What Is Generative AI Security? | How to Protect AI Models, Data & Prompts

As generative AI reshapes industries, it also introduces new attack surfaces.

In this video, we break down what Generative AI security really means — how it protects AI models, data, and outputs from threats like prompt injection, model poisoning, and shadow AI.

You’ll learn the five key steps to securing GenAI, why governance matters, and how frameworks like AI TRiSM and data lifecycle protection help organizations stay resilient. Whether you’re building with AI or managing cybersecurity risk, this video gives you the essential foundation for understanding and defending GenAI systems.

Stay ahead of AI threats. Secure your future with trusted GenAI security practices.

Key Details:
● Explains Generative AI Security fundamentals
● Covers real-world threats: prompt injection, data leakage, model poisoning, shadow AI
● Outlines five actionable steps for securing GenAI environments
● Introduces AI TRiSM, API security, and code security
● Educational and non-commercial tone designed for professionals, students, and teams exploring responsible AI use

Links:
● Learn more about AI Security: https://www.paloaltonetworks.com/cyberpedia/what-is-ai-security
● Explore Prisma Cloud AI Security: https://www.paloaltonetworks.com/prisma/cloud
● Visit the Cybersecurity Learning Hub: https://www.paloaltonetworks.com/resources/learning-center

0:00 What Is Generative AI Security?
0:19 Why Is Generative AI Security Important?
1:00 How Does GenAI Security Work?
1:32 Types of Generative AI Security
2:45 Top GenAI Threats and Risks
3:22 5 Steps to Secure Generative AI
4:12 Best Practices for AI Security
4:31 Final Takeaways

#GenerativeAI #AISecurity #Cybersecurity #AIProtection #DataSecurity #PaloAltoNetworks #responsibleai

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Transcript

What is Generative AI Security?

Generative AI security is about protecting the systems, data, and content created by AI technologies. It ensures AI operates safely and prevents threats like unauthorized access, data manipulation, and model exploitation.

Why is GenAI security important?

With AI adoption growing across industries, new risks like misinformation, data theft, and model misuse are rising. Gartner predicts that by 2027, 40% of AI-related breaches will stem from improper generative AI use.

How does GenAI security work?

It spans the AI lifecycle — from development to deployment — following a shared responsibility model between providers and users.

Different types of GenAI security:
LLM security, prompt security, AI TRiSM, data security, API security, and code security.

Top threats:
Prompt injection, data poisoning, shadow AI, insecure code, and AI supply chain risks.

5 key steps:
1. Harden I/O integrity
2. Protect the data lifecycle
3. Secure infrastructure
4. Enforce governance
5. Defend against adversarial threats

Best practices:
Risk assessments, shadow AI elimination, explainability, adversarial testing, continuous monitoring, and AI code audits.

Securing Generative AI isn’t optional — it’s essential for protecting systems, data, and trust.

Видео What Is Generative AI Security? | How to Protect AI Models, Data & Prompts канала Cyberpedia by Palo Alto Networks