The greatest threat to enterprise AI is coming from inside the organization. Instead of a brute-force attack, the new risk is a subtle manipulation of AI logic that can silently misdirect entire organizations. Called "prompt injection," the threat is akin to cinematic espionage. Here, a single, malicious idea slowly poisons the system from within.

As more companies deploy large language models (LLMs), security experts worry that legacy infrastructure is quickly becoming obsolete. Built to protect a perimeter that has since disappeared with the widespread adoption of AI, old fortresses of firewalls and signature detection are crumbling. The vulnerability is a simple logic failure: models cannot distinguish between trustworthy instructions and malicious ones. Now, AI itself is becoming the battlefield.

To understand this new reality, we spoke with two experts at the intersection of technical defense and business strategy. Bishoy Habib is the Global Pre-Sales Manager at ZINAD IT and a leading AI & Cybersecurity Expert with a track record at firms like Microsoft. Joining him is Mohamed Zayed, a Presales Director at ZINAD IT with over two decades of experience driving GTM strategy at firms including Mondia Group and Ericsson. Together, they paint a picture of enterprise security in desperate need of reevaluation.

• AI inception: "When you integrate AI, you're not just adding a tool. You're injecting a brain into your organization. The threat is like something out of the movie Inception. It's not about breaking down the door, but about planting a corrupting idea in that brain and letting it take hold. The destruction comes later, from the inside out."

Zayed's analogy is chillingly accurate. Unlike a traditional breach that shuts down systems, a compromised LLM continues to run. "The real danger isn't that an AI attack will take down your systems. It's that it will mislead them without you ever knowing it happened. A compromised AI won't crash your network. But it will silently corrupt your data, generate vulnerable code, or leak your most sensitive business strategies. That invisibility is the scariest part."

To defend against an Inception-style attack, leaders can’t rely on old defenses, Habib and Zayed say. Instead, a multi-layered playbook can help enterprises manage the AI "brain." But first, they'll need a new set of security and governance principles.

• Govern the inputs: The first line of defense is a "sanitization layer" to filter prompts for potential manipulation before they reach the model. Here, Habib recommends monitoring behavior, not logs. "We have to move past the old way of thinking. Security is no longer about monitoring logs for signs of a break-in. With AI, it’s about monitoring the behavior of the model itself. You're not looking for a footprint. You're looking for a subtle change in personality or reasoning that tells you something is wrong on the inside."

• Create narrowed personas: Next, the principle of least privilege must be applied to the AI itself, Habib adds. "You have to treat every AI model on a strict 'need-to-know' basis. We do this by creating narrowly defined 'personas' for each one, limiting their function to only the job they need to do. If you give a single AI engine access to all your data, it only takes one successful prompt injection to have all of that data leaked."

• Implement Zero Trust: Governing AI with Zero Trust principles is another prerequisite, Zayed explains. Ensuring the model only performs actions that users explicitly permit is essential. But most importantly, the system must never run all on its own. "The AI environment must be governed by Zero Trust principles, ensuring the model only does what you explicitly permit and is not manipulated by unverified instructions. You cannot leave the ecosystem to work on autopilot."

The new defensive posture spotlights the most common entry point for these attacks: employees. Now, organizations need a new kind of "human firewall" for the AI era, built on dedicated training and awareness.

• AI awareness programs: Advocating for new literacy initiatives, Zayed explains that the goal of these programs is to redefine what employees consider "sensitive data." Some companies are already taking action, he notes. "This is just the next evolution of the 'human firewall.' Like we train employees to spot phishing emails, we now need dedicated AI awareness programs to educate them on the new risks. This is already happening in practice. We're seeing organizations issue formal directives to employees, telling them to stop using public, open-source AI models for any business operations to prevent accidental data leaks."

• The golden rule of prompting: It's simple, according to Habib. "Don't tell an AI anything it doesn't absolutely need to know to do its job. I saw a case where a company gave its customer service bot access to R&D data. A customer then cleverly questioned the bot, tricking it into revealing confidential details about unreleased products. The employee who gave the AI that data never imagined it could be a risk, but that one mistake led to a major data leak."

"Securing AI is not about fear, it's about foresight," Habib and Zayed conclude. All that's left is for leaders to decide whether to develop the complex security controls in-house or partner with third-party specialists to expedite deployment. Regardless of the chosen path, the goal remains the same: total visibility. Now, the single greatest challenge of the AI era will be to transform these powerful systems into tools we can use, monitor, and understand. "The ultimate goal is transparency. You have to turn the black box of AI into a glass room, where you can see exactly what’s happening inside the engine at all times. That's the only way to be in control."