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. 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, 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 series, exploring how to secure the AI enterprise through a platform approach.
Up Next: Ian Swanson on the “Pilot Trap” and why legacy tools are keeping your AI stuck in the lab.
