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* Why Traditional Security Fails in the Age of Non-Deterministic AI

# Why Traditional Security Fails in the Age of Non-Deterministic AI

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**By [Anand Oswal](https://origin-www.paloaltonetworks.com.au/perspectives/author/anand-oswal/ "Posts by Anand Oswal")** | **3 min read** |  
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  ![](https://origin-www.paloaltonetworks.com.au/perspectives/wp-content/uploads/2025/01/anand-oswal.jpg)  
  Anand Oswal is the Executive Vice President of Network Security at cybersecurity leader Palo Alto Networks. His team of product managers, engineers and researchers deliver best-in-class enterprise security products and services to help to protect users, applications and infrastructure from cybersecurity threats. An innovation-driven technology leader who holds over 100 U.S. patents, Anand leads the AI transformation of the Network Security business. His team has developed a range of advanced security capabilities including: Prisma AIRS, a comprehensive AI security platform; Strata, an AI-powered platform; Cloud Delivered Security Services (CDSS); Secure Access Service Edge (SASE); Next Generation Firewalls (NGFW); and Zero Trust solutions. As a dynamic leader, Anand is dedicated to building strong, diverse and motivated teams focused on creating innovative security products and solutions for some of the world's biggest enterprise customers. Before joining Palo Alto Networks, Anand was SVP of Engineering for Cisco's Intent-Based Networking Group. At Cisco he was responsible for building the complete set of platforms and solutions for the Cisco enterprise networking portfolio. The portfolio spans enterprise products across routing, access switching, IoT connectivity, wireless, and network and cloud services deployed for customers worldwide. He joined Cisco through the acquisition of Starent Networks, and earlier in his career, held leadership roles at Siara Systems, Sun Microsystems and Ericsson. Anand holds a bachelor's degree in telecommunications from the College of Engineering, Pune, India and a master's degree in computer networking from the University of Southern California....

[Learn more](https://origin-www.paloaltonetworks.com.au/perspectives/author/anand-oswal/)

## IN THIS ARTICLE

Every enterprise today is in a race. We are racing to put AI to work --- from copilots to autonomous agents --- because the potential for productivity and innovation is extraordinary. But as we accelerate, we are discovering that the road itself has changed.

For decades, cybersecurity was built on a "deterministic" model. We knew how applications behaved: Input A led to Output B. If an application did something unexpected, it was a bug or a breach. The threat surface, while vast, was relatively static.

The page has officially turned on that era.

## **The Rise of the Non-Deterministic Threat**

AI introduces a fundamental shift in how applications behave, reason, and use data. AI models are non-deterministic; they are adaptive, creative, and constantly evolving. This means the threat surface is no longer static. It expands and changes in ways that traditional, rule-based security tools simply cannot match.

Without a unified strategy, every new AI interaction, whether through an app, model, or agent, multiplies your blind spots. It makes it increasingly difficult for leaders to answer the most basic, yet critical question: *Is this safe to deploy?*

## **The "Point Tool" Trap**

In the rush to secure these new systems, many organizations are falling into a familiar trap: adding more point tools. They see a new risk --- prompt injection, model theft, data leakage --- and they buy a specific tool to fix it.

But AI is ubiquitous. And so, securing AI is not about patching holes. Reacting to every new risk with a dedicated solution creates a fragmented security posture. It leaves you stitching together disconnected alerts while adversaries move across the gaps. In the Age of AI, complexity is the enemy of speed.

## **A Unified Approach for a Unified Ecosystem**

To move fast, we must stop treating AI security as an add-on and start treating it as an architectural requirement. It requires unifying how the entire AI ecosystem is protected --- from the models developers build to the agents and chatbot interfaces employees use every day.

Organizations must pivot to a platform approach that allows them to:

* **Discover** what AI is actually running in the environment (shadow AI is often 10x what IT suspects).
* **Assess** the risks holistically on your AI ecosystem including sensitive data exposure.
* **Protect** your AI deployment in real-time, end-to-end.

This is the vision behind [**Prisma AIRS**](https://www.paloaltonetworks.com/prisma/prisma-ai-runtime-security). We built it to give enterprises a single, comprehensive platform to secure AI at scale. Instead of managing disjointed tools, Prisma AIRS brings AI activity into a unified security and governance framework.

## **Deploying Bravely**

As I discuss in my [video](https://www.paloaltonetworks.com/deploybravely#executives), the goal isn't just to "lock down" AI since that may well be impossible to do. It is to turn security into an accelerator.

When you have a unified defense, you don't have to choose between security and speed. You can move boldly, knowing your AI is protected from development to deployment. Speed with safety fuels secure innovation.

*This post is Part 1 of our* ***[Deploy Bravely](https://www.paloaltonetworks.com/deploybravely#executives)*** *series, exploring how to secure the AI enterprise through a platform approach.*

***Up Next:*** *Ian Swanson on the* ***["Pilot Trap"](https://paloaltonetworks.com/perspectives/the-pilot-trap-why-scaling-ai-is-impossible-with-legacy-appsec-tools/)*** *and why legacy tools are keeping your AI stuck in the lab.*

* [AI](https://origin-www.paloaltonetworks.com.au/perspectives/all-articles/?cat=ai)
* [Secure AI Usage](https://origin-www.paloaltonetworks.com.au/perspectives/all-articles/?cat=secure-ai-usage)

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