competitor seeing your customer list? About OpenAI training their next model on your contracts? About an employee accidentally deleting a database with a careless prompt? About a denial-of-service attack on a system you depend on? Those are entirely separate problems.

---

Knowledge is the cure to anxiety. Once a vague worry becomes a specific named risk, it stops being paralyzing and starts being a problem you can actually decide about. Adopt the tool with guardrails. Avoid it entirely. Talk to your vendor. Write a policy. The decisions only become available once the risk has a shape.

---

---

---

What We'll Cover:

---

• A clear walkthrough of the actual AI security risks a business owner can encounter, broken down by category so you can match your specific worry to the specific concern. For each one: what it really is, what the realistic consequence looks like, and what to do about it.

---

• Data leakage. What happens to the customer list, contract, or financial sheet when an employee pastes it into a free chatbot. Who sees it, where it lives, who gets sued.

---

• Training contamination. The question owners ask most: "is OpenAI training their model on my conversations?" The honest answer, the free-vs-paid tier difference almost no one knows about, and what it means for proprietary information.

---

• Accidental destruction. When AI agents are given access to live systems and a bad prompt deletes a row, a file, or a database. Why this is becoming more common, not less, and what "human-in-the-loop" actually means in practice.

---

• Account compromise and credential exposure. AI tools as a new attack surface. Phishing that targets AI logins, prompt injection in shared documents, and the access permissions most people grant without reading.

---

• Availability and dependency. DDoS, outages, and what happens when the AI tool your business now relies on goes down for six hours. The risk of building critical workflows on a single vendor.

---

• Compliance and regulatory exposure. What changes when you handle HIPAA, financial, or legal data, and the specific places AI tools create disclosure problems most owners don't realize they're triggering. _

---

---

Each category is named, explained without jargon, and matched to a practical mitigation. The goal is for every attendee to leave able to say: "Here's the specific thing I was worried about, and here's what I'm going to do about it."

---